I received my PhD in Computer Science from Duke University in 1997. In August 2014, following a faculty appointment at Montana State University Department of Computer Science, and nearly 15 years as CEO of a bioinformatics software company, Golden Helix, I became a faculty member in the University of New Mexico Center for Global Health, Division of Translational Informatics, and Department of Internal Medicine.
My research areas include clinical research informatics, bioinformatics, cheminformatics, and systems thinking. I develop and apply methods for the analysis of longitudinal healthcare data for predictive and preventative medicine. Since its inception, I have collaborated with other members of the Observational Health Data Sciences and Informatics collaborative. The OHDSI/OMOP common data model has been adopted to represent over 10% of the global population of patients' electronic health and/or administrative claims records worldwide, enabling the development of a broad set of tools for the analysis of human health on these massive datasets. I am currently developing statistical and computational tools to compare treatment options and obtain better estimates of expected health outcomes despite large biases and confounding in the data, with a focus on mental illness (bipolar disorder, major depression, PTSD, suicidality).
In 2016, received an NIH NLM R21 award to research methods for observational comparative effectiveness research, and a PCORI award to compare bipolar disorder treatments and outcomes in large-scale administrative claims data. In 2020, I received an R56 award from the NIH NIMH, followed in 2022 with an NIH NIMH R01 to investigate undiagnosed and/or unrecorded PTSD, TBI, and self-harm through machine learning to determine the degree to which this phenomenon exists, and to examine differences in diagnosis/recording/outcomes among patient subgroups. At the interface of bioinformatics and cheminformatics, I have co-led with Dr. Jeremy Yang the NIH-funded Common Fund Data System (CFDE) Illuminating the Druggable Genome Data Coordinating Center since 2022.
I serve as the UNM Southwest Center for Advancing Clinical & Translational Innovation (SW CACTI) Informatics Core Lead. I hold secondary appointments in the UNM Department of Psychiatry and Behavioral Sciences and the UNM Department of Computer Science.
I inform all of my efforts through a palette of multiple systems disciplines including Theory of Constraints, System Dynamics, Requisite Organization, TRIZ, Cybernetics, and the Scientific Method.
Research Identifiers
ORCID iD:
https://orcid.org/0000-0003-1994-2893
Funding (9)
Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD ✓ NIH
2022-12-23 to 2026-11-30 | Grant
National Institute of Mental Health (Bethesda, US)
Homepage URL: https://app.dimensions.ai/details/grant/grant.13057758
GRANT_NUMBER: R01MH129764
Show more detailOrganization identifiers
National Institute of Mental Health: Bethesda, US
Funding project translated title
Funding project translated title
(en)
Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD
📄 Project Abstract (from NIH)
PROJECT SUMMARY
Post-traumatic stress disorder (PTSD) often has complex profiles of co-occurring medical conditions and is
associated with high risk of self-harm, including suicidality, which is a leading cause of death, particularly
among Veterans. There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased
use of off-label medications and polypharmacy (multiple drugs used simultaneously) with limited evidence on
their relative risks and benefits. Moreover, PTSD and suicidal and nonsuicidal self-harm often remain
undocumented in electronic health records (EHR). There is also poor predictability of disease outcomes since
there are frequent changes in pharmacological treatment and multiple modifying co-occurring conditions
including depression, bipolar disorder, schizophrenia, substance use disorders, traumatic brain injury, and
sleep disorders. Our long-term goal is to improve diagnostics, secondary/tertiary prevention, and treatment
outcomes of PTSD and its co-occurring conditions via enhanced EHR utilization. To achieve our objectives, we
will analyze EHR and administrative claims data from Veterans Health Administration (VHA) and non-VHA
databases, collectively covering >1.8M patients with PTSD. Specifically, we aim to: (1) Identify undetected and
uncoded co-occurring mental health phenotypes that impact PTSD outcomes using machine learning and
characterize disparities in their documentation; (2) Create robust models, accounting for biases and
co-occurring conditions, to identify clinical trajectories of PTSD decompensation/recovery in response to
time-varying treatments; and (3) Compare risk of self-harm and hospitalization among PTSD treatments using
coded and imputed phenotypes through an international network study. We will compare the effectiveness of
PTSD psychotropic monotherapies, polypharmacy, and psychotherapy to guide the choice of treatment for
improved patient outcomes. By enhancing and validating a positive-unlabeled machine learning approach
developed by our team, we will impute unrecorded/undetected mental health conditions co-occurring with
PTSD in both VHA and non-VHA databases, and characterize factors associated with documentation
disparities. We will model disease trajectories with enhanced latent class / latent trajectory analysis, focusing
on self-harm, substance use disorders, and psychiatric hospitalization in PTSD. Finally, we will perform the
largest comparative effectiveness studies to date of PTSD treatments on >100 monotherapy and
polypharmacy regimens, in addition to psychotherapy interventions, using causal models and methods for
addressing biases. These studies will provide high-quality evidence on the risk of hospitalizations and suicidal
acts/self-harm. Successful completion of these investigations will improve the quality of clinical psychiatric
decision-making, and guide improved service delivery to the Veteran and non-Veteran populations with
PTSD/TBI, and/or high risk of self-harm/suicidality.
Post-traumatic stress disorder (PTSD) often has complex profiles of co-occurring medical conditions and is
associated with high risk of self-harm, including suicidality, which is a leading cause of death, particularly
among Veterans. There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased
use of off-label medications and polypharmacy (multiple drugs used simultaneously) with limited evidence on
their relative risks and benefits. Moreover, PTSD and suicidal and nonsuicidal self-harm often remain
undocumented in electronic health records (EHR). There is also poor predictability of disease outcomes since
there are frequent changes in pharmacological treatment and multiple modifying co-occurring conditions
including depression, bipolar disorder, schizophrenia, substance use disorders, traumatic brain injury, and
sleep disorders. Our long-term goal is to improve diagnostics, secondary/tertiary prevention, and treatment
outcomes of PTSD and its co-occurring conditions via enhanced EHR utilization. To achieve our objectives, we
will analyze EHR and administrative claims data from Veterans Health Administration (VHA) and non-VHA
databases, collectively covering >1.8M patients with PTSD. Specifically, we aim to: (1) Identify undetected and
uncoded co-occurring mental health phenotypes that impact PTSD outcomes using machine learning and
characterize disparities in their documentation; (2) Create robust models, accounting for biases and
co-occurring conditions, to identify clinical trajectories of PTSD decompensation/recovery in response to
time-varying treatments; and (3) Compare risk of self-harm and hospitalization among PTSD treatments using
coded and imputed phenotypes through an international network study. We will compare the effectiveness of
PTSD psychotropic monotherapies, polypharmacy, and psychotherapy to guide the choice of treatment for
improved patient outcomes. By enhancing and validating a positive-unlabeled machine learning approach
developed by our team, we will impute unrecorded/undetected mental health conditions co-occurring with
PTSD in both VHA and non-VHA databases, and characterize factors associated with documentation
disparities. We will model disease trajectories with enhanced latent class / latent trajectory analysis, focusing
on self-harm, substance use disorders, and psychiatric hospitalization in PTSD. Finally, we will perform the
largest comparative effectiveness studies to date of PTSD treatments on >100 monotherapy and
polypharmacy regimens, in addition to psychotherapy interventions, using causal models and methods for
addressing biases. These studies will provide high-quality evidence on the risk of hospitalizations and suicidal
acts/self-harm. Successful completion of these investigations will improve the quality of clinical psychiatric
decision-making, and guide improved service delivery to the Veteran and non-Veteran populations with
PTSD/TBI, and/or high risk of self-harm/suicidality.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert
🏛️ Recipient Organization (from NIH)
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR (ALBUQUERQUE, NM, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2022-12-23T00:00:00
End: 2026-11-30T00:00:00
End: 2026-11-30T00:00:00
💰 Award Amount (from NIH)
$608,473
📊 Fiscal Year (from NIH)
2025
🏷️ Activity Code (from NIH)
R01
🔢 Project Number (from NIH)
5R01MH129764-03
🔗 Full Project Record (from NIH)
Added
2023-03-24
Last modified
2023-03-24
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
Unsupervised and semi-supervised ML/AI with iterative experimentation for rapid identification of targeted alphaviral small molecules
2022-10 to 2025-10 | Grant
Defense Threat Reduction Agency (VA, VA, US)
Homepage URL: https://www.usaspending.gov/award/ASST_NON_HDTRA12310005_097
GRANT_NUMBER: HDTRA12310005
Show more detailOrganization identifiers
Defense Threat Reduction Agency: VA, VA, US
Added
2025-09-29
Last modified
2025-09-29
Source:
Christophe Gerard Lambert
Christophe Gerard Lambert
Illuminating the Druggable Genome Data Coordinating Center - Engagement Plan with the CFDE ✓ NIH
2020-09-23 to 2023-09-22 | Grant
Office of the Director (Bethesda, US)
Homepage URL: https://app.dimensions.ai/details/grant/grant.9411836
GRANT_NUMBER: OT2OD030546
Show more detailOrganization identifiers
Office of the Director: Bethesda, US
Funding project translated title
Funding project translated title
(en)
Illuminating the Druggable Genome Data Coordinating Center - Engagement Plan with the CFDE
📄 Project Abstract (from NIH)
The Illuminating the Druggable Genome (IDG) consortium has two major goals: First, consolidate disparate
protein- and disease-centric data types from multiple sources, integrate and harmonize them, then make them
readily available to the public; Second, adapt and scale existing technologies to unveil the function of selected
understudied members of the G-protein coupled receptor, ion channel and protein kinase families. Within the
IDG, the Knowledge Management Center (IDG-KMC) integrates data from a wide range of chemical, biological
and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the
“dark genome”), and their potential contribution to specific pathologies. Specifically, the IDG KMC is creating
automated workflows to capture relevant public data for the entire proteome including manual annotations for
the IDG list, covering five major areas: genotype, phenotype, expression, structure & function, and interactions
& pathways. The IDG KMC designs, develops, implements, and updates the Target Central Repository Database
(TCRD), a protein knowledgebase. The IDG KMC also expands, improves, and maintains Pharos, the TCRD
portal, with support for automated data summaries, and active community feedback. Both TCRD and Pharos
already integrate data from three Common Fund projects: GTEx, IMPC/KOMP and LINCS. The IDG KMC
consolidates all the data generated by the Data and Resource Generation Centers (DRGCs), improving these
data findability, accessibility, interoperability, reusability (FAIRness) and serving these data on the Pharos portal.
The IDG program interface with the CFDE will enable hypothesis generation about novel drug targets for complex
diseases. Many other Common Fund (CF) programs produce data about genetic variants and differentially
expressed genes and proteins in the context of many complex human diseases. These genes in many cases do
not have much information about them. For example, the CF program Undiagnosed Disease Network (UDN)
identifies mutations in genes associated with undiagnosed diseases. The IDG-KMC has information from
empirical evidence and from computational predictions about the function of these genes, which are commonly
under-studied. Hence, data from the IDG-KMC can enrich the CFDE users who examine datasets that list genes
and proteins. Several IDG resources provide gene landing pages that provide unique information about genes.
These landing pages can be improved regarding FAIRness and can become a resource for the CFDE. In
addition, data collected by the DRGCs and by the R03 IDG awardees can enrich the content of the CFDE portal. In
particular, results from the R03 projects (Fig. 1) are currently not evaluated or stored in one place and are at risk
of becoming lost. The CFDE engagement will ensure that data from this investment remains available long term.
protein- and disease-centric data types from multiple sources, integrate and harmonize them, then make them
readily available to the public; Second, adapt and scale existing technologies to unveil the function of selected
understudied members of the G-protein coupled receptor, ion channel and protein kinase families. Within the
IDG, the Knowledge Management Center (IDG-KMC) integrates data from a wide range of chemical, biological
and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the
“dark genome”), and their potential contribution to specific pathologies. Specifically, the IDG KMC is creating
automated workflows to capture relevant public data for the entire proteome including manual annotations for
the IDG list, covering five major areas: genotype, phenotype, expression, structure & function, and interactions
& pathways. The IDG KMC designs, develops, implements, and updates the Target Central Repository Database
(TCRD), a protein knowledgebase. The IDG KMC also expands, improves, and maintains Pharos, the TCRD
portal, with support for automated data summaries, and active community feedback. Both TCRD and Pharos
already integrate data from three Common Fund projects: GTEx, IMPC/KOMP and LINCS. The IDG KMC
consolidates all the data generated by the Data and Resource Generation Centers (DRGCs), improving these
data findability, accessibility, interoperability, reusability (FAIRness) and serving these data on the Pharos portal.
The IDG program interface with the CFDE will enable hypothesis generation about novel drug targets for complex
diseases. Many other Common Fund (CF) programs produce data about genetic variants and differentially
expressed genes and proteins in the context of many complex human diseases. These genes in many cases do
not have much information about them. For example, the CF program Undiagnosed Disease Network (UDN)
identifies mutations in genes associated with undiagnosed diseases. The IDG-KMC has information from
empirical evidence and from computational predictions about the function of these genes, which are commonly
under-studied. Hence, data from the IDG-KMC can enrich the CFDE users who examine datasets that list genes
and proteins. Several IDG resources provide gene landing pages that provide unique information about genes.
These landing pages can be improved regarding FAIRness and can become a resource for the CFDE. In
addition, data collected by the DRGCs and by the R03 IDG awardees can enrich the content of the CFDE portal. In
particular, results from the R03 projects (Fig. 1) are currently not evaluated or stored in one place and are at risk
of becoming lost. The CFDE engagement will ensure that data from this investment remains available long term.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert, Jeremy Joseph Yang
🏛️ Recipient Organization (from NIH)
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR (ALBUQUERQUE, NM, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2025-09-23T00:00:00
End: 2027-03-22T00:00:00
End: 2027-03-22T00:00:00
💰 Award Amount (from NIH)
$350,000
📊 Fiscal Year (from NIH)
2025
🏷️ Activity Code (from NIH)
OT2
🔢 Project Number (from NIH)
4OT2OD030546-02
🔗 Full Project Record (from NIH)
Added
2023-03-24
Last modified
2023-03-24
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD and TBI ✓ NIH
2020-06-01 to 2021-05-31 | Grant
National Institute of Mental Health (Bethesda, US)
Homepage URL: https://app.dimensions.ai/details/grant/grant.9293321
GRANT_NUMBER: R56MH120826
Show more detailOrganization identifiers
National Institute of Mental Health: Bethesda, US
Funding project translated title
Funding project translated title
(en)
Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD and TBI
📄 Project Abstract (from NIH)
PROJECT SUMMARY
Post-traumatic stress disorder (PTSD) has complex profiles of co-occurring medical conditions (comorbidities)
and is associated with high risk of suicide, particularly among Veterans, in which it is a leading cause of death.
There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased use of off-label
medications and polypharmacy (multiple drugs used simultaneously). The consequent limited evidence on the
relative risks and benefits of treatments creates a crisis in PTSD management. Moreover, PTSD and its major
comorbidities [traumatic brain injury (TBI) and suicidality] often remain undocumented in electronic health
records (EHR). There is also poor predictability of disease outcomes since there are frequent changes in
pharmacological treatment and multiple modifying comorbidities. Our long-term goal is to improve diagnostics,
secondary/tertiary prevention, and treatment outcomes of PTSD and its comorbidities via enhanced EHR
utilization. To achieve our objectives, we will analyze EHR and administrative claims data from Veterans
Administration (VA) and non-VA databases, collectively covering >2M PTSD and >2M TBI patients.
Specifically, we aim to: (1) Identify undetected PTSD, TBI, and self-harm from EHRs (using machine learning
with and without natural language language processing) to guide health service improvements. (2) Predict
PTSD clinical course in the VA population through novel modeling of disease trajectories that account for
time-varying treatments and biases (3) Compare the effectiveness of PTSD psychotropic monotherapies,
polypharmacy, and psychotherapy to guide the choice of treatment for improved patient outcomes. By
enhancing and validating a machine learning approach developed by our team, we will impute unrecorded
PTSD, TBI, and self-harm from both datasets, and characterize factors associated with documentation
disparities. We will model diseases trajectories with enhanced latent class analysis, focusing on self-harm,
substance misuse, and psychiatric hospitalization in PTSD. With Local Control methodology innovations, we
will compare the risk of PTSD in veterans with and without comorbid TBI. Finally, we will perform the largest
comparative effectiveness studies (to date) of PTSD treatments on >100 monotherapy and polypharmacy
regimens plus psychotherapy interventions. These studies will provide high-quality evidence on the risk of
hospitalizations, substance misuse, and suicidal acts/self-harm. Successful completion of these investigations
will improve the quality of decision making for providers and patients, and guide improved service delivery to
the population of veterans and non-veterans with PTSD/TBI, and/or high risk of suicide.
Post-traumatic stress disorder (PTSD) has complex profiles of co-occurring medical conditions (comorbidities)
and is associated with high risk of suicide, particularly among Veterans, in which it is a leading cause of death.
There is a critical lack of advancement in PTSD pharmacotherapy, as illustrated by increased use of off-label
medications and polypharmacy (multiple drugs used simultaneously). The consequent limited evidence on the
relative risks and benefits of treatments creates a crisis in PTSD management. Moreover, PTSD and its major
comorbidities [traumatic brain injury (TBI) and suicidality] often remain undocumented in electronic health
records (EHR). There is also poor predictability of disease outcomes since there are frequent changes in
pharmacological treatment and multiple modifying comorbidities. Our long-term goal is to improve diagnostics,
secondary/tertiary prevention, and treatment outcomes of PTSD and its comorbidities via enhanced EHR
utilization. To achieve our objectives, we will analyze EHR and administrative claims data from Veterans
Administration (VA) and non-VA databases, collectively covering >2M PTSD and >2M TBI patients.
Specifically, we aim to: (1) Identify undetected PTSD, TBI, and self-harm from EHRs (using machine learning
with and without natural language language processing) to guide health service improvements. (2) Predict
PTSD clinical course in the VA population through novel modeling of disease trajectories that account for
time-varying treatments and biases (3) Compare the effectiveness of PTSD psychotropic monotherapies,
polypharmacy, and psychotherapy to guide the choice of treatment for improved patient outcomes. By
enhancing and validating a machine learning approach developed by our team, we will impute unrecorded
PTSD, TBI, and self-harm from both datasets, and characterize factors associated with documentation
disparities. We will model diseases trajectories with enhanced latent class analysis, focusing on self-harm,
substance misuse, and psychiatric hospitalization in PTSD. With Local Control methodology innovations, we
will compare the risk of PTSD in veterans with and without comorbid TBI. Finally, we will perform the largest
comparative effectiveness studies (to date) of PTSD treatments on >100 monotherapy and polypharmacy
regimens plus psychotherapy interventions. These studies will provide high-quality evidence on the risk of
hospitalizations, substance misuse, and suicidal acts/self-harm. Successful completion of these investigations
will improve the quality of decision making for providers and patients, and guide improved service delivery to
the population of veterans and non-veterans with PTSD/TBI, and/or high risk of suicide.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert
🏛️ Recipient Organization (from NIH)
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR (ALBUQUERQUE, NM, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2020-06-01T00:00:00
End: 2022-05-31T00:00:00
End: 2022-05-31T00:00:00
💰 Award Amount (from NIH)
$776,198
📊 Fiscal Year (from NIH)
2020
🏷️ Activity Code (from NIH)
R56
🔢 Project Number (from NIH)
1R56MH120826-01A1
🔗 Full Project Record (from NIH)
Added
2021-03-03
Last modified
2021-03-03
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
A microaggregation framework for reproducible research with observational data: addressing biases while protecting personal identities ✓ NIH
2016-07-01 to 2018-06-30 | Grant
National Library of Medicine (Bethesda, US)
GRANT_NUMBER: R21LM012389
Show more detailOrganization identifiers
National Library of Medicine: Bethesda, US
📄 Project Abstract (from NIH)
PROJECT SUMMARY/ABSTRACT
The primary objective of the current proposal is to foster efforts towards transparent and
reproducible knowledge repositories for evidence-based medicine. The wealth of healthcare
data already available in electronic health records could be better utilized to help guide
treatment choices and compliment findings from randomized controlled trials. This proposal
addresses two major obstacles. The first is the challenge of deriving high-quality evidence from
observational data in the presence of biases and confounders, particularly with temporal data.
The second is that patient privacy and other concerns prevent disclosure of source data, which
hinders reproducible research -- currently there is a vast body of medical literature whose
findings guide clinical practice, yet cannot be independently scrutinized. We will address these
challenges through an innovative methodology, local control, which both corrects biases and
enables disclosure of question-specific microaggregated data to reproduce research findings
without disclosure of individual information. The key idea behind local control is to form many
homogeneous patient clusters within which one can compare alternate treatments, statistically
correcting for measured biases and confounders, analogous to a randomized block design. Our
methodology provides a unified framework for enabling open, high quality, comparative
effectiveness research by combining novel feature selection approaches, based on fractional
factorial experimental design, with advances in survival analysis, including competing risks. We
will create a public R package containing a family of methods for nonparametric bias correction
and statistical disclosure control in cross-sectional, case-control, and survival analysis settings.
Success of this research will also enable a novel model, we term “parcelled data sharing” to
facilitate open selective release of proprietary data sources for specific questions --
simultaneously protecting patient privacy, proprietary interests, and the public good. Our
research will contribute to the goal of evidence-based medicine being supported by national and
global knowledge bases on thousands of comparative effectiveness questions from 100’s of
millions of patients’ health records. This application supports the NLM mission by assisting in
the advancement of medical and related sciences through the dissemination and exchange of
important information to the progress of medicine and health. The specific aims are to (1)
Develop and evaluate a survival-based local control methodology for bias-corrected treatment
comparisons in time-to-event observational data; and (2) Develop and evaluate local control-
based microaggregation for reproducible research.
The primary objective of the current proposal is to foster efforts towards transparent and
reproducible knowledge repositories for evidence-based medicine. The wealth of healthcare
data already available in electronic health records could be better utilized to help guide
treatment choices and compliment findings from randomized controlled trials. This proposal
addresses two major obstacles. The first is the challenge of deriving high-quality evidence from
observational data in the presence of biases and confounders, particularly with temporal data.
The second is that patient privacy and other concerns prevent disclosure of source data, which
hinders reproducible research -- currently there is a vast body of medical literature whose
findings guide clinical practice, yet cannot be independently scrutinized. We will address these
challenges through an innovative methodology, local control, which both corrects biases and
enables disclosure of question-specific microaggregated data to reproduce research findings
without disclosure of individual information. The key idea behind local control is to form many
homogeneous patient clusters within which one can compare alternate treatments, statistically
correcting for measured biases and confounders, analogous to a randomized block design. Our
methodology provides a unified framework for enabling open, high quality, comparative
effectiveness research by combining novel feature selection approaches, based on fractional
factorial experimental design, with advances in survival analysis, including competing risks. We
will create a public R package containing a family of methods for nonparametric bias correction
and statistical disclosure control in cross-sectional, case-control, and survival analysis settings.
Success of this research will also enable a novel model, we term “parcelled data sharing” to
facilitate open selective release of proprietary data sources for specific questions --
simultaneously protecting patient privacy, proprietary interests, and the public good. Our
research will contribute to the goal of evidence-based medicine being supported by national and
global knowledge bases on thousands of comparative effectiveness questions from 100’s of
millions of patients’ health records. This application supports the NLM mission by assisting in
the advancement of medical and related sciences through the dissemination and exchange of
important information to the progress of medicine and health. The specific aims are to (1)
Develop and evaluate a survival-based local control methodology for bias-corrected treatment
comparisons in time-to-event observational data; and (2) Develop and evaluate local control-
based microaggregation for reproducible research.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert
🏛️ Recipient Organization (from NIH)
UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR (ALBUQUERQUE, NM, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2016-07-01T00:00:00
End: 2019-06-30T00:00:00
End: 2019-06-30T00:00:00
💰 Award Amount (from NIH)
$162,870
📊 Fiscal Year (from NIH)
2017
🏷️ Activity Code (from NIH)
R21
🔢 Project Number (from NIH)
5R21LM012389-02
🔗 Full Project Record (from NIH)
Added
2017-06-21
Last modified
2017-06-21
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
Longitudinal Comparative Effectiveness of Bipolar Disorder Therapies
2016-01-01 to 2020-01-31 | Grant
Patient-Centered Outcomes Research Institute (Washington, US)
Homepage URL: https://grants.uberresearch.com/100006093/6bbd239d/Longitudinal-Comparative-Effectiveness-of-Bipolar-Disorder-Therapies
GRANT_NUMBER: 6bbd239d
Show more detailOrganization identifiers
Patient-Centered Outcomes Research Institute: Washington, US
Added
2017-06-21
Last modified
2017-06-21
Source:
DimensionsWizard via Christophe Gerard Lambert
DimensionsWizard via Christophe Gerard Lambert
Data Driven Prognostics
2002-10-01 to 2003-09-30 | Grant
Department of Defense, Small Business Innovation Research (Washington, US)
Homepage URL: https://grants.uberresearch.com/100000005/177031/Data-Driven-Prognostics
GRANT_NUMBER: F33615-03-M-4122
Show more detailOrganization identifiers
Department of Defense, Small Business Innovation Research: Washington, US
Added
2017-06-21
Last modified
2017-06-21
Source:
DimensionsWizard via Christophe Gerard Lambert
DimensionsWizard via Christophe Gerard Lambert
Software Relating Genes to Disease and Clinical Outcomes ✓ NIH
2001-04-01 to 2005-12-31 | Grant
National Institute of General Medical Sciences (Bethesda, US)
Homepage URL: https://grants.uberresearch.com/100000057/R44GM062081/Software-Relating-Genes-to-Disease-and-Clinical-Outcomes
GRANT_NUMBER: R44GM062081
Show more detailOrganization identifiers
National Institute of General Medical Sciences: Bethesda, US
📄 Project Abstract (from NIH)
DESCRIPTION (provided by applicant): The development of a software system is proposed that will combine statistical theory, computer science algorithms, and genetics expertise to take advantage of the great influx of data generated by the study of the human genome, clinical trials data and the creation of inexpensive genotyping techniques. This software will elucidate the complex relationship between drug efficacy and side effects, multiple interacting genes and environmental factors.
Our Phase I results show it is feasible to link phenotype to genotype for a list of "candidate" genes. A novel haplotype trend test has been developed to aid in finding associations across large SNP maps. Commercialization of this technique is essential for companies that intend to use large public or private SNP maps to locate genes that are associated with disease and drug safety and efficacy. Our statistical methods are expected to be successful even if the disease mechanism can differ from one person to another.
By analyzing and interpreting clinical trial data, the software will match drugs to target populations according to their specific genotype. This will enable pharmaceutical companies to create novel drugs that render maximum effectiveness and have minimum side effects, i.e. the right drug for the right person.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert
🏛️ Recipient Organization (from NIH)
GOLDEN HELIX, INC. (BOZEMAN, MT, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2001-04-01T00:00:00
End: 2005-12-31T00:00:00
End: 2005-12-31T00:00:00
💰 Award Amount (from NIH)
$69,000
📊 Fiscal Year (from NIH)
2005
🏷️ Activity Code (from NIH)
R44
🔢 Project Number (from NIH)
3R44GM062081-03S1
🔗 Full Project Record (from NIH)
Added
2017-06-21
Last modified
2017-06-21
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
Software Relating Genes to Disease and Clinical Outcomes ✓ NIH
2001-04-01 to 2001-09-30 | Grant
National Institute of General Medical Sciences (Bethesda, US)
Homepage URL: https://grants.uberresearch.com/100000057/R43GM062081/Software-Relating-Genes-to-Disease-and-Clinical-Outcomes
GRANT_NUMBER: R43GM062081
Show more detailOrganization identifiers
National Institute of General Medical Sciences: Bethesda, US
📄 Project Abstract (from NIH)
DESCRIPTION (Applicant's abstract): The development of a software system is
proposed that will combine statistical theory, computer science algorithms, and
genetics expertise to take advantage of the great influx of data generated by
both the study of the human genome and the creation of inexpensive genotyping
techniques. This software will elucidate the complex relationship between drug
efficacy and side effects, and multiple interacting genes and environmental
factors. Preliminary results, obtained by using simulated data, indicate that
it might be feasible to link phenotype to genotype for a list of "candidate
genes." The statistical methods are expected to be successful even if the
disease mechanism can differ from one person to another. By analyzing and
interpreting clinical trial data, the software will match drugs to target
populations according to their specific genotype. This will enable
pharmaceutical companies to create novel drugs that render maximum
effectiveness and have minimum side effects, i.e. the right drug for the right
person.
PROPOSED COMMERCIAL APPLICATION:
The target markets for the research include pharmaceutical companies, CRO'S
universities, and government agencies. It has good potential for commercialization
because it is expected to help create novel drugs, boost the safety of drug treatments,
save substantial resources, and make sense of complex genotype/phenotype relationships
in clinical trials context.
👤 Principal Investigator(s) (from NIH)
Christophe G. Lambert
🏛️ Recipient Organization (from NIH)
GOLDEN HELIX, INC. (BOZEMAN, MT, UNITED STATES)
📅 Project Dates (from NIH)
Start: 2001-04-01T00:00:00
End: 2001-09-30T00:00:00
End: 2001-09-30T00:00:00
💰 Award Amount (from NIH)
$99,650
📊 Fiscal Year (from NIH)
2001
🏷️ Activity Code (from NIH)
R43
🔢 Project Number (from NIH)
1R43GM062081-01A1
🔗 Full Project Record (from NIH)
Added
2017-06-21
Last modified
2017-06-21
Source:
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
DimensionsWizard via Christophe Gerard Lambert
| ✓ Enriched from NIH
Education and qualifications (4)
Duke University: Durham, NC, US
Organization identifiers
RINGGOLD:
3065
Duke University : Durham, NC, US
Department
Computer Science
Added
2015-10-23
Last modified
2015-10-23
Source:
Christophe Gerard Lambert
Christophe Gerard Lambert
Duke University: Durham, North Carolina, US
Organization identifiers
Duke University : Durham, North Carolina, US
Other organization identifiers provided by ROR
FUNDREF:
100006510 (preferred), 100005531, 100008200, 100008936, 100010326, 100002690, 100006967, 100005853, 100005854, 100005855, 100006511, 100006513, 100006830, 100007908, 100006512, 100005572, 100015191, 100017461
GRID:
grid.26009.3d (preferred)
ISNI:
0000000419367961
WIKIPEDIA_URL:
http://en.wikipedia.org/wiki/Duke_University (preferred)
Department
Computer Science
Added
2021-09-09
Last modified
2025-04-15
Source:
Christophe Gerard Lambert
Christophe Gerard Lambert
Montana State University: Bozeman, MT, US
Department
Computer Science
Added
2025-04-15
Last modified
2025-04-15
Source:
Christophe Gerard Lambert
Christophe Gerard Lambert
University of Calgary: Calgary, AB, CA
Organization identifiers
RINGGOLD:
2129
University of Calgary : Calgary, AB, CA
Department
Computer Science
Homepage URL
Added
2021-09-09
Last modified
2021-09-09
Source:
Christophe Gerard Lambert
Christophe Gerard Lambert
Praveen Kumar, Vincent T. Metzger, Swastika T. Purushotham, Priyansh Kedia, Cristian G. Bologa, Christophe G. Lambert, Jeremy J. Yang. KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes. 2025 March. DOI: 10.1101/2025.03.17.25323906.
Ringer JA, Lambert CG, Bradfute SB, Bologa CG, Yang JJ. Badapple 2.0: An Empirical Predictor of Compound Promiscuity, Updated, Modernized, and Enhanced for Explainability. J Chem Inf Model. 2025 Dec 8;65(23):12641-12647. doi: 10.1021/acs.jcim.5c02297. Epub 2025 Nov 14. PubMed PMID: 41235766.
Kumar P, Metzger V, Purushotham S, Kedia P, Bologa C, Lambert C, Yang J. KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes. [preprint]. 2025 March. doi: 10.1101/2025.03.17.25323906.
Kumar P, Moomtaheen F, Malec SA, Yang JJ, Bologa CG, Schneider KA, Zhu Y, Tohen M, Villarreal G, Perkins DJ, Fielstein EM, Davis SE, Matheny ME, Lambert CG. Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning. IEEE J Biomed Health Inform. 2025 Feb;29(2):750-757. doi: 10.1109/JBHI.2024.3515805. Epub 2025 Feb 10. PubMed PMID: 40030473; PubMed Central PMCID: PMC11971012.
Idnay B, Xu Z, Adams WG, Adibuzzaman M, Anderson NR, Bahroos N, Bell DS, Bumgardner C, Campion T, Castro M, Cimino JJ, Cohen IG, Dorr D, Elkin PL, Fan JW, Ferris T, Foran DJ, Hanauer D, Hogarth M, Huang K, Kalpathy-Cramer J, Kandpal M, Karnik NS, Katoch A, Lai AM, Lambert CG, Li L, Lindsell C, Liu J, Lu Z, Luo Y, McGarvey P, Mendonca EA, Mirhaji P, Murphy S, Osborne JD, Paschalidis IC, Harris PA, Prior F, Shaheen NJ, Shara N, Sim I, Tachinardi U, Waitman LR, Wright RJ, Zai AH, Zheng K, Lee SS, Malin BA, Natarajan K, Price Ii WN, Zhang R, Zhang Y, Xu H, Bian J, Weng C, Peng Y. Environment scan of generative AI infrastructure for clinical and translational science. Npj Health Syst. 2025;2(1):4. doi: 10.1038/s44401-024-00009-w. Epub 2025 Jan 25. PubMed PMID: 39872195; PubMed Central PMCID: PMC11762411.
Clinton O. Onyango, Samuel B. Anyona, Ivy Hurwitz, Evans Raballah, Sharely A. Wasena, Shamim W. Osata, Philip Seidenberg, Benjamin H. McMahon, Christophe G. Lambert, Kristan A. Schneider, Collins Ouma, Qiuying Cheng, Douglas J. Perkins. Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism. Pathogens. 2024 October. doi: 10.3390/pathogens13100867.
Kumar P, Lambert CG. Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation without the selected completely at random assumption. PeerJ Comput Sci. 2024;10:e2451. doi: 10.7717/peerj-cs.2451. eCollection 2024. PubMed PMID: 39650456; PubMed Central PMCID: PMC11622864.
Onyango CO, Anyona SB, Hurwitz I, Raballah E, Wasena SA, Osata SW, Seidenberg P, McMahon BH, Lambert CG, Schneider KA, Ouma C, Cheng Q, Perkins DJ. Transcriptomic and Proteomic Insights into Host Immune Responses in Pediatric Severe Malarial Anemia: Dysregulation in HSP60-70-TLR2/4 Signaling and Altered Glutamine Metabolism. Pathogens. 2024 Oct 3;13(10). doi: 10.3390/pathogens13100867. PubMed PMID: 39452740; PubMed Central PMCID: PMC11510049.
Kumar P, Moomtaheen F, Malec SA, Yang JJ, Bologa CG, Schneider KA, Zhu Y, Tohen M, Villarreal G, Perkins DJ, Fielstein EM, Davis SE, Matheny ME, Lambert CG. Quantifying the opioid use disorder crisis: PULSNAR finds nearly 3/4 undiagnosed. 2024 OHDSI Symposium. 2024 October; East Brunswick, NJ, United States.
Samuel Anyona, Qiuying Cheng, Yan Guo, Evans Raballah, Ivy Hurwitz, Clinton Onyango, Philip Seidenberg, Kristan Schneider, Christophe Lambert, Benjamin McMahon, Collins Ouma, Douglas Perkins. Entire Expressed Peripheral Blood Transcriptome in Pediatric Severe Malarial Anemia. 2023 July. DOI: 10.21203/rs.3.rs-3150748/v1.
Ivy Hurwitz, Alexandra V Yingling, Teah Amirkabirian, Amber Castillo, Jehanzaeb J Khan, Alexandra Do, Dominic K Lundquist, October Barnes, Christophe G Lambert, Annabeth Fieck, Gregory Mertz, Clinton Onyango, Samuel B Anyona, J Pedro Teixeira, Michelle Harkins, Mark Unruh, Qiuying Cheng, Shuguang Leng, Philip Seidenberg, Anthony Worsham, Jens O Langsjoen, Kristan A Schneider, Douglas J Perkins. Disproportionate impact of COVID-19 severity and mortality on hospitalized American Indian/Alaska Native patients. PNAS Nexus. 2023 August. doi: 10.1093/pnasnexus/pgad259.
Praveen Kumar, Christophe G. Lambert. Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation when the SCAR assumption does not hold. 2023. doi: 10.48550/ARXIV.2303.08269.
Onyango CO, Cheng Q, Munde EO, Raballah E, Anyona SB, McMahon BH, Lambert CG, Onyango PO, Schneider KA, Perkins DJ, Ouma C. Human NCR3 gene variants rs2736191 and rs11575837 alter longitudinal risk for development of pediatric malaria episodes and severe malarial anemia. BMC Genomics. 2023 Sep 13;24(1):542. doi: 10.1186/s12864-023-09565-1. PubMed PMID: 37704951; PubMed Central PMCID: PMC10498606.
Anyona S, Cheng Q, Guo Y, Raballah E, Hurwitz I, Onyango C, Seidenberg P, Schneider K, Lambert C, McMahon B, Ouma C, Perkins D. Entire Expressed Peripheral Blood Transcriptome in Pediatric Severe Malarial Anemia. Res Sq. 2023 Jul 19;. doi: 10.21203/rs.3.rs-3150748/v1. PubMed PMID: 37503086; PubMed Central PMCID: PMC10371159.
Evangelista JE, Clarke DJB, Xie Z, Marino GB, Utti V, Jenkins SL, Ahooyi TM, Bologa CG, Yang JJ, Binder JL, Kumar P, Lambert CG, Grethe JS, Wenger E, Taylor D, Oprea TI, de Bono B, Ma'ayan A. Toxicology knowledge graph for structural birth defects. Commun Med (Lond). 2023 Jul 17;3(1):98. doi: 10.1038/s43856-023-00329-2. PubMed PMID: 37460679; PubMed Central PMCID: PMC10352311.
Kumar P, Lambert CG. PULSNAR -- Positive unlabeled learning selected not at random: class proportion estimation when the SCAR assumption does not hold. [preprint]. 2023 March. Available from: https://arxiv.org/abs/2303.08269. doi: https://doi.org/10.48550/arXiv.2303.08269.
Samuel B. Anyona, Qiuying Cheng, Evans Raballah, Ivy Hurwitz, Christophe G. Lambert, Benjamin H. McMahon, Collins Ouma, Douglas J. Perkins. Ingestion of hemozoin by peripheral blood mononuclear cells alters temporal gene expression of ubiquitination processes. Biochemistry and Biophysics Reports. 2022 March; 29:101207. doi: 10.1016/j.bbrep.2022.101207.
Kisia LE, Cheng Q, Raballah E, Munde EO, McMahon BH, Hengartner NW, Ong'echa JM, Chelimo K, Lambert CG, Ouma C, Kempaiah P, Perkins DJ, Schneider KA, Anyona SB. Genetic variation in CSF2 (5q31.1) is associated with longitudinal susceptibility to pediatric malaria, severe malarial anemia, and all-cause mortality in a high-burden malaria and HIV region of Kenya. Trop Med Health. 2022 Jun 25;50(1):41. doi: 10.1186/s41182-022-00432-5. PubMed PMID: 35752805; PubMed Central PMCID: PMC9233820.
Perkins DJ, Yingling AV, Cheng Q, Castillo A, Martinez J, Bradfute SB, Leng S, Edwards J, Guo Y, Mertz G, Harkins M, Unruh M, Worsham A, Lambert CG, Teixeira JP, Seidenberg P, Langsjoen J, Schneider K, Hurwitz I. Elevated SARS-CoV-2 in peripheral blood and increased COVID-19 severity in American Indians/Alaska Natives. Exp Biol Med (Maywood). 2022 Jul;247(14):1253-1263. doi: 10.1177/15353702221091180. Epub 2022 May 1. PubMed PMID: 35491994; PubMed Central PMCID: PMC9379605.
Clarke DJB, Kuleshov MV, Xie Z, Evangelista JE, Meyers MR, Kropiwnicki E, Jenkins SL, Ma'ayan A. Gene and drug landing page aggregator. Bioinform Adv. 2022;2(1):vbac013. doi: 10.1093/bioadv/vbac013. eCollection 2022. PubMed PMID: 35368424; PubMed Central PMCID: PMC8969666.
Jarratt L, Situ J, King RD, Montanez Ramos E, Groves H, Ormesher R, Cossé M, Raboff A, Mahajan A, Thompson J, Ko RF, Paltrow-Krulwich S, Price A, Hurwitz AM, CampBell T, Epler LT, Nguyen F, Wolinsky E, Edwards-Fligner M, Lobo J, Rivera D, Langsjoen J, Sloane L, Hendrix I, Munde EO, Onyango CO, Olewe PK, Anyona SB, Yingling AV, Lauve NR, Kumar P, Stoicu S, Nestsiarovich A, Bologa CG, Oprea TI, Tollestrup K, Myers OB, Anixter M, Perkins DJ, Lambert CG. A Comprehensive COVID-19 Daily News and Medical Literature Briefing to Inform Health Care and Policy in New Mexico: Implementation Study. JMIR Med Educ. 2022 Feb 23;8(1):e23845. doi: 10.2196/23845. PubMed PMID: 35142625; PubMed Central PMCID: PMC8908195.
Kropiwnicki E, Lachmann A, Clarke DJB, Xie Z, Jagodnik KM, Ma'ayan A. DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules. BMC Bioinformatics. 2022 Feb 19;23(1):76. doi: 10.1186/s12859-022-04590-5. PubMed PMID: 35183110; PubMed Central PMCID: PMC8858480.
Anyona SB, Cheng Q, Raballah E, Hurwitz I, Lambert CG, McMahon BH, Ouma C, Perkins DJ. Ingestion of hemozoin by peripheral blood mononuclear cells alters temporal gene expression of ubiquitination processes. Biochem Biophys Rep. 2022 Mar;29:101207. doi: 10.1016/j.bbrep.2022.101207. eCollection 2022 Mar. PubMed PMID: 35071802; PubMed Central PMCID: PMC8761598.
Nestsiarovich, Anastasiyaand Kumar, Praveenand Lauve, Nicolas Raymondand Hurwitz, Nathaniel Gand Mazurie, Aur'elien Jand Cannon, Daniel Cand Zhu, Yiliangand Nelson, Stuart Jamesand Crisanti, Annette Sand Kerner, Beritand Tohen, Mauricioand Perkins, Douglas Jand Lambert, Christophe Gerard. Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study. JMIR Ment Health. 2021 April; 8(4):e24522. doi: 10.2196/24522.
Nestsiarovich A, Reps JM, Matheny ME, DuVall SL, Lynch KE, Beaton M, Jiang X, Spotnitz M, Pfohl SR, Shah NH, Torre CO, Reich CG, Lee DY, Son SJ, You SC, Park RW, Ryan PB, Lambert CG. Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study. Transl Psychiatry. 2021 Dec 20;11(1):642. doi: 10.1038/s41398-021-01760-6. PubMed PMID: 34930903; PubMed Central PMCID: PMC8688463.
Raballah E, Anyona SB, Cheng Q, Munde EO, Hurwitz IF, Onyango C, Ndege C, Hengartner NW, Pacheco MA, Escalante AA, Lambert CG, Ouma C, Obama HCJT, Schneider KA, Seidenberg PD, McMahon BH, Perkins DJ. Complement component 3 mutations alter the longitudinal risk of pediatric malaria and severe malarial anemia. Exp Biol Med (Maywood). 2022 Apr;247(8):672-682. doi: 10.1177/15353702211056272. Epub 2021 Nov 29. PubMed PMID: 34842470; PubMed Central PMCID: PMC9039490.
Yang JJ, Grissa D, Lambert CG, Bologa CG, Mathias SL, Waller A, Wild DJ, Jensen LJ, Oprea TI. TIGA: target illumination GWAS analytics. Bioinformatics. 2021 Nov 5;37(21):3865-3873. doi: 10.1093/bioinformatics/btab427. PubMed PMID: 34086846; PubMed Central PMCID: PMC11025677.
Davis SE, Kumar P, Lauve NR, Parr SK, Park D, Matheny ME, Villarreal G, Uhl G, Zhu Y, Tohen M, Perkins DJ, Lambert CG. Disparities in Coded and Imputed Post-Traumatic Stress Disorder and Self-Harm Among US Veterans. AMIA 2021 Annual Symposium; 2021 November 2; San Diego, CA, USA. c2021.
Kumar P, Lauve NR, Davis SE, Parr SK, Park D, Matheny ME, Villarreal G, Uhl G, Zhu Y, Tohen M, Perkins DJ, Lambert CG. Detecting PTSD and self-harm among US Veterans using positive unlabeled learning. OHDSI 2021 Global Symposium; 2021 September 14; online. Observational Health Data Sciences and Informatics; c2021.
Haendel MA, Chute CG, Bennett TD, Eichmann DA, Guinney J, Kibbe WA, Payne PRO, Pfaff ER, Robinson PN, Saltz JH, Spratt H, Suver C, Wilbanks J, Wilcox AB, Williams AE, Wu C, Blacketer C, Bradford RL, Cimino JJ, Clark M, Colmenares EW, Francis PA, Gabriel D, Graves A, Hemadri R, Hong SS, Hripscak G, Jiao D, Klann JG, Kostka K, Lee AM, Lehmann HP, Lingrey L, Miller RT, Morris M, Murphy SN, Natarajan K, Palchuk MB, Sheikh U, Solbrig H, Visweswaran S, Walden A, Walters KM, Weber GM, Zhang XT, Zhu RL, Amor B, Girvin AT, Manna A, Qureshi N, Kurilla MG, Michael SG, Portilla LM, Rutter JL, Austin CP, Gersing KR. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc. 2021 Mar 1;28(3):427-443. doi: 10.1093/jamia/ocaa196. PubMed PMID: 32805036; PubMed Central PMCID: PMC7454687.
Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert. LocalControl: An R Package for Comparative Safety and Effectiveness Research. Journal of Statistical Software. 2020; 96(4). doi: 10.18637/jss.v096.i04.
Praveen Kumar, Anastasiya Nestsiarovich, Stuart J Nelson, Berit Kerner, Douglas J Perkins, Christophe G Lambert. Imputation and characterization of uncoded self-harm in major mental illness using machine learning. Journal of the American Medical Informatics Association. 2020 January. doi: 10.1093/jamia/ocz173.
Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte-Salles T, Fernández-Bertolín S, Aragón M, DuVall SL, Lynch K, Falconer T, van Bochove K, Sung C, Matheny ME, Lambert CG, Nyberg F, Alshammari TM, Williams AE, Park RW, Weaver J, Sena AG, Schuemie MJ, Rijnbeek PR, Williams RD, Lane JCE, Prats-Uribe A, Zhang L, Areia C, Krumholz HM, Prieto-Alhambra D, Ryan PB, Hripcsak G, Suchard MA. Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis. Lancet Digit Health. 2021 Feb;3(2):e98-e114. doi: 10.1016/S2589-7500(20)30289-2. Epub 2020 Dec 17. PubMed PMID: 33342753; PubMed Central PMCID: PMC7834915.
Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl: An R Package for Comparative Safety and Effectiveness Research. J Stat Softw. 2020;96(4). doi: 10.18637/jss.v096.i04. Epub 2020 Nov 29. PubMed PMID: 34349611; PubMed Central PMCID: PMC8330612.
Lane JCE, Weaver J, Kostka K, Duarte-Salles T, Abrahao MTF, Alghoul H, Alser O, Alshammari TM, Biedermann P, Banda JM, Burn E, Casajust P, Conover MM, Culhane AC, Davydov A, DuVall SL, Dymshyts D, Fernandez-Bertolin S, Fišter K, Hardin J, Hester L, Hripcsak G, Kaas-Hansen BS, Kent S, Khosla S, Kolovos S, Lambert CG, van der Lei J, Lynch KE, Makadia R, Margulis AV, Matheny ME, Mehta P, Morales DR, Morgan-Stewart H, Mosseveld M, Newby D, Nyberg F, Ostropolets A, Park RW, Prats-Uribe A, Rao GA, Reich C, Reps J, Rijnbeek P, Sathappan SMK, Schuemie M, Seager S, Sena AG, Shoaibi A, Spotnitz M, Suchard MA, Torre CO, Vizcaya D, Wen H, de Wilde M, Xie J, You SC, Zhang L, Zhuk O, Ryan P, Prieto-Alhambra D. Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study. Lancet Rheumatol. 2020 Nov;2(11):e698-e711. doi: 10.1016/S2665-9913(20)30276-9. Epub 2020 Aug 21. PubMed PMID: 32864627; PubMed Central PMCID: PMC7442425.
Kim Y, Tian Y, Yang J, Huser V, Jin P, Lambert CG, Park H, You SC, Park RW, Rijnbeek PR, Van Zandt M, Reich C, Vashisht R, Wu Y, Duke J, Hripcsak G, Madigan D, Shah NH, Ryan PB, Schuemie MJ, Suchard MA. Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis. Sci Rep. 2020 Jul 6;10(1):11115. doi: 10.1038/s41598-020-68037-8. PubMed PMID: 32632237; PubMed Central PMCID: PMC7338498.
Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte-Salles T, Fernández-Bertolín S, Aragón M, DuVall SL, Lynch K, Falconer T, van Bochove K, Sung C, Matheny ME, Lambert CG, Nyberg F, Alshammari TM, Williams AE, Park RW, Weaver J, Sena AG, Schuemie MJ, Rijnbeek PR, Williams RD, Lane JCE, Prats-Uribe A, Zhang L, Areia C, Krumholz HM, Prieto-Alhambra D, Ryan PB, Hripcsak G, Suchard MA. Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study. medRxiv. 2020 Jun 12;. doi: 10.1101/2020.06.11.20125849. PubMed PMID: 32587982; PubMed Central PMCID: PMC7310640.
Nestsiarovich A, Kerner B, Mazurie AJ, Cannon DC, Hurwitz NG, Zhu Y, Nelson SJ, Oprea TI, Crisanti AS, Tohen M, Perkins DJ, Lambert CG. Diabetes mellitus risk for 102 drugs and drug combinations used in patients with bipolar disorder. Psychoneuroendocrinology. 2020 Feb;112:104511. doi: 10.1016/j.psyneuen.2019.104511. Epub 2019 Nov 9. PubMed PMID: 31744781.
Anyona SB, Hengartner NW, Raballah E, Ong'echa JM, Lauve N, Cheng Q, Fenimore PW, Ouma C, Lambert CG, McMahon BH, Perkins DJ. Cyclooxygenase-2 haplotypes influence the longitudinal risk of malaria and severe malarial anemia in Kenyan children from a holoendemic transmission region. J Hum Genet. 2020 Jan;65(2):99-113. doi: 10.1038/s10038-019-0692-3. Epub 2019 Oct 29. PubMed PMID: 31664161; PubMed Central PMCID: PMC7255056.
Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. Local Control: a microaggregation methodology for performing bias-corrected reproducible observational studies across data silos while protecting patient privacy. 2019 OHDSI Symposium; 2019 September 16; Bethesda, MD. https://www.ohdsi.org/2019-us-symposium-showcase-51/; c2019.
Kisia LE, Kempaiah P, Anyona SB, Munde EO, Achieng AO, Ong'echa JM, Lambert CG, Chelimo K, Ouma C, Perkins DJ, Raballah E. Genetic variation in interleukin-7 is associated with a reduced erythropoietic response in Kenyan children infected with Plasmodium falciparum. BMC Med Genet. 2019 Aug 16;20(1):140. doi: 10.1186/s12881-019-0866-z. PubMed PMID: 31420016; PubMed Central PMCID: PMC6698010.
Achieng AO, Hengartner NW, Raballah E, Cheng Q, Anyona SB, Lauve N, Guyah B, Foo-Hurwitz I, Ong'echa JM, McMahon BH, Ouma C, Lambert CG, Perkins DJ. Integrated OMICS platforms identify LAIR1 genetic variants as novel predictors of cross-sectional and longitudinal susceptibility to severe malaria and all-cause mortality in Kenyan children. EBioMedicine. 2019 Jul;45:290-302. doi: 10.1016/j.ebiom.2019.06.043. Epub 2019 Jul 2. PubMed PMID: 31278068; PubMed Central PMCID: PMC6642287.
Achieng AO, Guyah B, Cheng Q, Ong'echa JM, Ouma C, Lambert CG, Perkins DJ. Molecular basis of reduced LAIR1 expression in childhood severe malarial anaemia: Implications for leukocyte inhibitory signalling. EBioMedicine. 2019 Jul;45:278-289. doi: 10.1016/j.ebiom.2019.06.040. Epub 2019 Jun 27. PubMed PMID: 31257148; PubMed Central PMCID: PMC6642411.
Kerner B, Crisanti AS, DeShaw JL, Ho JG, Jordan K, Krall RL, Kuntz MJ, Mazurie AJ, Nestsiarovich A, Perkins DJ, Schroeter QL, Smith AN, Tohen M, Volesky E, Zhu Y, Lambert CG. Preferences of Information Dissemination on Treatment for Bipolar Disorder: Patient-Centered Focus Group Study. JMIR Ment Health. 2019 Jun 25;6(6):e12848. doi: 10.2196/12848. PubMed PMID: 31237566; PubMed Central PMCID: PMC6614999.
Nestsiarovich A, Kerner B, Mazurie AJ, Cannon DC, Hurwitz NG, Zhu Y, Nelson SJ, Oprea TI, Unruh ML, Crisanti AS, Tohen M, Perkins DJ, Lambert CG. Comparison of 71 bipolar disorder pharmacotherapies for kidney disorder risk: The potential hazards of polypharmacy. J Affect Disord. 2019 Jun 1;252:201-211. doi: 10.1016/j.jad.2019.04.009. Epub 2019 Apr 8. PubMed PMID: 30986735.
Human Genome Informatics [Internet] Elsevier; 2018. Available from: https://doi.org/10.1016%2Fc2015-0-04314-3.
Christophe G. Lambert. How Cytogenetics Paradigms Shape Decision Making in Translational Genomics. 2018; :45--59. doi: 10.1016/b978-0-12-809414-3.00003-6.
Anastasiya Nestsiarovich, Aurélien J Mazurie, Nathaniel G Hurwitz, Berit Kerner, Stuart J Nelson, Annette S Crisanti, Mauricio Tohen, Ronald L Krall, Douglas J Perkins, Christophe G Lambert. Comprehensive comparison of monotherapies for psychiatric hospitalization risk in bipolar disorders. Bipolar Disorders. 2018 December. doi: 10.1111/bdi.12665.
Jennifer C. Thompson, Yuko M. Komesu, Fares Qeadan, Peter C. Jeppson, Sara B. Cichowski, Rebecca G. Rogers, Aurélien J. Mazurie, Anastasiya Nestsiarovich, Christophe G. Lambert, Gena C. Dunivan. Trends in patient procurement of postoperative opioids and route of hysterectomy in the United States from 2004 through 2014. American Journal of Obstetrics and Gynecology. 2018 November; 219(5):484.e1--484.e11. doi: 10.1016/j.ajog.2018.07.003.
J. Thompson, G. Dunivan, P.C. Jeppson, S. Cichowski, Y. Komesu, R. Rogers, A. Mazurie, A. Nestsiarovich, C. Lambert. 09: Trends in postoperative opioid prescribing practices and route of hysterectomy in the United States from 2003 to 2014. American Journal of Obstetrics and Gynecology. 2018 February; 218(2):S886--S887. doi: 10.1016/j.ajog.2017.12.193.