Lana Garmire

Lana Garmire, Ph.D. (CV) Linkedin Social Twitter Social
Associate Professor (with tenure)
Email: LGarmire at med dot umich dot edu

Primary Faculty
Department of Computational Medicine and Bioinformatics

Affiliated Faculty
Department of Biomedical Engineering
Department of Biostatistics

Rogel Cancer Center, Core member

Dr. Garmire is an awardee of US Presidential Early Career Scientists and Engineers in 2019, and a fellow of Americian Institute of Medical and Biological Engineering (AIMBE). She is a nationally and internationally recognized expert in translational bioinformatics.

Dr. Garmire obtained the MA degree in Statistics (2005) and PhD degree in Comparative Biochemistry (Computational Biology focus, 2007), both from UC-Berkeley. She started the first tenure-track faculty position in University of Hawaii Cancer Center later 2012 and was promoted to Associate Professor with tenure in 2017. She moved to University of Michigan in 2018 to expand the research to multi-modal research (genomics, EMR and pathological imaging analysis). She has published over 100 papers in top quality journals including Cell, Nature Communications, Genome Biology, Genome Medicine, Clinical Cancer Research. She contributed as the senior corresponding author in the majority of them. She delivered over 90 invited talks to institutes including National Library of Medicine (NLM) and National Academy of Sciences (NAS). She has mentored over 90 Assistant Professors, MD fellows, postdocs, graduate students and undergraduates of various academic backgrounds, in Biology, Mathematics, Physics, (bio)Statistics, Bioengineering, Computer Science and Electrical Engineering. Most PhD and postdoc trainees became faculty or senior scientists in private sectors. She has served on various NIH study sections and currently a standing member of BDMA study section. She is on the editorial advisory board for journals Genome Biology and Journal of Proteome Research.

As an academic mom of 2 young kids, she tweets about science, gender and racial disparity @GarmireGroup from She is a strong advocate for women in STEM, minority and under represented groups.

Research Interests

The major research interests of Garmire Group
single-cell and spatial transcriptomics and bioinformatics
Integrative EMR/imaging/omics/clinic data analysis
Translational bioinformatics of cancers, prognosis and diagnosis prediction
Actional computational models, drug reposition/repurposing
Women's and neonatal health research

Established Collaborators

Major established collaborators
University of Pennsyvinia:
Dr Shen Li, BMI
University of Michigan:
Dr. Evan Keller (Urology and Pathology)
Dr. Jiaqi Shi (Pathology)
Dr. Darnell Keigler (Dental School)
Dr. Duxin Sun (Pharmacology)
Dr. Anna Lok (GI)
Dr. Neehar Parikh (GI)
Dr. Maria Westerhoff (Pathology)
Dr. James Moon (Pharmacology)
Dr. Weiping Zou (Surgeory)
Dr. Julia Dial (Pathology)
Dr. Elizabeth Langen (OBGYN)
University of Hawaii School of Medicine:
Dr. Steven Ward (Director of Institute of Biogenesis Research)
Dr. Monika Ward (Institute of Biogenesis Research)
Dr. Ben Fogelgren (Department of Biochemistry, Physiology and Anatomy)
University of California at San Diego:
Dr. Eric Courchesne (Neuroscience)
Dr. Karen Pierce (Neuroscience)
University of Florida:
Dominick Lemas

Establishing Collaborators

Other establishing collaborators
UT San Antonio:
Dr. Kumar Sharma (Nephrology)


Principal Investigator

Lana Garmire, Ph.D.

Lana is a proud and longest-serving member of the Garmire group. She hopes to create an inclusive and diversified group, where all enjoy learning and exploring science.



Shayanki Lahiri, Ph.D.

Shayanki had obtained her bachelor’s and master’s degrees in microbiology from University of Calcutta, India and pursued her PhD from National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India. She had worked as postdoc fellow in University of Free State, South Africa, and Department of Internal Medicine, University of Michigan. Presently, she is utilizing placental samples from Preeclampsia patients to generate the DNA methylation, and single cell RNA sequencing data.


PhD Student

Yijun Li

Yijun Li is a PhD candidate in the department of Biostatistics. She earned a Bachelor degree in Statistics and Mathematics at Duke University and a Masters degree in Biostatistics here in University of Michigan. Yijun’s primary research interest is in spatial transcriptomics, neuroimaging and developing novel methodology that combines traditional statistical methods with deep learning.


Yuheng Du

Yuheng is a PhD student in the DCMB. Yuheng obtained her BS degree in Biology and Computer Science from Brandeis University, and MS degree in Biostatistics from University of Michigan. She is currently working on DNA methylation and single-cell RNA-seq analysis.


Xiaotong Yang

Xiaotong Yang is a PhD student in Computational Medicine and Bioinformatics. She received her dual BS degree in statistics and economics and her MS degree in applied statistics from the University of Michigan, Ann Arbor. She’s currently working with DNA methylation microarray data and electronic health record (EHR) data in preeclamptic pregnancies. Her research interests include building disease prediction models utilizing EHR and multi-omics data.


Thatchayut Unjitwattana

Thatchayut is a PhD student in Biomedical Engineering at the University of Michigan. He obtained his bachelor's degree in Computer Engineering from Chiang Mai University, Thailand. He completed his master's degree in Biomedical Engineering at University of Michigan. His research interests include using machine learning approaches for biomarker discovery and predictive analyses. Besides, He is also interested in integrating spatial transcriptomics data to the deep learning model.


Master Student Research Assistant

Yijun Guo

Yijun Guo is currently a master student in Biostatistics at the University of Michigan. She received her BS degree in Pharmaceutical Sciences from Sun Yat-sen University. Her current research interest is incorporating spatial transcriptomics information into application of tensor cell2cell.


Bowei Li

Bowei Li is currently a master student at New York University. He received his bachelor degree in psychology at the University of California, Irvine. His current research focuses on pan-cancer analysis.


Leyuan Qian

Leyuan Qian is currently a master student in Biostatistics at the University of Michigan. She received her dual bachelor’s degree from Duke Kunshan University and Duke University. Her current research focuses on omics-based biomarker identification in Alzheimer’s disease.


Ziqi Rong

Ziqi is currently a master’s student in Information Science at the University of Michigan. He earned a bachelor’s degree in Electrical and Computer Engineering from Shanghai Jiao Tong University. His research focuses on Unsupervised Learning, single-cell RNA sequencing and DNA Methylation.


Haoran Lu

Haoran Lu is currently a master student of Applied Statistics at University of Michigan. He is currently focusing on spatial multi-omics analysis related to cancer.


Research Specialists

Shu Zhou

Shu has obtained his master’s degree in University of Michigan in Statistics and bachelor’s degree in electronics and computer engineering from Shanghai Jiaotong University. Presently, he is working on the Graph Neural Network to predict gene expression based on spatial transcriptomics data.


Undergraduate Research Assistant

Katherine Dong

Katherine Dong is an undergraduate student in the Department of Biology and Statistics. Her current research is about single-cell RNA-seq analysis.


Haoming Zhu

Haoming is a senior undergraduate student in Biomedical Engineering and Electrical and Computer Engineering. He is interested in the cross-disciplinary applications of computational methods in biomedical problems. He is currently working on the long-term effect of pre-eclampsia based on EHR.


Ziyin Huang

Ziyin is currently an undergraduate student in University of Science and Technology of China, majoring in Biosciences. Her current research focuses on spatial transcriptomics analysis.


Leyang Tao

Leyang is a junior undergraduate student majoring in Biomedical Engineering and Electrical and Computer Engineering. He is interested in spatial transcriptomics in cancer research.



Abdullah Karaaslanli, Ph.D.

Abdullah has gotten his PhD from the department of Electrical and Computer Engineering at Michigan State University. He received his B.S. degree in Electrical and Electronics Engineering from Bogazici University, Istanbul, Turkey. His research focus on graph signal processing and machine learning and application of these fields to single cell data.



Wenting Liu

Wenting Liu is currently a master student in Data Science at Michigan State University. She received her BS degree at Ocean University of China. Her current research focuses on the Cord Blood Methylation of Preeclampsia.


Haodong Liang

Haodong Liang is currently a Master student in Applied Statistics at the University of Michigan. He received his BS degree in Mathematical Statistics at Xiamen University, China. His current research focuses on using spatial transcriptomics data to identify intercellular heterogeneity, and its application in drug repurposing.


Mingyi Tang

Mingyi obtained her BS degree in Data Science at the University of Michigan. She is currently a Master’s student in Data Science Master's Program at the University of Michigan. She is currently working on single-cell RNA-seq analysis.


Shuyue Sheng

Shuyue Sheng is a master student in Applied Statistics in University of Michigan. She received her BS degree in Actuarial Science from the Pennsylvania State University. Her research interest is spatial transcriptomics and cell-cell communication with tensor cell2cell.


Haoyu Huang

Haoyu Huang is currently a master student in Data Science at University of Michigan. He received his BS degree in Electrical & Computer Engineering at Shanghai Jiaotong University. His current research is based on improvement of DeepProg, which is an ensemble of deep-learning and machine-learning models for prognosis using multi-omics data.


Mengtian Zhou

Mengtian Zhou is currently a Master student in Data Science at the University of Michigan. She received her BS degree in Electrical and Computer Engineering from Shanghai Jiao Tong University, China. Her current research focuses on DNA methylation deconvolution model, and its application on placenta, lung and PBMC data.


Bing He, Ph.D.

Bing got his bachelor’s degree from Xi’an Jiaotong University and Ph.D.'s degree from Hong Kong Baptist University. He is a bioinformatics scientist with experiences on single-cells, genomics, transcriptomics, mass spectrometry-based proteomics, protein networks and databases. He specializes in complex diseases and personalized medicine. His current research focuses on single-cell heterogeneity, drug repurposing, combination therapeutics and machine learning.


Stefan Stanojevic, Ph.D.

Stefan obtained his BSc in mathematics and physics from Brandeis University and his PhD in theoretical physics from Brown University. He is interested in machine learning applied to multi-omics data.


Qianhui Huang

Qianhui is a PhD candidate in the DCMB. Qianhui received her dual bachelor’s degree in biology and applied mathematics from the University of Pittsburgh and master’s degree in biostatistics from the University of Michigan. Her research interest involves scRNA-seq analysis, GWAS analysis, DNA methylation analysis and expanding her knowledge in deconvolution problems in omics analysis.


Zhixin Mao

Zhixin Mao received her Bachelor’s degree in mathematics at Rutgers University, and she is getting a Master’s degree in Statistics at University of Michigan. She is currently working with cord blood DNA methylation data and downstream analysis in preeclampsia.


Xingwen Wei

After getting a Bachelor of Science degree of Computer Science from Lafayette College in PA, Xingwen is getting a Master’s degree in Data Science in University of Michigan. He is working on DeepProg webapps for Dr. Garmire.


Shashank Yadav

Shashank is a research associate in the group. He obtained his bachelors and masters degree in Biochemical Engineering and Biotechnology from the Indian Institute of Technology Delhi, New Delhi, India. He is currently working on understanding cell-cell interactions in cancer prognosis.


Cameron Lassiter

Cameron obtained his BS degree in Physiology and Neurobiology from the University of Connecticut, and an MS from John Hopkins University and the University of Maryland, Baltimore in Bioinformatics and Biomedical Sciences, respectively. He has previously worked in both industry and academia on a mixture of pre-clinical and clinical projects; including chemotherapy-induced peripheral neuropathy, lupus, cerebral ataxia, and hepatitis C; involving omics data sets and wet-lab validation. He is interested in bridging the gap between computational and experimental knowledge.


Breck Yunits

Breck obtained his BS degree in Economics from Duke University. He previously worked at Microsoft on cloud computing and did research on programming languages and data visualization. He is interested in machine learning applied to genomics and biomarkers.

Thomas Wolfgruber

Thomas received his BS degree in Computer Engineering from Santa Clara University and PhD in Molecular Biosciences and Bioengineering at the University of Hawaii at Manoa. He has researched the genomics and epigenomics of centromeres, and is interested in making software for research.


Cédric Arisdakessian

Cedric received his Engineering degree from the Ecole Centrale de Lyon (ECL) and specialized in Computer Sciences. In addition, he obtained his MS degree in Biomathematics, Biostatistics, Bioinformatics and Public Health from Lyon I university. His research interests include machine learning approaches for biomarker discovery and predictive analyses using multiomics data.


Xun Zhu

Xun obtained his BS degree in Applied Mathematics from Tianjing Polytechnic University, China, and MS degree in Applied Mathematics from University of Southern California. His research interests include single-cell RNA-seq analysis.


Ryan Schlueter

Ryan Schlueter obtained his BS in biological sciences from the College of William and Mary and his medical degree (D.O.) from the Virginia College of Osteopathic Medicine in Blacksburg, VA. He then completed residency training in Obstetrics and Gynecology at the University of Buffalo, SUNY. Currently he is a fellow at the University of Hawaii in Maternal-Fetal Medicine. Research interests include maternal obesity and preeclampsia, stillbirth, and modifiable risk factors for maternal disease.


Paula Benny

Paula obtained her B.Sc (Hons) in Life Sciences from the National University of Singapore. She also has a M.Sc in Biomedical Sciences and a PhD in Biochemistry. Her research interests include genetics, pathophysiology and cancer.


Fadhl Alakwaa

Fadhl Alakwaa obtained his Master’s degree in Biomedical engineering at Cairo University in Giza, Egypt. He then completed his PhD in the same department, working on extracting biological knowledge from biological databases. His research was mainly on modeling human gene-gene interaction using Bayesian networks and biclustering. His research interests include but are not limited to Genomic Cancer Biology, Bioinformatics, Gene Regulatory Network, Biological Networks, Molecular Biology, and Biotechnology.


Olivier Poirion

Olivier obtained an Engineering degree in Bioprocess Sciences with minor in Bioinformatics from Ecole Nationale Superieure d'Agronomie et des Industries Alimentaires (ENSAIA), France and a Ph.D. in Evolutionary Genomics, delivered by the Ecole Centrale de Lyon (ECL), France. Olivier is interested in machine learning and datamining analyses, applied to genomics.


Sijia Huang

Sijia Huang, PhD (Aug. 2013 - Dec. 2017) is current a postdoc researcher in the group of Prof. Jason Moore, Director of Biomedical Informatics Insitute at U Penn. She obtained her BS degree in Financial Statistics in HuaZhong University of Science and Technology in China, and MS degree in biostatistics from University of Florida. Her PhD thesis was in biomarker classification and genomic/clinic data integration in the context of breast cancers.


Kumardeep Chaudhary

Kumardeep, Postdoc (April 2016- Dec 2017). Kumar is current a senior postdoc in School of Medicine, Mount Sainai. He was a postdoc in Garmire group and relocated for family reason, he did his BSc (Gold Medal) in Zoology (Hons.) from Panjab University, Chandigarh (India) followed by MSc in Systems Biology and Bioinformatics from the same University. He earned his Doctorate from Bioinformatics Centre at Institute of Microbial Technology, Chandigarh (IMTECH-JNU PhD program). His broad interests include: Personalized Medicine, Next Generation Sequencing, Biomaker identification, survival analysis, Bioinformatics, Computational Biology, GWAS and Medical Health. His ultimate goal is to improvise upon translational health for the benefit of mankind using high-throughput data.


Travers Ching

Travers, PhD (2013-2017) is currently a computational biologist in Adaptive Biotechnologies Inc, Seattle. Travers graduated with PhD in May 2017 from Garmire group. He obtained his BS degree in Applied Physics with minor in Biomedical Engineering from Cornell University, and MS degree in microbiology from University of Hawaii at Manoa. He has broad interest in methylation data analysis, RNA-Seq sample size estimation, and integration of RNA-Seq, methylation data in breast cancer and lung cancers.


Michael Ortega

Michael obtained his PhD in Developmental and Reproductive Biology with the Institute for Biogenesis Research at the University of Hawaii while working on replication and DNA damage in gametes and preimplantation embryos. His current research interests include understanding human health and disease by utilizing high-throughput sequencing and multi-omics approaches to investigate critical issues in RNA and cancer biology. He is concentrated in exploring these issues by developing methods for single-cell research and data analysis.


Joshua Chen

Joshua obtained his BS degree in Biomedical Engineering from the University of Miami and is currently working on his MS in Electrical Engineering at the University of Hawaii at Manoa. He plans on attending medical school afterwards. He is currently working on a miRNA project.


Austin Tasato

Austin Tasato majors in Electrical Engineering at University of Hawaii at Manoa. He worked with Xun Zhu on developing Granatum, an scRNA-seq analysis online platform.


Runmin Wei

Runmin obtained his MS degree in Pharmaceutical Science from Shanghai Jiaotong University, China.


Jonathan Uejbe

Jonathan Uejbe is an undergraduate student pursuing his Bachelor's of Science in Electrical Engineering. His interests include new computational methods and algorithms in data analysis. He currently is working on new computational techniques to analyze GWAS data.


Nicole Chong

Nicole Chong is a 3rd year undergraduate student with 4.0 GPA majoring in Biology (with Mathematical Biology Certificate) at University of Hawaii at Manoa. She is working on breast cancer biomarker classification project in collaboration with Sijia Huang, using an integrative metabolomics and transcriptomics approach.


Jeffery Li

Jeffery Li is a sophomore with 4.0 GPA majoring in Biomedical Engineering at John Hopkins University. A Punahou graduate and local Hawaiian, he returns to UH for summer session, and is working on prediction of global gene expression using gene methylation patterns.

James Ha

James Ha is a sophomore with 4.0 GPA majoring in Biology at Caltech. A Punahou graduate and local Hawaiian, he returns to UH for summer session, and is working on global methylation changes of cord blood in pre-eclampsia.

Mark Menor

Information and Computer Science Department, University of Hawaii at Manoa.

Cameron Yee

Undergraduate intern, Neuroscience Major, University of Washington.

Liangqun Lu

Liangqun obtained her BS degree in Biological Sciences and MS degree in Bioinformatics, both from China Agricultural University.





Li YJ, Stanojevic S, He B, Jing Z, Huang Q , Kang J, Garmire LX, Benchmarking Computational Integration Methods for Spatial Transcriptomics Data (preprint)

Liu W, Yang X , Mao Z, Du Y , Lassiter C, AlAkwaa FM, Benny PA, Garmire LX.: Severe preeclampsia is not associated with significant DNA methylation changes but cell proportion changes in the cord blood - caution on the importance of confounding adjustment. (preprint)

Hailey K Ballard, Xiaotong Yang, , Aditya Mahadevan, Dominick J Lemas, Garmire LX.: Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data (preprint)

Xiaotong Yang, , Hailey K Ballard, Aditya D Mahadevan, Ke Xu, David G Garmire, Elizabeth S Langen, Dominick J Lemas, Garmire LX.: Deep learning-based prognosis models accurately predict the time to delivery among preeclamptic pregnancies using electronic health record (preprint)

Haoming Zhu, Xiaotong Yang , Ruowang Li, Lana X Gamire.: Discover overlooked comorbidities of preeclampsia using electronic health records (EHR). (preprint)


105. Chengyi Li,Ryan Clauson, Luke F. Bugada, Fang Ke, Bing He, Zhixin Yu, Hongwei Chen, Binyamin Jacobovitz, Hongxiang Hu, Polina Chuikov, Brett Dallas Hill, Syed M. Rizvi, Yudong Song, Kai Sun, Pasieka Axenov, Daniel Huynh, Xinyi Wang, Lana Garmire, Yu Leo Lei, Irina Grigorova, Fei Wen, Marilia Cascalho, Wei Gao,* and Duxin Sun: Antigen-Clustered Nanovaccine Achieves Long-Term Tumor Remission by Promoting B/ CD 4 T Cell Crosstalk. ACS Nano (link)

104. Lana X. Garmire, Yijun Li, Qianhui Huang, Chuan Xu, Sarah Teichmann, Naftali Kaminski, Matteo Pellegrini, Quan Nguyen, Andrew E. Teschendorff : Challenges and perspectives in computational deconvolution of genomics data. Nature Methods (link)


103. Yadav S, Zhou S, He B, Du Y, Garmire LX. : Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data. Communications Medicine.2023 (link)

102. Al Ghadban Y, Du Y , Charnock-Jones DS, Garmire LX, Smith GCS, Sovio U.: Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study. BJOG.2023 (link)

101. Sperotto F, Gutiérrez-Sacristán A, Makwana S, Li X, Rofeberg VN, Cai T, Bourgeois FT, Omenn GS, Hanauer DA, Sáez C, Bonzel CL, Bucholz E, Dionne A, Elias MD, García-Barrio N, González TG, Issitt RW, Kernan KF, Laird-Gion J, Maidlow SE, Mandl KD, Ahooyi TM, Moraleda C, Morris M, Moshal KL, Pedrera-Jiménez M, Shah MA, South AM, Spiridou A, Taylor DM, Verdy G, Visweswaran S, Wang X, Xia Z, Zachariasse JM; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Newburger JW, Avillach P.: Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium. EClinicalMedicine.2023 (link)

100. Dagliati A, Strasser ZH, Hossein Abad ZS, Klann JG, Wagholikar KB, Mesa R, Visweswaran S, Morris M, Luo Y, Henderson DW, Samayamuthu MJ, Tan BWQ, Verdy G, Omenn GS, Xia Z, Bellazzi R; Consortium for Clinical Characterization of COVID-19 by EHR (4CE),; Murphy SN, Holmes JH, Estiri H; Consortium for Clinical Characterization of COVID-19 by EHR (4CE).: Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study. EClinicalMedicine.2023 (link)

99. Tan BWL, Tan BWQ, Tan ALM, Schriver ER, Gutiérrez-Sacristán A, Das P, Yuan W, Hutch MR, García Barrio N, Pedrera Jimenez M, Abu-El-Rub N, Morris M, Moal B, Verdy G, Cho K, Ho YL, Patel LP, Dagliati A, Neuraz A, Klann JG, South AM, Visweswaran S, Hanauer DA, Maidlow SE, Liu M, Mowery DL, Batugo A, Makoudjou A, Tippmann P, Zöller D, Brat GA, Luo Y, Avillach P, Bellazzi R, Chiovato L, Malovini A, Tibollo V, Samayamuthu MJ, Serrano Balazote P, Xia Z, Loh NHW, Chiudinelli L, Bonzel CL, Hong C, Zhang HG, Weber GM, Kohane IS, Cai T, Omenn GS, Holmes JH, Ngiam KY; Consortium for Clinical Characterization of COVID-19 by EHR (4CE) : Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. eClinicalMedicine.2023 (link)

98. Zhang HG, Honerlaw JP, Maripuri M, Samayamuthu MJ, Beaulieu-Jones BR, Baig HS, L'Yi S, Ho YL, Morris M, Panickan VA, Wang X, Weber GM, Liao KP, Visweswaran S, Tan BWQ, Yuan W, Gehlenborg N, Muralidhar S, Ramoni RB; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Kohane IS, Xia Z, Cho K, Cai T, Brat GA. : Potential pitfalls in the use of real-world data for studying long COVID. Nature Medicine.2023 (link)

97. Bing He, Yao Xiao, Haodong Liang, Qianhui Huang, Yuheng Du, Yijun Li, David Garmire , Duxin Sun, Lana X Garmire : ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs. Nature Communications.2023 (link)


96. Klann JG, Strasser ZH, Hutch MR, Kennedy CJ, Marwaha JS, Morris M, Samayamuthu MJ, Pfaff AC, Estiri H, South AM, Weber GM, Yuan W, Avillach P, Wagholikar KB, Luo Y; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Omenn GS, Visweswaran S, Holmes JH, Xia Z, Brat GA, Murphy SN. : Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study. Jounal of Medical Internet Research.2022 (link)

95. Weber GM, Hong C, Xia Z, Palmer NP, Avillach P, L'Yi S, Keller MS, Murphy SN, Gutiérrez-Sacristán A, Bonzel CL, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Benoit V, Bourgeois FT, Chiovato L, Cho K, Dagliati A, DuVall SL, Barrio NG, Hanauer DA, Ho YL, Holmes JH, Issitt RW, Liu M, Luo Y, Lynch KE, Maidlow SE, Malovini A, Mandl KD, Mao C, Matheny ME, Moore JH, Morris JS, Morris M, Mowery DL, Ngiam KY, Patel LP, Pedrera-Jimenez M, Ramoni RB, Schriver ER, Schubert P, Balazote PS, Spiridou A, Tan ALM, Tan BWL, Tibollo V, Torti C, Trecarichi EM, Wang X; Consortium for Clinical Characterization of COVID-19 by EHR (4CE); Kohane IS, Cai T, Brat GA : International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality. npj Digital Medicine.2022 (link)

94. GTan BWL, Tan BWQ, Tan ALM, Schriver ER, Gutiérrez-Sacristán A, Das P, Yuan W, Hutch MR, García Barrio N, Pedrera Jimenez M, Abu-El-Rub N, Morris M, Moal B, Verdy G, Cho K, Ho YL, Patel LP, Dagliati A, Neuraz A, Klann JG, South AM, Visweswaran S, Hanauer DA, Maidlow SE, Liu M, Mowery DL, Batugo A, Makoudjou A, Tippmann P, Zöller D, Brat GA, Luo Y, Avillach P, Bellazzi R, Chiovato L, Malovini A, Tibollo V, Samayamuthu MJ, Serrano Balazote P, Xia Z, Loh NHW, Chiudinelli L, Bonzel CL, Hong C, Zhang HG, Weber GM, Kohane IS, Cai T, Omenn GS, Holmes JH, Ngiam KY; Consortium for Clinical Characterization of COVID-19 by EHR (4CE). : Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. eClinicalMedicine.2022 (link)

93. Gutiérrez-Sacristán A, Serret-Larmande A, Hutch MR, Sáez C, Aronow BJ, Bhatnagar S, Bonzel CL, Cai T, Devkota B, Hanauer DA, Loh NHW, Luo Y, Moal B, Ahooyi TM, Njoroge WFM, Omenn GS, Sanchez-Pinto LN, South AM, Sperotto F, Tan ALM, Taylor DM, Verdy G, Visweswaran S, Xia Z, Zahner J, Avillach P, Bourgeois FT; Consortium for Clinical Characterization of COVID-19 by EHR (4CE). : Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Network Open.2022 (link)

92. Xiaotong Yang, Paula A Benny, Elorri Cervera-Marzal, Biyu Wu, Cameron B Lassiter, Joshua Astern, Lana X Garmire : Placental telomere length shortening is not associated with severe preeclampsia but the gestational age. Aging (Albany NY).2022 (link)

91. Jintao Xu, Bing He , Kyle Carver ,Debora Vanheyningen, Brian Parkin, Lana X Garmire , Michal A Olszewski, Jane C Deng : Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls. Frontiers in Immunology.2022 (link)

90. Badowski C, He B, Garmire LX : Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty. npj Precision Oncology.2022 (link)

89. Li Y, Stanojevic S, Garmire LX : Emerging artificial intelligence applications in Spatial Transcriptomics analysis. Computational and Structural Biotechnology Journal.2022 (link)

88. Stefan Stanojevic, Li YJ, Garmire LX : Computational Methods for Single-Cell Multi-Omics Integration and Alignment. Accepted. Genomics Proteomics and Bioinformatics. (link)

87. Yile Chen, Bing He, Yu Liu, Max T. Aung, Zaira Rosario-Pabón, Carmen M. Vélez-Vega, Akram Alshawabkeh, José F. Cordero, John D. Meeker, Garmire LX . Maternal plasma lipids are involved in the pathogenesis of preterm birth. Gigascience. (link)

86.Chen VL, Huang Q , Harouaka R, Du Y, Lok AS, Parikh ND, Garmire LX , Wicha MS. A Dual-Filtration System for Single-Cell Sequencing of Circulating Tumor Cells and Clusters in HCC. Hepatology Communications.2022 (link )

85. Vahed M, , Vahed M, Garmire LX . BML: a versatile web server for bipartite motif discovery, Briefings in Bioinformatics. (link )


84. Huang Q, Liu Y, Du Y, Garmire LX; Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data. Genomics Proteomics Bioinformatics . 2021 (link )

83. Roberts JM, Rich-Edwards JW, McElrath TF, Garmire L, , Myatt L; Global Pregnancy Collaboration. Subtypes of Preeclampsia: Recognition and Determining Clinical Usefulness. (link )

82. Bourgeois FT, Gutiérrez-Sacristán A, Keller MS, Liu M, Hong C, Bonzel CL, Tan ALM, Aronow BJ, Boeker M, Booth J, Cruz Rojo J, Devkota B, García Barrio N, Gehlenborg N, Geva A, Hanauer DA, Hutch MR, Issitt RW, Klann JG, Luo Y, Mandl KD, Mao C, Moal B, Moshal KL, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Patel LP, Jiménez MP, Sebire NJ, Balazote PS, Serret-Larmande A, South AM, Spiridou A, Taylor DM, Tippmann P, Visweswaran S, Weber GM, Kohane IS, Cai T, Avillach P; Consortium for Clinical Characterization of COVID-19 by EHR (4CE). International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Network Open. 2021 (link )

81. Engström K, Mandakh Y, Garmire L , Masoumi Z, Isaxon C, Malmqvist E, Erlandsson L, Hansson SR. Early Pregnancy Exposure to Ambient Air Pollution among Late-Onset Preeclamptic Cases Is Associated with Placental DNA Hypomethylation of Specific Genes and Slower Placental Maturation. Toxics. 2021. 9(12):338. (link )

80. Weber GM,..., Consortium For Clinical Characterization Of COVID-19 By EHR (4CE), Kohane IS, Cai T, South AM, Brat GA. International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res. 2021. (link )

79. Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan ALM, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS; Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Visweswaran S, Mowery DL, Xia Z. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19. Sci Rep. 2021. (link )

78. Estiri H, Strasser ZH, Brat GA, Semenov YR; Consortium for Characterization of COVID-19 by EHR (4CE), Patel CJ, Murphy SN. Evolving phenotypes of non-hospitalized patients that indicate long COVID. BMC Med. 2021 (link )

77. He B , Liu Y, , Maurya MR, Benny P, Lassiter C, Li H , Subraminiam S, Garmire LX . The maternal blood lipidome is indicative of the pathogenesis of severe preeclampsia. J Lipid Res . 2021 Sep 18;100118. (link)

76. Garmire D, Zhu X, Mantravadi A, Huang Q, Yunits B, Liu Y, Wolfgruber T, Poirion O, Zhao T, Arisdakessian C, Stanojevic S, Garmire LX . GranatumX: A community engaging, modularized and flexible software environment for single-cell analysis. Proteomics, Genomics and Bioinformatics( link )

75. Piorion O, Chaudhary K, Huang S, Garmire LX , DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. 2021 ( link )

74. Cherry C, Maestas DR, Han J, ..., Garmire LX , Elisseeff JH, Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics. 2021 ( link )

73. Paula A Benny, Fadhl M. Al-Akwaa , Corbin Dirkx, Ryan J. Schlueter, Thomas K. Wolfgruber, Ingrid Y. Chern , Suzie Hoops, Dan Knights, Lana X. Garmire . Placentas delivered by pre-pregnant obese women have reduced abundance and diversity in the microbiome. FASEB. ( link)

72. Zhucheng Zhan, Zheng Jing, Bing He , Noshad Hossenei, Maria Westerhoff, Eun-Young Choi, Lana Garmire . Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data (link)

71. Alakwaa F, Garmire LX , Savelieff MG. Correction to "Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer". J Proteome Res . ( link )

70. Wang Di, He Kevin, Garmire LX . Cox-nnet v2.0: improved neural-network based survival prediction extended to large-scale EMR dataset. Bioinformatics. (link)

69. Fang X, Liu Y, Ren Z, Du Y, Huang Q, Garmire LX. Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data. Gigascience. 10(1):giaa162. doi: 10.1093/gigascience/giaa162. ( link )

68. Garmire LX . Mentorship is not co-authorship: a revisit to mentorship. Genome Biology. 22(1):2. ( link )


67. Brito JJ, Li J, Moore JH, Greene CS, Nogoy NA, $Garmire LX , $Mangul S.Corrigendum to: Recommendations to enhance rigor and reproducibility in biomedical research. Gigascience. 2020. ( link )

66. Li H, Huang SJ, Liu Y, Garmire LX , Single Cell Transcriptome Research in Human Placenta, Reproduction. (link)

65. He Bing, Garmire LX . Prediction of repurposed drugs for treating lung injury in COVID-19. F1000 Research. 9:609 ( link )

64. Huang QH, Liu Y, Du Y, Garmire LX , Evaluation of cell type deconvolution R packages on single cell RNA-seq data, accepted, Genomics, Proteomics and Bioinformatics. (preprint)

63. Brito JJ, Li J, Moore JH, Greene CS, Nogoy NA, $Garmire LX , $Mangul S. Recommendations to enhance rigor and reproducibility in biomedical research. Gigascience. 2020. 9(6) ( link )

62. Du Y, Huang Q, Arisdakessian C, Garmire L , evaluation of STAR and Kallisto aligners on single cell RNA-Seq data, G3. ( link )

61. Schlueter, RJ, Al-Akwaa FM, Benny PA, Gurary A, Xie G, Jia W, Chun X, Chern I, Garmire L, Metabolomics profile of cord blood is associated with maternal pre-pregnant obesity in a multi-ethnic cohort, Journal of Proteome Research. 19(4):1361-1374 ( link )

60. Chen B, Garmire LX , Calvisi BF,...,Chen X, Harnessing Big 'Omics' Data and AI for Drug Discovery in Hepatocellular Carcinoma, Nature Reviews. Gastroenterology & Hepatology. 17(4):238-251 ( link )

59. Benny PA, Alakwaa FM, Schlueter RJ, Lassiter CB, Garmire LX . A review of omics approaches to study preeclampsia. Placenta. 2020. 92:17-27. ( link )

58.Liu QZ, Ha MJ, Bhattacharyya R, Garmire L , Baladandayuthapani V, Network-Based Matching of Patients and Targeted Therapies for Precision Oncology. Pacific Symposium on Biocomputing 2020 ( link )


57. Garmire L , From Hawaii to PECASE, tips of success from a female bioinformatician. Genome Biology.20(1):271. ( link )

56. Garmire LX , Guo-cheng Yuan, Rong Fan, Gene Yeo, John Quackenbush, Single Cell Analysis, what is in the future? ( link )

55. J Olender, BD Wang, K Nguyen, T Ching , C Samtal, Y JI, J Rim, L Garmire , P Latham, NH Lee, Identification and cloning of a novel FGF3 splice variant involved in Arfican American prostate cancer disparities, Molecular Cancer Research, ( link )

54. Zhu X, Garmire LX , Book chapters: chapter 19: Data analysis in Single Cell Omics. Single-cell Omics, Vol. 1 (Elsevier)

53. The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution, Dev. Cell, 2019 ( link )

52. Benny P, Yamasato K, ..., Ching T, Garmire LX, Berry M, Towner D, Evaluation of a maternal cardiovascular gene array in early on-set preeclampia in a dominantly Asian cohort. PLoS One. ( link )

51. Arisdakessian C, Poirion O, Yunits B, Zhu X, Garmire LX . DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-Seq data. Genome Biology. 20(1):211. ( link)


50. Stein-O' Brien, Arora R, Culhane AC, Favorov A, Garmire LX , Greene C, Goff LA, Li Y, Ngom A, Yanxun Xu Y, Fertig EJ. Entering the matrix: factorization uncovers knowledge from omics. Trands in Genetics. 34(10):790-805. ( link)

49. Alakwaa F, Huang S, Yunits B and Garmire LX . Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data. Gigascience. 7(12). ( link)

48. Ching T, Zhu X, Garmire LX. Cox-nnet: an artificial neural network Cox regression for prognosis prediction, PLoS Comp Biol. ( link )

47. Chaudhary K, Lu L, Poirion O, Garmire LX , Multi-modal meta-analysis of 1494 hepatocellular carcinoma samples reveals vast impacts of consensus driver genes on phenotypes. Clinical Cancer Research (IF=10.2) ( link)

46. Poirion O, Zhu X, Ching T, Garmire L Using Single Nucleotide Variations of Single-Cell RNA-Seq to identify tumor Subpopulation and Genotype-phenotype Links. Nature Communications. 20;9(1):4892. ( link)

45. Poirion O, Chaudhary K, Garmire LX , Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:197-206


44. Ortega M, Poirion O, Zhu X, Huang SJ , Sebra R, Garmire LX , Using Single-Cell Multiple Omics Approaches to Resolve Tumor Heterogeneity. Clinical and Translational Medicine (Springer). 2017. 6(1):46


43. Ching T, Garmire LX , Pan-cancer analysis of expressed single nucleotide variants in long inter-genic non-coding RNA. Pac Symp Biocomput. 2018;23:512-523.

(link )

42. Al-Akwaa F, Chaudhary K, Garmire LX , Deep learning predicts estrogen receptor status in breast cancer metabolomics data. Journal of Proteome Research. (link)

41. Huang SJ, Chaudhary K, Garmire LX, More is better: recent progress in multi-omics data integration, accepted, Frontiers in Genetics. (link)

40. Zhu X, Wolfgruber T, Tasato A, Arisdakessian C, Garmie D, Garmire LX, Granatum: A graphical single cell RNA-Seq analysis pipeline for genomics scientists, Genome Medicine, 9(1):108. ( link)

39. Chaudhary K, Poirion O, Lu L, Garmire LX, Deep Learning based multi-omics integration robustly predicts survivals in liver cancer. Clinical Cancer Research ( link)

38. Wang BD, Ceniccola K, Hwang S, Andrawis R, Horvath A, Freeman JA, Knapp S, Ching T, Garmire LX, Pate lV, Garcia-Blanco MA, Patierno SR, Lee NH, Aberrant Alternative Splicing in African American Prostate Cancer: novel driver of tumor aggressiveness and drug resistance, accepted, Nature Communications.

37. Han B, Park HK, Wang H, Panneerselvam J, Shen Y, Zhang J, Li L, Lee YH, Su M, Ching T, Garmire LX , Jia W, Yu H, Fei P, HDBR1 Modulates U2 snRNP Function to Maintain RNA Populations, Contributing to the Suppression of Human Cancer Development, accepted, Oncogene.

36. Greene CS, Garmire LX , Gilbert JA, Ritchie DR, Hunt L, Celebrate parasites, accepted, Nature Genetics. ( link)

35. Feng N, Wang Y, Zheng M, Yu X, Lin H, Ma RN, Shi O, Zheng X, Gao M, Yu H, Garmire L, Qian B. Genome-wide analysis of DNA methylation and their associations with long noncoding RNA/mRNA expression in non-small-cell lung cancer. Accepted, Epigenomics. ( link)


34. Yang J, Tanaka Y, Seay M, Li Z, Jin JQ, Garmire L, Zhu X , Euskirchen G, Synder M, Li W, Park IH, Pan X, Weissman SM. Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Research (2016). ( link)

33. Zhu X, Ching T, Pan X, Weissman S, Garmire LX. Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization, PeerJ 5:e2888 ( link)

32. Garmire LX, Gliske S, Nguyen QC, Chen JH, Nemati S, VAN Horn JD, Moore JH, Shreffler C, Dunn M. The training of next generation data scientists in biomedicine. Pac Symp Biocomput. 2016;22:640-645. ( link)

31. Feng N§, Ching T§, Wang Y, Liu B, Lin H,Shi O,Zhang X, Yao Y, Hua L, Zheng X, Gao M, Yu H#, Garmire LX #, Qian B#. Analysis of Microarray Data on Gene Expression and Methylation to Identify Long Non-coding RNAs in Non-small Cell Lung Cancer. Scientific Reports 6 (2016). (#: co-corresponding authors) ( link)

30. Lu L, McCurdy S, Huang S, Zhu X, Peplowska K, Tiirikainen M, Boisvert WA, Garmire LX, Time Series miRNA-mRNA integrated analysis reveals critical miRNAs and targets in macrophage polarization. Scientific Reports 6 (2016). ( link)

29. Poirion A§, Zhu X§, Ching T, Garmire LX Single-Cell Transcriptomics Bioinformatics and Computational Challenges,7:163.Frontiers in Genetics (2016). ( link)

28. Huang S*, Kou L*, Furuya H, Yu CH, Kattan M, Goodison S, Garmire LX, Rosser CJ, A nomogram derived by combination of demographic and biomarker data improves the non-invasive evaluation of patients at risk for bladder cancer, accepted, Cancer Epidemiology, Biomarkers and Prevention, 2016 Jul 6. pii: cebp.0260.


27. Wei R, De Vivo I, Huang S, Zhu X, Risch, H, Moore JH, Yu H, Garmire LX, Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer, Oncotarget, 2016 Jul 9. doi: 10.18632/oncotarget.10509. ( link)

26. Ching T, Peplowska K, Huang S, Zhu X , Shen Y, Molnar J, Yu H, Tiirikainen M, Fogelgren B, Fan R, Garmire LX . Pan-cancer analyses reveal a panel of biologically and clinically relevant lincRNAs for tumour diagnosis, subtyping and prognosis, 2016, 7:62-72, EBioMedicine. ( link)

25. Huang S, Chong N, Lewis NE, Jia W, Xie G, Garmire LX. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis, 8(1):34, Genome Medicine (2016). ( link)


24. Ching T, Masaki J, Weirather J, Garmire LX . Non-coding yet non-trivial: a review on the genomics of long intergenic non-coding RNAs, 8:44. doi: 10.1186/s13040-015-0075-z, BioData Mining. ( link)

23. Xie G, Zhou B, Zhao Y, Qiu Y, Zhao X, Garmire LX , Yu H, Yen Y, Jia W, Lowered circulating aspartate is a metabolic feature of human breast cancer,Oncotarget, 6 (32), 33369-81. ( link)

22. Li J, Ching T, Huang S, Garmire LX, Using Epigenomics Data to Predict Differential Gene Expression in Lung Cancer, BMC Bioinformatics. 2015;16 Suppl 5:S10 ( link)

21. Ching T, Ha J,Song MA, Tiirikainen M, Molnar J Berry M, Towner D, Garmire LX, Genome-scale hypomethylation in the cord blood cells associated with early onset preeclampsia, Clinical Epigenetics. 2015 Mar 13;7(1):21. ( link)

20. Gagliani N, Iseppon A, Vesely CA, Brockmann L, Palm NW, Zeote MR, Licona-Limon P, Paiva R, Ching T, Zi X, Fan R, Garmire L, Geginat J, Stockinger B, Esplugues E, Huber S, Flavell R, Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation, Nature. 2015 Apr 29. doi: 10.1038/nature14452. ( link)


19. Menor M, Ching T, Garmire D, Zhu X, Garmire LX, mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome Biology. Oct 25;15(10):500. ( link)

18. Han L, Zi XY, Garmire LX, Pan XH, Weissman SM, Fan R, Co-detection and sequencing of genes and transcripts from the same single cells enabled by a microfluidics platform, Sci Rep. Sep 26;4:6485. doi: 10.1038/srep06485. ( link)

17. Ching T, Huang S, Garmire LX: power analysis and sample size estimation for RNA-Seq differential expression, RNA. 2014 Sep 22. [Epub ahead of print]. ( link)

16. Huang S, Yee C, Ching T, Yu H, Garmire LX, A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer, PLOS Computational Biology. Sep 18;10(9):e1003851. ( link)

15. Ching T,Song MA, Tiirikainen M, Berry M, Towner D, Garmire LX, Global hypermehtylation coupled with promoter hypomethylation in the chorioamniotic membranes of early onset preeclampsia. Mol. Hum. Reprod. Sep;20(9):885-904. ( link)


14. Garmire LX, Subramaniam S. The poor performance of TMM on microRNA-Seq. RNA 19, 735-6 (2013). PMCID: PMC3683907. ( link)

13. Hadd AG, Houghton J, Choudhary A, Sah S, Chen L, Marko AC, Sanford T, Buddavarapu K, Krosting J, Garmire LX, Wylie D, Shinde R, Beaudenon S, Alexander EK, Mambo E, Adai AT, Latham GJ. Targeted, high-depth, next-generation sequencing of cancer genes in formalin-fixed, paraffin-embedded and fine-needle aspiration tumor specimens. J Mol Diagn 15, 234-47 (2013). ( link)

Pre-Garmire Group time. 2012 and earlier

12. Colas A, McKeithan W, Cunningham T, Bushway P, Garmire LX, Duester G, Subramaniam S, Mercola M, Whole genome microRNA screening identifies let-7 And mir-18 as regulators of germ layer formation during early embryogenesis, Genes & Development 26, 2567-79 (2012).PMCID: PMC3521625. ( link)

11. Wu Y, Garmire LX, Fan R, Dynamic analysis of intercellular signaling reveals a mechanistic transition in tumor microenvironment, Integrative Biology,(Camb) 4, 1478-86 (2012).PMCID: PMC3502715. ( link)

10. Nathan S*, Garmire LX*, McDonald J, Norihito S, Reichart D, Heudobler D, Raetz CR, Murphy RC, Merril AH, Brown A, Dennis EA, Li AC, Fahy E, Subramaniam S, Quehenberger O, Russell DW, and Glass CK, Regulated accumulation of desmosterol integrates macrophage lipid metabolism and inflammatory responses. Cell 151, 138-52 (2012) (*: equal contribution). PMCID: PMC3464914. ( link)

9. Garmire LX, Subramaniam S. Evaluation of microRNA-Seq normalization methods. RNA 18, 1279-1288 (2012). PMCID: PMC3358649. ( link)

8. Garmire LX, Garmire DG, Huang W, Yao J, Glass CK, Subramaniam S. A global clustering approach to identify intergenic non-coding RNA, with application in mouse macrophages. PLoS ONE 6(9):e24051 (2011). PMCID: PMC3184070. ( link)

7. Wang KC, Garmire LX, Young A, Nguyen P, Trinh A, Subramaniam S, Wang NP, Shyy J, Li J, Chien S, Role of miR-23b in flow-regulation of microRNA signature and cell growth in endothelial Cells, Proc Natl Acad Sci U S A 107, 3234-9 (2010). PMCID: PMC2840325. ( link)

6. Garmire LX, Shen ZX, Briggs S, Yeo G, Glass CK, Subramaniam S, Regulatory Network of microRNAs in RAW 264.7 Macrophage Cells. Proceedings of 32nd International Conference of the IEEE Eng Med Biol Soc 6198-201 (2010). ( link)

5. Garmire LX, Hunt CA, In silico methods for unraveling the mechanistic complexities of intestinal absorption: metabolism-efflux transport interactions. Drug Metab Dispos 36,1414-24 (2008). PMC, in process. ( link)

4. Garmire LX, Garmire DG, Hunt CA, An in silico transwell device for drug transport and drug-drug interaction studies. Pharmaceutical Research 24, 2171-86 (2007). Featured Article. ( link)

3. Garmire LX, Mechanistic study of enzyme-efflux transporter relations using in silico devices. Lecture Notes in Engineering and Computer Science 2167, 34-39 (2007). ( link)

2. Grant MR, Hunt CA, Xia L*, Fata JM, Bissell MJ, Modeling mammary gland morphogenesis as a reaction-diffusion process, Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA September 1-5 (2004) (*: Maiden name). ( link)

1. Fan T, Xia L*, Han Y. Mitochondrion and apoptosis. Acta Biochica et Biophysica Sinica 33, 7-12 (2001) (*: Maiden name). ( link)


  • PE-prognosis-predictor - Interactive web app to predict risk of delivery of preeclampsia patients
  • cox-nnet - predict patient prognosis by extending Cox Regression to the non-linear neural network framework
  • SSrGE - Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Tumor Subpopulations and Genotype-phenotype Linkage
  • Lilikoi - an R package for personalized pathway-based classification modeling using metabolomics data
  • Granatum - a single-cell RNA-Seq analysis platform for bench scientists
  • GranatumX - a community engaging, flexible computing environment for single-cell RNA-Seq analysis platform connecting tool developers with bench scientists
  • NMFEM - cluster scRNA-Seq samples using NMF, extract important genes, and generate PPI network modules from these genes
  • mirMark - random forest based machine learning method to predict the microRNA targets both at site-level and UTR-level
  • RNASeqPowerCalculator - R code to calculate the power and sample size for RNA-Seq differential expression
  • MetaboloPathwayModel - R code to build pathway-based metabolomics diagnosis models