Scientific Publications:
ORCID iD iconORCID / Google Scholar
  1. Fang LT. Powering toxicogenomic studies by applying machine learning to genomic sequencing and variant detection. In: Hong H, editor. Machine Learning and Deep Learning in Computational Toxicology. 2023;611-627. DOI:10.1007/978-3-031-20730-3_27

  2. Talsania K, Shen T, Chen X, Jaeger E, Li Z, Chen Z, Chen W, Tran B, Kusko R, Wang L, et al. Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies. Genome Biology. 2022;23(1):255. DOI:10.1186/s13059-022-02816-6 / PMID:36514120

  3. Xiao C, Chen Z, Chen W, Padilla C, Colgan M, Wu W, Fang LT, Liu T, Yang Y, Schneider V, et al. Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples. Genome Biology. 2022;23(1):237. DOI:10.1186/s13059-022-02803-x / PMID:36352452

  4. Olson ND, Wagner J, McDaniel J, Stephens SH, Westreich ST, Prasanna AG, Johanson E, Boja E, Maier EJ, Serang O, et al. PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions. Cell Genomics. 2022;2(5):100129. DOI:10.1016/j.xgen.2022.100129 / PMID:35720974

  5. Yao L, Rey DA, Bulgarelli L, Kast R, Osborn J, Van Ark E, Fang LT, Lau B, Lam H, Teixeira LM, et al. Gene expression scoring of immune activity levels for precision use of hydrocortisone in vasodilatory shock. Shock. 2022;57(3):384-391. DOI:10.1097/SHK.0000000000001910 / PMID:35081076

  6. Sahraeian SME, Fang LT, Karagiannis K, Moos M, Smith S, Santana-Quintero L, Xiao C, Colgan M, Hong H, Mohiyuddin M, et al. Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample. Genome Biology. 2022;23(1):12. DOI:10.1186/s13059-021-02592-9 / PMID:34996510

  7. Fang LT, Zhu B, Zhao Y, Chen W, Yang Z, Kerrigan L, Langenbach K, de Mars M, Lu C, Idler K, et al. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nature Biotechnology. 2021;39(9):1151-1160. DOI:10.1038/s41587-021-00993-6 / PMID:34504347 / SharedIt Link

  8. Xiao W, Ren L, Chen Z, Fang LT, Zhao Y, Lack J, Guan M, Zhu B, Jaeger E, Kerrigan L, et al. Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing. Nature Biotechnology. 2021;39(9):1141-1150. DOI:10.1038/s41587-021-00994-5 / PMID:34504346 / SharedIt Link

  9. Zhao Y, Fang LT, Shen T-W, Choudhari S, Talsania K, Chen X, Shetty J, Kriga Y, Tran B, Zhu B, et al. Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study. Scientific Data. 2021;8(1):296. DOI:10.1038/s41597-021-01077-5 / PMID:34753956

  10. Fang LT. SomaticSeq: an ensemble and machine learning method to detect somatic mutations. In: Boegel S. (eds) Bioinformatics for Cancer Immunotherapy. Methods in Molecular Biology. 2020;2120(1):47-70. DOI:10.1007/978-1-0716-0327-7_4 / PMID:32124311

  11. Jiang J, Adams H-P, Yao L, Yaung S, Lal P, Balasubramanyam A, Fuhlbruck F, Tikoo N, Lovejoy AF, Froehler S, Fang LT, et al. Concordance of genomic alterations by next-generation sequencing in tumor tissue versus cell-free DNA in stage I-IV non-small cell lung cancer. The Journal of Molecular Diagnostics. 2020;22(2):228-235. DOI:10.1016/j.jmoldx.2019.10.013 / PMID:31837429

  12. Mendez P, Fang LT, Jablons DM, Kim I-J. Systematic comparison of two whole-genome amplification methods for targeted next-generation sequencing using frozen and FFPE normal and cancer tissues. Scientific Reports. 2017;7(1):4055. DOI:10.1038/s41598-017-04419-9 / PMID:28642587

  13. Lau B, Mohiyuddin M, Mu JC, Fang LT, Bani Asadi N, Dallett C, Lam HYK. LongISLND: in silico sequencing of lengthy and noisy datatypes. Bioinformatics. 2016;32(24):3829-3832. DOI:10.1093/bioinformatics/btw602 / PMID:27667791

  14. Kang HC, Kim HK, Lee S, Mendez P, Kim JW, Woodard G, Yoon J-H, Jen K-Y, Fang LT, Jones K, et al. Whole exome and targeted deep sequencing identify genome-wide allelic loss and frequent SETDB1 mutations in malignant pleural mesotheliomas. Oncotarget. 2016;7(7):8321-8331. DOI:10.18632/oncotarget.7032 / PMID:26824986

  15. Fang LT, Afshar PT, Chhibber A, Mohiyuddin M, Fan Y, Mu JC, Gibeling G, Barr S, Bani Asadi N, Gerstein MB, et al. An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome Biology. 2015;16(1):197. DOI:10.1186/s13059-015-0758-2 / PMID:26381235

  16. Fang LT, Lee S, Choi H, Kim HK, Jew G, Kang HC, Chen L, Jablons D, Kim I-J. Comprehensive genomic analyses of a metastatic colon cancer to the lung by whole exome sequencing and gene expression analysis. International Journal of Oncology. 2014;44(1):211-221. DOI:10.3892/ijo.2013.2150 / PMID:24172857

  17. Zhang S, Yang Y-L, Wang Y, You B, Dai Y, Chan G, Hsieh D, Kim I-J, Fang LT, Au A, et al. CK2α, over-expressed in human malignant pleural mesothelioma, regulates the hedgehog signaling pathway in mesothelioma cells. Journal of Experimental & Clinical Cancer Research. 2014;33(1):93. DOI:10.1186/s13046-014-0093-6 / PMID:25422081

  18. Yang Y-L, Hung M-S, Wang Y, Ni J, Mao J-H, Hsieh D, Au A, Kumar A, Quigley D, Fang LT, et al. Lung tumorigenesis in a conditional Cul4A transgenic mouse model. The Journal of Pathology. 2014;233(2):113-123. DOI:10.1002/path.4352 / PMID:24648314

  19. Bosco-Clement G, Zhang F, Chen Z, Zhou H-M, Li H, Mikami I, Hirata T, Yagui-Beltran A, Lui N, Do HT, Cheng T, Tseng H-H, Choi H, Fang LT, et al. Targeting Gli transcription activation by small molecule suppresses tumor growth. Oncogene. 2014;33(16):2087-2097. DOI:10.1038/onc.2013.164 / PMID:23686308

  20. Mulvihill MS, Kwon Y-W, Lee S, Fang LT, Choi H, Ray R, Kang HC, Mao J-H, Jablons D, Kim I-J. Gremlin is overexpressed in lung adenocarcinoma and increases cell growth and proliferation in normal lung cells. PLoS ONE. 2012;7(8):e42264. DOI:10.1371/journal.pone.0042264 / PMID:22870311

  21. Kim JWS, Lee S, Lui N, Choi H, Mulvihill M, Fang LT, Kang HC, Kwon Y-W, Jablons D, Kim I-J. A somatic TSHR mutation in a patient with lung adenocarcinoma with bronchioloalveolar carcinoma, coronary artery disease and severe chronic obstructive pulmonary disease. Oncology Reports. 2012;28(4):1225-1230. DOI:10.3892/or.2012.1938 / PMID:22842620

  22. Fang LT, Gelbart WM, Ben-Shaul A. The size of RNA as an ideal branched polymer. The Journal of Chemical Physics. 2011;135(15):155105. DOI:10.1063/1.3652763 / PMID:22029339

  23. Fang LT. The end-to-end distance of RNA as a randomly self-paired polymer. Journal of Theoretical Biology. 2011;280(1):101-107. DOI:10.1016/j.jtbi.2011.04.010 / PMID:21515288

  24. Fang LT, Yoffe AM, Gelbart WM, Ben-Shaul A. A sequential folding model predicts length-independent secondary structure properties of long ssRNA. The Journal of Physical Chemistry B. 2011;115(12):3193-3199. DOI:10.1021/jp110680e / PMID:21370842

  25. Qiu X, Rau DC, Parsegian VA, Fang LT, Knobler CM, Gelbart WM. Salt-dependent DNA-DNA spacings in intact bacteriophage λ reflect relative importance of DNA self-repulsion and bending energies. Physical Review Letters. 2011;106(2):028102. DOI:10.1103/PhysRevLett.106.028102 / PMID:21405253

  26. Prinsen P, Fang LT, Yoffe AM, Knobler CM, Gelbart WM. The force acting on a polymer partially confined in a tube. The Journal of Physical Chemistry B. 2009;113(12):3873-3879. DOI:10.1021/jp808047u / PMID:19296704

  27. Evilevitch A, Fang LT, Yoffe AM, Castelnovo M, Rau DC, Parsegian VA, Gelbart WM, Knobler CM. Effects of salt concentrations and bending energy on the extent of ejection of phage genomes. Biophysical Journal. 2008;94(3):1110-1120. DOI:10.1529/biophysj.107.115345 / PMID:17890396

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