Publications

2020

  1. Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, Dentro S, Wintersinger JA, Liu LY, Ko M, Sivanandan S, Zhang H, Zhu K, Ou Yang TH, Chilton JM, Buchanan A, Lalansingh CM, P'ng C, Anghel CV, Umar I, Lo B, Zou W; DREAM SMC-Het Participants, Simpson JT, Stuart JM, Anastassiou D, Guan Y, Ewing AD, Ellrott K, Wedge DC, Morris Q, Van Loo P, Boutros PC. 2020. A community effort to create standards for evaluating tumor subclonal reconstruction. Nature Biotechnology. Jan;38(1):97-107. doi: 10.1038/s41587-019-0364-z. Epub 2020 Jan 9.
  2. Mason MJ, Schinke C, Eng CLP, Towfic F, Gruber F, Dervan A, White BS, Pratapa A, Guan Y, Chen H, Cui Y, Li B, Yu T, Chaibub Neto E, Mavrommatis K, Ortiz M, Lyzogubov V, Bisht K, Dai HY, Schmitz F, Flynt E, Dan Rozelle, Danziger SA, Ratushny A; Multiple Myeloma DREAM Consortium, Dalton WS, Goldschmidt H, Avet-Loiseau H, Samur M, Hayete B, Sonneveld P, Shain KH, Munshi N, Auclair D, Hose D, Morgan G, Trotter M, Bassett D, Goke J, Walker BA, Thakurta A, Guinney J. 2020. Multiple Myeloma DREAM Challenge Reveals Epigenetic Regulator PHF19 As Marker of Aggressive Disease. Leukemia. Feb 14. doi: 10.1038/s41375-020-0742-z.
  3. Wang Z, Li H, Guan Y. 2020. Machine Learning For Cancer Drug Combination. Clinical Pharmacology & Therapeutics. Epub 11 February 2020 https://doi.org/10.1002/cpt.1773
  4. Deng K, Li H, Guan Y. 2020. Treatment Stratification of Patients with Metastatic Castration-resistant Prostate Cancer by Machine Learning. iScience. 2020 Feb 21;23(2):100804. doi: 10.1016/j.isci.2019.100804. Epub 2019 Dec 26.
  5. Li H, Guan Y. 2020. Machine Learning Empowers Phosphoproteome Prediction in Cancers. Bioinformatics. Feb 1;36(3):859-864. doi: 10.1093/bioinformatics/btz639
  6. Rai V, Quang D, Erdos M, Cusanovich R, Daza R, Narisu N, Zou L, Didion J, Guan Y, Shendure J, Parker SCJ, Collins FS. 2020. Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Molecular Metabolism. Feb;32:109-121. doi: 10.1016/j.molmet.2019.12.006.
  7. Wu S, Li H, Quang D, Guan Y. In Press. Three-plane-assembled Deep Learning Segmentation of Gliomas. Radiology: Artificial Intelligence.

2019

  1. Guan Y. 2019. Waking up to data challenges. Nature Machine Intelligence. Jan 7 1 (1), 67
  2. Li H, Quang D, Guan Y. 2019. Anchor: Trans-cell Type Prediction of Transcription Factor Binding Sites. Genome Research. 2019 Feb;29(2):281-292. doi: 10.1101/gr.237156.118. Epub 2018 Dec 19.
  3. Guan Y, Zhang H, Quang D, Wang Z, Pappas D, Kremer J, Parker S, Zhu F. 2019. Machine learning to predict anti-TNF drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis & Rheumatology. Dec;71(12):1987-1996. doi: 10.1002/art.41056. Epub 2019 Nov 4. (Highlighted in Nature Review Rheumatol. 2019 Can machine learning predict responses to TNF inhibitors? Oct 10. doi: 10.1038/s41584-019-0320-9.)
  4. Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M; AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, Tang EKY, Garnett MJ, Veroli GYD, Fawell S, Stolovitzky G, Guinney J, Dry JR, Saez-Rodriguez J. 2019. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications. (10, Article number: 2674 (2019))
  5. Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, al. et. 2019. Assessment of network module identification across complex diseases. Nature Methods. Sep;16(9):843-852. doi: 10.1038/s41592-019-0509-5. Epub 2019 Aug 30.
  6. Li D, Balamurugan S, Yang YF, Zhang JW, Huang D, Zou LG, Yang WD, Liu JS, Guan Y, Li HY. 2019. Transcriptional regulation of microalgae for concurrent lipid overproduction and secretion. Science Advances. Jan 30;5(1):eaau3795. doi: 10.1126/sciadv.aau3795. eCollection 2019 Jan.
  7. Al'Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, Pandey M, Maliakal G, van Rosendael AR, Beecy AN, Berman DS, Leipsic J, Nieman K, Andreini D, Pontone G, Schoepf UJ, Shaw LJ, Chang HJ, Narula J, Bax JJ, Guan Y, Min JK. 2019. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. Jun 21;40(24):1975-1986. doi: 10.1093/eurheartj/ehy404.
  8. Zou Z, Zhang H, Guan Y, Zhang J. 2019. Deep residual neural networks resolve quartet molecular phylogenies. Molecular Biology and Evolution. Dec 23. pii: msz307. doi: 10.1093/molbev/msz307.
  9. Li H, Siddiqui O, Zhang H, Guan Y. 2019. Joint Learning Improves Protein Abundance Prediction in Cancers. BMC Biology. Dec 23;17(1):107. doi: 10.1186/s12915-019-0730-9
  10. Li H-D, Bai T, Sandford E, Burmeister M, Guan Y. 2019. BaiHui: Cross-species Brain-specific Network Built with Hundreds of Hand-curated Datasets. Bioinformatics. (Jul 15;35(14):2486-2488. doi: 10.1093/bioinformatics/bty1001.)
  11. Jiang YQ, Xiong JH, Li HY, Guan Y, Gu H, Sun JF. 2019. Recognizing Basal Cell Carcinoma on Smartphone-Captured Digital Histopathology Images with Deep Neural Network. British Journal of Dermatology. Apr 24. doi: 10.1111/bjd.18026. [Epub ahead of print]
  12. Li H, Hu S, Neamati N, Guan Y. 2019. TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction. Bioinformatics. Jul 1;35(13):2338-2339. doi: 10.1093/bioinformatics/bty955.
  13. Tang M, Gao C, Goutman S, Kalinin A, Mukherjee B, Guan Y, Dinov I. 2019. Model-based and Model-free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics. Jul;17(3):407-421. doi: 10.1007/s12021-018-9406-9.
  14. Sun C, Li H, Mills R, Guan Y. 2019. Prognostic Model for Multiple Myeloma Progression Integrating Gene Expression and Clinical Features. GigaScience. Dec 1;8(12). pii: giz153. doi: 10.1093/gigascience/giz153.

2018

  1. Li H, Li T, Quang D, Guan Y. 2018. Network propagation predicts drug synergy in cancers. Cancer Research. Sep 15;78(18):5446-5457. doi: 10.1158/0008-5472.CAN-18-0740. Epub 2018 Jul 27.
  2. Li H, Panwar B, Omenn GS, Guan Y. 2018. Accurate Prediction of Personalized Olfactory Perception from Large-Scale Chemoinformatic Features.. Gigascience. Feb 1;7(2). doi: 10.1093/gigascience/gix127.
  3. Duda M, Zhang H, Li HD, Wall DP, Burmeister M, Guan Y. 2018. Brain-specific functional relationship networks inform autism spectrum disorder gene prediction. Transl Psychiatry. 8(1(56))
  4. Siddiqui O, Zhang H, Guan Y, Omenn G. 2018. Chromosome 17 Missing Proteins: Recent Progress and Future Directions as part of the Next-50MP Challenge. Journal of Proteome Research. doi: 10.1021/acs.jproteome.8b00442. [Epub ahead of print]
  5. Causey JL, Ashby C, Walker K, Wang ZP, Yang M, Guan Y, Moore JH, Huang X. 2018. DNAp: A Pipeline for DNA-seq Data Analysis. Sci Rep. (May 1;8(1):6793. doi: 10.1038/s41598-018-25022-6.)
  6. Hosoya T, Li H, Ku CJ, Wu Q, Guan Y, Engel JD. 2018. High throughput single cell sequencing of both T-cell-receptor-beta alleles. Journal of Immunology. (Dec 1;201(11):3465-3470. doi: 10.4049/jimmunol.1800774. Epub 2018 Oct 31.)
  7. Guan Y, Li T, Zhang HJ, Zhu F, Omenn GS. 2018. Prioritizing Predictive Biomarkers for Gene Essentiality in Cancer Cells with mRNA Expression Data and DNA Copy Number Profile. Bioinformatics. 2018 Dec 1;34(23):3975-3982. doi: 10.1093/bioinformatics/bty467.
  8. Zhang H, Zhu F, Dodge H, Higgins G, Omenn G, Guan Y. 2018. A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease. Gigascience. Jul 11. doi: 10.1093/gigascience/giy085
  9. Quang D, Guan Y, Parker SCJ. 2018. YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. Bioinformatics. Oct 15;34(20):3578-3580. doi: 10.1093/bioinformatics/bty396

2017

  1. Keller A, Gerkin RC, Guan Y, Dhurandhar A, Turu G, Szalai B, Mainland JD, Ihara Y, Yu CW, Wolfinger R, Vens C, Schietgat L, De Grave K, Norel R; DREAM Olfaction Prediction Consortium, Stolovitzky G, Cecchi GA, Vosshall LB, Meyer P. 2017. Predicting human olfactory perception from chemical features of odor molecules. Science. Feb 20. pii: eaal2014. doi: 10.1126/science.aal2014.
  2. Gönen M, Weir BA, Cowley GS, Vazquez F, Guan Y, Jaiswal A, Karasuyama M, Uzunangelov V, Wang T, Tsherniak A. 2017. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Syst. Nov 22;5(5):485-497.e3. doi: 10.1016/j.cels.2017.09.004. Epub 2017 Oct 4.
  3. Li H-D, Zhang Y, Guan Y, Menon R, Omenn GS. 2017. Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks. Methods Mol Biol. (1558):415-436.
  4. Ramani B, Panwar B, Moore LR, Wang B, Huang R, Guan Y, Paulson HL. 2017. Comparison of spinocerebellar ataxia type 3 mouse models identifies early gain-of-function, cell-autonomous transcriptional changes in oligodendrocytes. Hum Mol Genet. (Sep 1;26(17):3362-3374, doi:10.1093/hmg/ddx224)
  5. Huang Z, Zhang H, Boss J, Goutman SA, Mukherjee B, Dinov ID, Guan Y. 2017. Complete hazard ranking to analyze right-censored data: An ALS survival study. PLoS Comput Biol. Dec 18;13(12):e1005887. doi: 10.1371/journal.pcbi.1005887.
  6. Ku CJ, Sekiguchi JM, Panwar B, Guan Y, Takahashi S, Yoh K, Maillard I, Hosoya T, Engel JD. 2017. GATA3 abundance is a critical determinant of T cell receptor beta allelic exclusion. Mol Cell Biol. Mar 20. pii: MCB.00052-17. doi: 10.1128/MCB.00052-17.
  7. Panwar B, Omenn GS, Guan Y. 2017. miRmine: A Database of Human miRNA Expression Profiles. Bioinformatics. Jan 19. pii: btx019. doi: 10.1093/bioinformatics/btx019.

2016

  1. Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ; HPN-DREAM Consortium, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G, Saez-Rodriguez J, Mukherjee S. 2016. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 13(4):310-8.
  2. Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, Pappas D, Aguilar D, Anton B, Bonet J, Eksi R, Fornés O, Guney E, Li H, Marín MA, Panwar B, Planas-Iglesias J, Poglayen D, Cui J, Falcao AO, Suver C, Hoff B, Balagurusamy VSK, Dillenberger D, Neto EC, Norman T, Aittokallio T, Ammad-Ud-Din M, Azencott CA, Bellón V, Boeva V, Bunte K, Chheda H, Cheng L, Corander J, Dumontier M, Goldenberg A, Gopalacharyulu P, Hajiloo M, Hidru D, Jaiswal A, Kaski S, Khalfaoui B, Khan SA, Kramer ER, Marttinen P, Mezlini AM, Molparia B, Pirinen M, Saarela J, Samwald M, Stoven V, Tang H, Tang J, Torkamani A, Vert JP, Wang B, Wang T, Wennerberg K, Wineinger NE, Xiao G, Xie Y, Yeung R, Zhan X, Zhao C; Members of the Rheumatoid Arthritis Challenge Consortium, Greenberg J, Kremer J, Michaud K, Barton A, Coenen M, Mariette X, Miceli C, Shadick N, Weinblatt M, de Vries N, Tak PP, Gerlag D, Huizinga TWJ, Kurreeman F, Allaart CF, Louis Bridges S Jr, Criswell L, Moreland L, Klareskog L, Saevarsdottir S, Padyukov L, Gregersen PK, Friend S, Plenge R, Stolovitzky G, Oliva B, Guan Y, Mangravite LM. 2016. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.. Nat Commun. 7:12460.
  3. Zhu F, Panwar B, Dodge HH, Li H, Hampstead BM, Albin RL, Paulson HL, Guan Y. 2016. COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease. Sci Rep. 6:34567.
  4. Allen GI, Amoroso N, Anghel C, Balagurusamy V, Bare CJ, Beaton D, Bellotti R, Bennett DA, Boehme KL, Boutros PC, Caberlotto L, Caloian C, Campbell F, Chaibub Neto E, Chang YC, Chen B, Chen CY, Chien TY, Clark T, Das S, Davatzikos C, Deng J, Dillenberger D, Dobson RJ, Dong Q, Doshi J, Duma D, Errico R, Erus G, Everett E, Fardo DW, Friend SH, Fröhlich H, Gan J, St George-Hyslop P, Ghosh SS, Glaab E, Green RC, Guan Y, Hong MY, Huang C, Hwang J, Ibrahim J, Inglese P, Iyappan A, Jiang Q, Katsumata Y, Kauwe JS, Klein A, Kong D, Krause R, Lalonde E, Lauria M, Lee E, Lin X, Liu Z, Livingstone J, Logsdon BA, Lovestone S, Ma TW, Malhotra A, Mangravite LM, Maxwell TJ, Merrill E, Nagorski J, Namasivayam A, Narayan M, Naz M, Newhouse SJ, Norman TC, Nurtdinov RN, Oyang YJ, Pawitan Y, Peng S, Peters MA, Piccolo SR, Praveen P, Priami C, Sabelnykova VY, Senger P, Shen X, Simmons A, Sotiras A, Stolovitzky G, Tangaro S, Tateo A, Tung YA, Tustison NJ, Varol E, Vradenburg G, Weiner MW, Xiao G, Xie L, Xie Y, Xu J, Yang H, Zhan X, Zhou Y, Zhu F, Zhu H, Zhu S; Alzheimer's Disease Neuroimaging Initiative. 2016. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimers Dement.
  5. Menon S, Lu C, Menon R, Schwartz J, Guan Y. 2016. Effects of Antioxidants in Human Cancers: Differential Effects on Non-Coding Intronic RNA Expression. Antioxidants (Basel) . 5(1)
  6. Panwar B, Menon R, Eksi R, Li H, Omenn GS, Guan Y. 2016. Genome-wide Functional Annotation of Human Protein-coding Splice Variants Using Multiple Instance Learning. J Proteome Res.
  7. Li H-D, Menon R, Eksi R, Guerler A, Zhang Y, Omenn GS, Guan Y. 2016. A Network of Splice Isoforms for the Mouse. Sci Rep. 6:24507.
  8. Li H-D, Omenn GS, Guan Y. 2016. A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling. Brief Bioinform.
  9. Giorgetti E, Yu Z, Chua JP, Shimamura R, Zhao L, Zhu F, Venneti S, Pennuto M, Guan Y, Hung G, Lieberman AP. 2016. Rescue of Metabolic Alterations in AR113Q Skeletal Muscle by Peripheral Androgen Receptor Gene Silencing. Cell Rep. 17(1):125-36.

2015

  1. Zhu F, Panwar B, Guan Y. 2015. Algorithms for modeling global and context-specific functional relationship networks. Brief Bioinform.
  2. Menon R, Panwar B, Eksi R, Kleer C, Guan Y, Omenn GS. 2015. Computational Inferences of the Functions of Alternative/Non-Canonical Splice Isoforms Specific to HER2+/ER-/PR- Breast Cancers, a Chromosome 17 C-HPP Study. J Proteome Res.
  3. Li H-D, Menon R, Govindarajoo B, Panwar B, Zhang Y, Omenn GS, Guan Y. 2015. Functional Networks of Highest-Connected Splice Isoforms, from the Chromosome 17 Human Proteome Project. J Proteome Res.
  4. Guan Y, Martini S, Mariani LH. 2015. Genes Caught In Flagranti: Integrating Renal Transcriptional Profiles With Genotypes and Phenotypes. Semin Nephrol. 35(3):237-44.
  5. Panwar B, Menon R, Eksi R, Omenn GS, Guan Y. 2015. MI-PTV: A Tool for Visualizing the Chromosome-centric Human Proteome. J Proteome Res.
  6. Li H-D, Omenn GS, Guan Y. 2015. MIsoMine: a genome-scale high-resolution data portal of expression, function and networks at the splice isoform level in the mouse. Database (Oxford). 2015:bav045.
  7. Horvatovich P, Lundberg EK, Chen YJ, Sung TY, He F, Nice EC, Goode RJ, Yu S, Ranganathan S, Baker MS, Domont GB, Velasquez E, Li D, Liu S, Wang Q, He QY, Menon R, Guan Y, Corrales FJ, Segura V, Casal JI, Pascual-Montano A, Albar JP, Fuentes M, Gonzalez-Gonzalez M, Diez P, Ibarrola N, Degano RM, Mohammed Y, Borchers CH, Urbani A, Soggiu A, Yamamoto T, Salekdeh GH, Archakov A, Ponomarenko E, Lisitsa A, Lichti CF, Mostovenko E, Kroes RA, Rezeli M, Végvári Á, Fehniger TE, Bischoff R, Vizcaíno JA, Deutsch EW, Lane L, Nilsson CL, Marko-Varga G, Omenn GS, Jeong SK, Lim JS, Paik YK, Hancock WS. 2015. A Quest for Missing Proteins: update 2015 on Chromosome-Centric Human Proteome Project. J Proteome Res.
  8. Zhu F, Shi L, Engel JD, Guan Y. 2015. Regulatory network inferred using expression data of small sample size: application and validation in erythroid system. Bioinformatics.

2014

  1. Li H-D, Menon R, Omenn GS, Guan Y. 2014. The emerging era of genomic data integration for analyzing splice isoform function. Trends Genet. 30(8):340-347.
  2. Ma Q, Ozel AB, Ramdas S, McGee B, Khoriaty R, Siemieniak D, Li H-D, Guan Y, Brody LC, Mills JL et al.. 2014. Genetic variants in PLG, LPA and SIGLEC 14 as well as smoking contribute to plasma plasminogen levels..Blood.
  3. Bethunaickan R, Berthier CC, Zhang W, Eksi R, Li H-D, Guan Y, Kretzler M, Davidson A. 2014. Identification of Stage-Specific Genes Associated With Lupus Nephritis and Response to Remission Induction in (NZB × NZW)F1 and NZM2410 Mice. Arthritis Rheumatol. 66(8):2246-58.
  4. Shi L, Sierant MC, Gurdziel K, Zhu F, Cui S, Kolodziej KE, Strouboulis J, Guan Y, Tanabe O, Lim KC, Engel JD. 2014. Biased, Non-equivalent Gene-Proximal and -Distal Binding Motifs of Orphan Nuclear Receptor TR4 in Primary Human Erythroid Cells.. PLoS Genet. 10(5):e1004339.
  5. Shi L, Lin Y-H, Sierant MC, Zhu F, Cui S, Guan Y, Sartor MA, Tanabe O, Lim K-C, Engel JD. 2014. Developmental transcriptome analysis of human erythropoiesis.. Hum Mol Genet. 23(17):4528-42.
  6. Zhu F, Shi L, Li H, Eksi R, Engel JD, Guan Y. 2014. Modeling Dynamic Functional Relationship Networks and Application to Ex Vivo Human Erythroid Differentiation.. Bioinformatics.
  7. Omenn GS, Guan Y, Menon R. 2014. A new class of protein cancer biomarker candidates: differentially expressed splice variants of ERBB2 (HER2/neu) and ERBB1 (EGFR) in breast cancer cell lines.. J Proteomics. 107:103-12.
  8. Zhu F, Guan Y. 2014. Predicting Dynamic Signaling Network Response under Unseen Perturbations. Bioinformatics.
  9. Li H-D, Menon R, Omenn GS, Guan Y. 2014. Revisiting the identification of canonical splice isoforms through integration of functional genomics and proteomics evidence.. Proteomics.

2013

  1. Yang Z-K, Niu Y-F, Ma Y-H, Xue J, Zhang M-H, Yang W-D, Liu J-S, Lu S-H, Guan Y, Li H-Y. 2013. Molecular and cellular mechanisms of neutral lipid accumulation in diatom following nitrogen deprivation.. Biotechnol Biofuels. 6(1):67.
  2. Guan Y, Dunham MJ, Troyanskaya OG, Caudy AA. 2013. Comparative gene expression between two yeast species.. BMC Genomics. 14:33.
  3. Park CY, Wong AK, Greene CS, Rowland J, Guan Y, Bongo LA, Burdine RD, Troyanskaya OG. 2013. Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.. PLoS Comput Biol. 9(3):e1002957.
  4. Caudy AA, Guan Y, Jia Y, Hansen C, DeSevo C, Hayes AP, Agee J, Alvarez-Dominguez JR, Arellano H, Barrett D et al.. 2013. A new system for comparative functional genomics of Saccharomyces yeasts.. Genetics. 195(1):275-87.
  5. Eksi R, Li H-D, Menon R, Wen Y, Omenn GS, Kretzler M, Guan Y. 2013. Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.. PLoS Comput Biol. 9(11):e1003314.

2012

  1. Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, Troyanskaya OG. 2012. IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.. Nucleic Acids Res. 40(Web Server issue):W484-90.
  2. Guan Y, Gorenshteyn D, Burmeister M, Wong AK, Schimenti JC, Handel MAnn, Bult CJ, Hibbs MA, Troyanskaya OG. 2012. Tissue-specific functional networks for prioritizing phenotype and disease genes.. PLoS Comput Biol. 8(9):e1002694.

2011

  1. Guan Y, Yao V, Tsui K, Gebbia M, Dunham MJ, Nislow C, Troyanskaya OG. 2011. Nucleosome-coupled expression differences in closely-related species.. BMC Genomics. 12:466.

2010

  1. Guan Y, Ackert-Bicknell CL, Kell B, Troyanskaya OG, Hibbs MA. 2010. Functional genomics complements quantitative genetics in identifying disease-gene associations.. PLoS Comput Biol. 6(11):e1000991.
  2. Guan Y, Dunham M, Caudy A, Troyanskaya O. 2010. Systematic planning of genome-scale experiments in poorly studied species.. PLoS Comput Biol. 6(3):e1000698.

2009

  1. Guan Y, Ramalingam S, Nagegowda D, Taylor PWJ, Chye M-L. 2008. Brassica juncea chitinase BjCHI1 inhibits growth of fungal phytopathogens and agglutinates Gram-negative bacteria.. J Exp Bot. 59(12):3475-84.
  2. Guan Y, Chye M-L. 2008. A Brassica juncea chitinase with two-chitin binding domains show anti-microbial properties against phytopathogens and Gram-negative bacteria.. Plant Signal Behav. 3(12):1103-5.
  3. Peña-Castillo L, Tasan M, Myers CL, Lee H, Joshi T, Zhang C, Guan Y, Leone M, Pagnani A, Kim WK, Krumpelman C, Tian W, Obozinski G, Qi Y, Mostafavi S, Lin GN, Berriz GF, Gibbons FD, Lanckriet G, Qiu J, Grant C, Barutcuoglu Z, Hill DP, Warde-Farley D, Grouios C, Ray D, Blake JA, Deng M, Jordan MI, Noble WS, Morris Q, Klein-Seetharaman J, Bar-Joseph Z, Chen T, Sun F, Troyanskaya OG, Marcotte EM, Xu D, Hughes TR, Roth FP. 2008. A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.. Genome Biol. 9 Suppl 1:S2.

2008

  1. Guan Y, Myers CL, Lu R, Lemischka IR, Bult CJ, Troyanskaya OG. 2008. A genomewide functional network for the laboratory mouse.. PLoS Comput Biol. 4(9):e1000165.
  2. Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, Troyanskaya OG. 2008. Predicting gene function in a hierarchical context with an ensemble of classifiers.. Genome Biol. 9 Suppl 1:S3.

2007

  1. Lui W-Y, Wong EWP, Guan Y, Lee WM. 2007. Dual transcriptional control of claudin-11 via an overlapping GATA/NF-Y motif: positive regulation through the interaction of GATA, NF-YA, and CREB and negative regulation through the interaction of Smad, HDAC1, and mSin3A.. J Cell Physiol. 211(3):638-48.
  2. Guan Y, Dunham MJ, Troyanskaya OG. 2007. Functional analysis of gene duplications in Saccharomyces cerevisiae..Genetics. 175(2):933-43.