Postdoc and PhD student candidates: Garmire Group is recruiting competent and motivated postdoc and PhD students who are passionate to cure cancers with the computational biology approach. Previous rigorous training in quantative sciences is required.
Lana Garmire

Lana Garmire, Ph.D. (CV)
Associate Professor (with tenure)
Cancer Epidemiology Program
University of Hawaii Cancer Center

Graduate Faculty
Program of Molecular Biosciences and Bioengineering
College of Tropical Agriculture and Human Resources

Graduate Faculty
Program of Public Health Studies
University of Hawaii

Faculty Member
Institute of Biogenesis Research
University of Hawaii

Adjunct Faculty
Department of Biochemistry, Physiology and Anatomy
John A. Burns School of Medicine
University of Hawaii

Adjunct Faculty
Department of Ob/Gyn and Women's Health
John A. Burns School of Medicine
University of Hawaii

Since September 2012, Dr. Lana Garmire has rapidly risen from a tenure track faculty in translational bioinformatics to a tenured Associate Professor leading a multidisciplinary team of computational and experimental human genomics. Garmire has won numerous competitive federal grant awards of over 7.8 million dollars as the PI, including NIH/NIGMS P20 COBRE (2014-2019), NIH/BD2K K01 award (2014-2019), and two concurrent NIH R01 grant awards from NICHD (2016-2021) and NLM (2016-2020). Her annual direct expenditure is over 1 million per year.

Dr. Garmire obtained the MA degree in Statistics (2005) and PhD degree in Comparative Biochemistry (Computational Biology focus, 2007), both from UC-Berkeley. She then did her postdoctoral training (2008-2011) under the joint mentorship of Prof. Shankar Subramaniam in the Bioengineering Dept. and Prof. Christopher Glass in the Department of Cellular and Molecular Medicine, UC-San Diego. In 2012, she won the first bioinformatics NIH SBIR grant for Asuragen Inc (a spin-off of Ambion, the RNA company), and then resumed the tenure-track faculty position in the University of Hawaii Cancer Center since September 2012. Dr. Garmire collaborates with a variety of top researchers nationally and internationally. She has published over 40 papers in high quality journals including Cell and Nature. She has mentored over 30 MD fellows, postdocs, graduate students and undergraduates of various academic backgrounds, in Biology, Mathematics, Phyiscs, (bio)Statistics, Bioengineering, Computer Science and Electrical Engineering. She is an Associate Editor of BMC Bioinformatics and Guest Editor of PLoS Computational Biology.

Research Interests

The major research interests of Garmire Group
Translational bioinformatics of cancers and immune-related diseases, such as classifying biomarkers for cancer prognosis and diagnosis
Use high-throughput methods (next-generation sequencing method etc.) to study non-coding RNAs, such as microRNAs and intergenic non-coding RNAs
Integrative omics/clinic data analysis
single-cell sequencing and bioinformatics
Develop computational methods to analyze high-throughput data from next-generation sequencing et al.
Pregnancy adversities and fetal orgin of cancers, public health genomics

Established Collaborators

Major established collaborators
Yale University:
Dr. Sherman Weissman (Sterling Professor and a member of National Academy of Sciences)
Dr. Rong Fan (Associate Professor of Biomedical Engineering)
Dr. Richard Flavell (Sterling Professor, a member of National Academy of Sciences, and Chair of Immunology Department)
University of Pennsyvinia:
Dr. Jason Moore (Founding Director of Institute for Biomedical Informatics and an AAAS member)
University of Hawaii School of Medicine:
Dr. William Boisvert
Dr. Dena Towner (OBGYN and Associate Director of Clinical Affairs)
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 Hawaii Cancer Center:
Dr. Wei Jia (Associate Director of Shared Resources)
Dr. Herbert Yu (Director of Epidemiology Program)
Dr. Charles Rosser (Director of Clinical and Translational Research Program)

Establishing Collaborators

Other establishing collaborators (with active projects and/or papers to be submitted together)
Yale University:
Dr. Shangqin Guo (Assistant Professor of Yale Stem Cell Center)
University of Hawaii:
Drs. Linda Chang and Thomas Ernest at Neuroscience and MRI Program.
Dr. Jason Leigh, Department of Information and Computer Science.



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.


Kumardeep Chaudhary

Kumardeep 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.


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.


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.


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.


Graduate Students

Sijia Huang

Sijia 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. She is interested in biomarker classification and genomic/clinic data integration in the context of breast cancers.


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. He is currently working on single-cell RNA-seq analysis.


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.


Software Engineer

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.


Industrial Consultant

Mike Kubal

Michael earned his MS in Computer Science from the University of Chicago. He enjoys working at the interface of biology and software with commercial and academic teams on a wide variety of bioinformatics projects. His passion is finding creative solutions that leverage machine learning and immersive technologies.



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.





50. Schlueter, RJ, Al-Akwaa FM, Benny PA, Gurary A, Xie G, Jia W, Chun X, Chern I, Garmire L, Metabolomics profile of umbilical cord blood is associated with maternal pre-pregnant obesity in a prospective multi-ethnic cohort displaying health disparities, submitted. preprint)

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

48. Chaudhary K, Lu L, Poirion O, Garmire LX , Phenotypic associations of consesus driver mutations in hepatocellular carcinoma. in revision. ( preprint)

47. 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, submitted.

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. in revision. ( preprint)


45. Poirion O, Chaudhary K, Garmire LX , Deep Learning data integration for better risk stratification models of bladder cancer. Accepted. AMIA joint summit meeting, 2018

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

43. Ching T, Garmire LX , Pan-cancer analysis of expressed single nucleotide variants in long inter-genic non-coding RNA. Accepted. PSB 2018

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.


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)


  • 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