Publications

Lukas B, Dai Y. An Integrative Approach to Building Regulatory Potential-Weighted Gene Regulatory Networks: A Leiomyoma Case Study, BCB ’23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Article No.: 23. Pages 1–6. doi.org/10.1145/3584371.3612985.
ACM Digital Library

Zuberi A, Huang Y, Dotts AJ, Wei H, Coon V JS, Liu S, Iizuka T, Wu O, Sotos O, Saini P, Chakravarti D, Boyer TG, Dai Y, Bulun SE, Yin P. MED12 mutation activates tryptophan-kynurenine-AHR pathway to promote of uterine leiomyoma. JCI Insight 2023. doi: 10.1172/jci.insight.171305.
PubMed

Sanborn MA, Wang X, Gao S, Dai Y, Rehman J. SenePy: Unveiling the Cell-Type Specific Landscape of Cellular Senescence through Single-Cell Analysis in Living Organisms. bioRxiv 2023:2023.08.30.555644. DOI: 10.1101/2023.08.30.555644.
bioRxiv

Huang Y, Alvernaz S, Kim SJ, Maki P, Dai Y, Peñalver-Bernabé B. Predicting prenatal depression and assessing model bias using machine learning models. medRxiv 2023:2023.07.17.23292587. DOI: 10.1101/2023.07.17.23292587.
medRxiv

Yazici C, Thaker S, Castellanos KK, Al Rashdan H, Huang Y, Sarraf P, Boulay B, Grippo P, Gaskins HR, Danielson KK, Papachristou GI, Tussing-Humphreys L, Dai Y, Mutlu ER, Layden BT. Diet, gut microbiome and their end-metabolites associate with acute pancreatitis risk. Clin Transl Gastroenterol. 2023 May 10. doi: 10.14309/ctg.0000000000000597. Epub ahead of print. PMID: 37162146.
PubMed

Dotts AJ, Reiman D, Yin P, Kujawa S, Grobman WA, Dai Y, Bulun SE. In Vivo Genome-Wide PGR Binding in Pregnant Human Myometrium Identifies Potential Regulators of Labor. Reprod. Sci. 30, 544–559 (2023), doi.org/10.1007/s43032-022-01002-0. PMID: 35732928; PMCID: PMC9988762.
PubMed

Mathew B, Acha LG, Torres LA, Huang CC, Liu A, Kalinin S, Leung K, Dai Y, Feinstein DL, Ravindran S, Roth S. MicroRNA-based engineering of mesenchymal stem cell extracellular vesicles for treatment of retinal ischemic disorders: Engineered extracellular vesiclesand retinal ischemia. Acta Biomaterialia. 2023;158:782-797. doi: 10.1016/j.actbio.2023.01.014. Epub 2023 Jan 11. PMID: 36638942; PMCID: PMC10005109.
PubMed

Pachpor K, Priyadarshini M, Reiman D, Layden BT, and Dai Y. MOMMI-MP: A Comprehensive Database for Integrated Analysis of Metabolic and Microbiome Profiling of Mouse Pregnancy. Preprints, 2022, doi: 10.20944/preprints202212.0378.v1
PDF. Link to MOMMI-MP database

Zandigohar M and Dai Y. Information retrieval in single cell chromatin analysis using TF-IDF transformation methods. The Proc. of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, pp. 877-882. doi:10.1109/BIBM55620.2022.9994949
IEEE Digital Library

Larsen PE and Dai Y. Modeling interaction networks between host, diet, and bacteria predicts obesogenesis in a mouse model. Frontiers in Molecular Biosciences, 2022, Vol. 9. doi: 10.3389/fmolb.2022.1059094.PMID: 36458093; PMCID: PMC9705962.
PubMed

Goad J, Rudolph J, Zandigohar M, Tae M, Dai Y, Wei JJ, Bulun SE, Chakravarti D, Rajkovic A. Single-cell sequencing reveals novel cellular heterogeneity in uterine leiomyomas. Human Reproduction, 2022, deac183. doi: 10.1093/humrep/deac183. PMID: 36001050.
PubMed

Gao S, Rehman J, and Dai Y. Assessing Comparative Importance of DNA Sequence and Epigenetic Modifications on Gene Expression using a Deep Convolutional Neural Network. Computational and Structural Biotechnology Journal, vol 20, 2022, Pages 3814-3823. doi: 10.1016/j.csbj.2022.07.014. PMID: 35891778; PMCID: PMC9307602.
PubMed

Zhang L, Gao S, White Z, Dai Y, Malik AB, Rehman J. Single-cell transcriptomic profiling of lung endothelial cells identifies dynamic inflammatory and regenerative subpopulations. JCI insight. 2022 Jun 8;7(11):e158079. doi: 10.1172/jci.insight.158079. PMID: 35511435.
PubMed

Wang, X., Sanborn, M.A., Dai, Y., and Rehman, J. Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19. JCI insight, 2022 Apr 8;7(7):e157255. doi: 10.1172/jci.insight.157255. PMID: 35175937; PMCID: PMC9057597.
PubMed
Link to Press

Priyadarshini, M., Navarro, G., Reiman, D.J., Sharma, A., Xu, K., Lednovich, K., Manzella, C.R., Khan, M.W., Garcia, M.S., Allard, S., Wicksteed, B, Chlipala, G., Szynal, B, Penalver Bernabe, B, Maki, P., Gill, R., Perdew, G., Gilbert, J., Dai, Y, and Layden, B. Gestational insulin resistance is mediated by the gut microbiome-indoleamine 2,3-dioxygenase axis. Gastroenterology, 2022. May;162(6):1675-1689.e11. doi: 10.1053/j.gastro.2022.01.008. Epub 2022 Jan 13. PMID: 35032499; PMCID: PMC9040389.
PubMed
Link to UIC Today

Liu S, Yin P, Xu J, Dotts AJ, Kujawa SA, Coon VJ, Zhao H, Dai Y and Bulun SE. Progesterone receptor-DNA methylation crosstalk regulates depletion of uterine leiomyoma stem cells: A potential therapeutic target. Stem Cell Reports, 2021 Sep 14;16(9):2099-2106. doi: 10.1016/j.stemcr.2021.07.013. Epub 2021 Aug 12. PMID: 34388365; PMCID: PMC8452515.
PubMed

Khajeh T, Reiman D, Morley R and Dai Y. Integrating microbiome and metabolome data for host disease prediction via deep learning neural networks. 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2021, pp. 1-4, doi: 10.1109/BHI50953.2021.9508601.
IEEE Explore
YouTube talk

Gao T, Ha C, Zhang Q, Huang Y, Yin P, Zhang Q, Huang Y, Dai Y and Serdar SE, and Wei, JJ. Leiomyoma with Bizarre Nuclei: A Stagnant Precursor to Leiomyosarcoma?. Biomarkers Journal, 2021, Vol.7 No.4:85.
PDF

Gao S, Dai Y and Rehman J. A Bayesian Inference Transcription Factor Activity Model for the Analysis of Single Cell Transcriptomes. Genome Research, 2021. 31(7):1296-1311 doi:10.1101/gr.265595.120.PMID: 34193535
PubMed
Link to UIC Today

Reiman D, Layden BT, Dai Y. MiMeNet: Exploring Microbiome-Metabolome Relationships using Neural Networks. PLoS Computational Biology, 2021. May 17;17(5):e1009021. doi: 10.1371/journal.pcbi.1009021. PMID: 33999922; PMCID: PMC8158931.
PubMed and YouTube talk at ISMB 2020
Link to GitHub

Reiman D, Sosa U, Dai Y. Machine Learning in Identification of Disease-Associated Microbiota. in Inflammation, Infection, and Microbiome in Cancers: Evidence, Mechanisms, and Implications, J. Sun, Ed., ed Cham: Springer International Publishing, 2021. pp. 431-456. doi.org/10.1007/978-3-030-67951-4_15.
Springer Link

Güntürkün F, Chen D, Akbilgic O, Davis RL, Karabayir I, Strome M, Dai Y, Saraf SL, Ataga KI. Using machine learning to predict rapid decline of kidney function in sickle cell anemia. EJHaem. 2021 Feb 10;2(2):257-260. doi: 10.1002/jha2.168. PMID: 35845269; PMCID: PMC9176130.
PubMed

Liu S, Yin P, Xu J, Dotts AJ, Kujawa SA, Coon V JS, Zhao H, Shilatifard A, Dai Y and Bulun SE. “Targeting DNA Methylation Depletes Uterine Leiomyoma Stem Cell–enriched Population by Stimulating Their Differentiation”, Endocrinology, (2020) vol. 161 (10). bqaa143. doi:10.1210/endocr/bqaa143. PMID: 32812024.
PubMed

Reiman D, Farhat AM and Dai Y. “Predicting Host Phenotype Based on Gut Microbiome Using a Convolutional Neural Network Approach” , in Artificial Neural Networks , H. Cartwright ed, Springer US, (2020) vol. 2190, pp. 249-266. doi:10.1007/978-1-0716-0826-5_12. PMID: 32804370.
PubMed
Springer Link

Reiman D and Dai Y. “Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets” , Proceedings of the 1st International Conference on Deep Learning Theory and Applications, (2020) Vol 1: DeLTA:103-110. doi: 10.5220/0009892601030110.
SCITEPRESS Digital Library
bioRxiv

Reiman D, Metwally AA, Sun J, and Dai Y. “Meta-Signer: Metagenomic Signature Identifier based on Rank Aggregation of Features”. F1000Research 2021, 10:194. doi: 10.12688/f1000research.27384.1
F1000Research
Link to GitHub

Reiman D, Metwally AA, Sun J and Dai Y. “PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolution Neural Networks for Metagenomic Data” , The IEEE Journal of Biomedical and Health Informatics, (2020) 10, pp. 2993-3001. doi: 10.1109/JBHI.2020.2993761. PMID: 32396115.
PubMed
Link to GitHub

Chatterjee I, Lu R, Zhang Y, Zhang J, Dai Y, Xia Y and Sun J. “Vitamin D receptor promotes healthy microbial metabolites and microbiome”, Scientific Reports ,(2020) 10 (1), p.7340. doi: 10.1038/s41598-020-64226-7. PMID: 32355205 PMCID: PMC7192915
PubMed

Jambusaria A, Hong Z, Zhang L, Srivastava S, Jana A, Toth PT, Dai Y, Malik AB and Rehman J. “Endothelial heterogeneity across distinct vascular beds during homeostasis and inflammation” , eLife , (2020) 9, pp. e51413. doi: 10.7554/eLife.51413. PMID:31944177.
PubMed

Chen L, Chen Z, Simões A, Wu X, Dai Y, DiPietro LA, and Zhou X. “Site-Specific Expression Pattern of PIWI-Interacting RNA in Skin and Oral Mucosal Wound Healing”, International Journal of Molecular Sciences, (2020) 21(2), 521. doi.org/10.3390/ijms21020521. PMID: 31947648.
PubMed

Xu J, Reiman D, Gao S, and Dai Y. “Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression”, Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.2399-2404. doi: 10.1109/BIBM47256.2019.8983037.
IEEE Explore Digital Library

Reiman D and Dai Y. “Using Autoencoders for Predicting Latent Microbiome Community Shifts Responding to Dietary Changes”,Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1884-1891. doi:10.1109/BIBM47256.2019.8983124
IEEE Explore Digital Library

Chen L, Simões A, Chen Z, Zhao Y, Wu X, Dai Y, DiPietro LA and Zhou X. “Overexpression of the Oral Mucosa-Specific microRNA-31 Promotes Skin Wound Closure”, International Journal of Molecular Sciences, (2019) 20(15):3679. doi: 10.3390/ijms20153679. PMID: 31357577
PubMed

Simões A, Chen L, Chen Z, Zhao Y, Gao S, Marucha PT, Dai Y, DiPietro LA and Zhou X. “Differential microRNA profile underlies the divergent healing responses in skin and oral mucosal wounds”, Scientific Reports, (2019) 9(1):7160. doi: 10.1038/s41598-019-43682-w. PMID: 31073224.
PubMed

Metwally AA, Yu PS, Reiman D, Dai Y, Finn PW and Perkins DL. “Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks”, PLOS Computational Biology , (2019) 15(2): e1006693. doi: 10.1371/journal.pcbi.1006693. PMID: 30716085. PMCID: PMC6361419.
PubMed

Xu J, Liu S, Yin P, Bulun S and Dai Y. “MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data”, BMC Bioinformatics, (2018) 19 (1), P.540. doi:10.1186/s12859-018-2574-7. PMID:30577750.
PubMed

Bakke D, Chatterjee I, Agrawal A, Dai Y and Sun J, “Regulation of Microbiota by Vitamin D Receptor: A Nuclear Weapon in Metabolic Diseases”, Nuclear Receptor Research, (2018) 5, Article ID:101377. doi:10.11131/2018/101377. PMID: 30828578.
PDF

Jambusaria A, Klomp J, Hong Z, Rafii S, Dai Y, Malik AB and Rehman J. “A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks”, BMC Bioinformatics, (2018) 19(1):217. doi: 10.1186/s12859-018-2190-6. PMCID: PMC6019795. PMID: 29940845.
PubMed

Larsen PE, Zerbs S, Laible PD, Collart FR, Korajczyk P, Dai Y and Noirot P.“Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communic: Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information”, mSystems , (2018) 3(3) e00189-17. doi: 10.1128/mSystems.00189-17. PMCID: PMC6009100, PMID: 2994656.
PubMed

Metwally AA, Yang J, Ascoli C, Dai Y, Finn PW and Perkins DL. “MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies”, Microbiome, (2018) 6:32. DOI: 10.1186/s40168-018-0402-y. PMID: 29439731, PMCID: PMC5812052.
PubMed
Metalonda on CRAN

Metwally AA, Finn PW, Dai Y and Perkins DL. “Detection of Differential Abundance Intervals in Longitudinal Metagenomic Data Using Negative Binomial Smoothing Spline ANOVA”, Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics (ACM-BCB’17), (2017) 295-304.
ACM Digital Library

Reiman D, Metwally AA and Dai Y. “Using Convolutional Neural Networks to Explore the Microbiome” , Conf Proc IEEE Eng Med Biol Soc., (2017) 4269-4272. doi: 10.1109/EMBC.2017.8037799. PMID: 29060840.
IEEE Explore Digital Library
PubMed

Gao S, Leonardo T, Zhou X and Dai Y. “An integrative analysis of miRNA and mRNA expression data of head and neck oral cancer in TCGA (poster)”, F1000Research, (2017) 6 (ISCB Comm J):862.
F1000Research

Chen Z, Yu T, Cabay RJ, Jin Y, Mahjabeen I, Luan X, Huang L, Dai Y and Zhou X. miR-486-3p, miR-139-5p, and miR-21 as Biomarkers for the Detection of Oral Tongue Squamous Cell Carcinoma, Biomarkers in Cancer, (2017) 9 pp. 1-8. PMID: 28096697 PMCID: PMC5224348.
PubMed

Metwally AA, Dai Y, Finn PW and Perkins DL. “Weighted Voting Taxonomic Identification Method of Microbial Sequences.”, PLoS ONE, (2016) 11(9): e0163527. doi:10.1371/journal.pone.0163527. PMID: 27683082.
PubMed

Xu J, Hu H and Dai Y. “LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines”, PLoS ONE,(2016) 11(9): e0163491. doi:10.1371/journal.pone.0163491. PMID: 27662487.
PubMed

He Q, Chen Z, Dong Q, Zhang L, Chen D, Patel A, Koya A, Luan X, Cabay RJ, Dai Y, Wang A and Zhou X. “MicroRNA-21 regulates prostaglandin E2 signaling pathway by targeting 15-hydroxyprostaglandin dehydrogenase in tongue squamous cell carcinoma”, BMC Cancer, (2016) 16(1), p.685. DOI: 10.1186/s12885-016-2716-0. PMID: 27561985 PMCID: PMC5000501.
PubMed

Hu H and Dai Y. “Prioritize Transcription Factor Binding Sites for Multiple Co-Expressed Gene Sets Based on Lasso Multinomial Regression Models”, Emerging Research in the Analysis and Modeling of Gene Regulatory Networks, IGI Global (2016) p.280-315.
To IGI Gloabl

Peter E. Larsen, Avinash Sreedasyam, Geetika Trivedi, Shalaka Desai, Yang Dai, Leland J. Cseke, and Frank R. Collart. “Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction”, Frontiers in Plant Science, (2016) 1061. 2 DOI: 10.3389/fpls.2015.01061. PMID: 26834754 PMCID: PMC471729.
PubMed

Emmadi, R., Canestrari, E., Arbieva, Z. H., Mu, W., Dai, Y., Frasor, J., & Wiley, E. “Correlative Analysis of miRNA Expression and Oncotype Dx Recurrence Score in Estrogen Receptor Positive Breast Carcinomas”, PLOS One, (2015) 10 (12): e0145346. DOI:10.1371/journal.pone.0145346. PMID: 26717565 PMCID: PMC4696739 DOI: 10.1371/journal.pone.0145346.
PubMed

Peter Larsen, Frank Collart and Yang Dai. “Metabolome of human gut microbiome is predictive of host dysbiosis”, GigaScience, (2015) 4 (1), pp.42. DOI:10.1186/s13742-015-0084-3. PMID: 26380076 PMCID: PMC4570295 DOI: 10.1186/s13742-015-0084-3.
PubMed

Peter Larsen, Frank Collart and Yang Dai. “Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling”, PLoS ONE, (2015) 10 (9), e0132837. DOI: 10.1371/journal.pone.0132837. PMID: 26332409.
PubMed

Hong Hu, Jingting Xu, and Yang Dai. “Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression”, IEEE J. Biomed. Health Inform, (2015) 19 (4):1293-1300. PMID: 26332409 PMCID: PMC4557938 DOI: 10.1371/journal.pone.0132837.
PubMed

Peter Larsen, Yang Dai and Frank Collart. “Prediction Bacterial Community Assemblages using an Artificial Neural Network Approach”, in Methods Mol Biol., Springer (2015) 1260:33-43. doi: 10.1007/978-1-4939-2239-0_3. PMID: 25502374.
PubMed

Yun Xu, Changyu Hu, Yang Dai and Jie Liang. “On simplified global nonlinear function for fitness landscape: a case study of inverse protein folding”, PLoS One , (2014) 11:9(8) PMCID: PMC4128808 doi: 10.1371/journal.pone.0104403.
PubMed

Hong Hu and Yang Dai. “Exploring Regulatory Elements in Low-methylated Regions for Gene Expression Prediction”, Proc. of the Annual International Conference of the IEEE , (2014) pp. 4763 – 4766 DOI: 10.1109/EMBC.2014.6944689. PMID:25571057.
PubMed

Hong Hu and Yang Dai. “A Model-based Approach to Transcription Regulatory Network Reconstruction from Time-Course Gene Expression Data”, Proc. of the Annual International Conference of the IEEE , (2014) 4767 – 4770 10.1109/EMBC.2014.6944690. PMID:25571058.
PubMed

Peter Larsen, Frank Collart and Yang Dai. “Using Metabolomic and Transportomic Modeling and Machine Learning to Identify Putative Novel Therapeutic Targets for Antibiotic Resistant Pseudomonad Infections”, Proc. of the Annual International Conference of the IEEE , (2014) 314-317. doi: 10.1109/EMBC.2014.6943592. PMID: 25569960.
PubmMed

A. Turabelidze, S. Guo, A. Y. Chung, L. Chen, Y. Dai, P. T. Marucha, and L. A. DiPietro “Intrinsic Differences between Oral and Skin Keratinocytes” , PLoS One, (2014) 9 (9): e101480. DOI: 10.1371/journal.pone.0101480.
PubMed

MT. Dyson, D. Roqueiro, D. Monsivais, M. Ercan, ME Pavone, DC. Brooks, T. Kakinuma, M. Ono, N. Jafari, Y. Dai and SE.Bulun. “Genome-Wide DNA Methylation Analysis Predicts an Epigenetic Switch for GATA Factor Expression in Endometriosis” , PLoS Genet., (2014) 10(3): e1004158. doi: 10.1371/journal.pgen.1004158, PMCID: PMC3945170.
PubMed

Jeol Fontanarosa, Yang Dai. “Exploration of microRNA Genomic Variation Associated with Common Human Diseases.” in microRNAs in Toxicology and Medicine (S. Sahu, ed), Wiley (2013) pp.309-316. doi: 10.1002/9781118695999.ch19.
Abstract

Yi Jin, Stéphanie D Tymen, Dan Chen, Zong J Fang, Yan Zhao, Dragan Dragas, Yang Dai, Phillip T Marucha, Xiaofeng Zhou. “MicroRNA-99 Family Targets AKT/mTOR Signaling Pathway in Dermal Wound Healing”, PLoS ONE , (2013) 8(5): e64434. doi:10.1371/journal.pone.0064434.
PubMed

Cheng Wang, Xiqiang Liu, Zujian Chen, Hongzhang Huang, Yi Jin, Antonia Kolokythas, Anxun Wang, Yang Dai, David T.W. Wong, Xiaofeng Zhou. “Polycomb group protein EZH2-mediated E-cadherin repression promotes metastasis of oral tongue squamous cell carcinoma”, Molecular Carcinogenesis , (2013) 52(3):229-36. doi: 10.1002/mc.21848.
PubMed

Antonia Kolokythas, Mitchell J Bosman, Kristen B Pytynia, Suchismita Panda, Herve Y Sroussi, Yang Dai, Joel L Schwartz, Guy R Adami.“A prototype tobacco-associated oral squamous cell carcinoma classifier using RNA from brush cytology”, J. Oral Pathol. Med., (2013). doi: 10.1111/jop.12068.
PubMed

Yang Dai, Lei Huang. “Systems Biology Understanding of Tamoxifen Resistance of Breast Cancer based on Integrative Bioinformatics Approaches” , in Breast Cancer Metastasis and Drug Resistance: Progress and Prospects, (ed. A. Ahmad), Springer-Verlag New York Inc. (2013) pp.249-259. doi:10:100/7/978-1-4614-5647-6_14.
PDF

Wenbo Mu, Damian Roqueiro, Yang Dai. “A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules”, Scientific World Journal , (2013). doi:10.1155/2013/197406.
PubMed

Damian Roqueiro, Lei Huang, Yang Dai. “Identifying Transcription Factors and microRNAs as Key Regulators of Pathways Using Bayesian Inference on Known Pathway Structures”, Proteome Science , (2012). doi:10.1186/1477-5956-10-S1-S15.
PubMed

Ping Yin, Damian Roqueiro, Lei Huang, Jonas K. Owen, Anna Xie, Antonia Navarro, Diana Monsivais, John S. Coon V, J. Julie Kim, Yang Dai, Serdar E. Bulun. “Genome-Wide Progesterone Receptor Binding: Cell Type-Specific and Shared Mechanisms in T47D Breast Cancer Cells and Primary Leiomyoma Cells”, PLoS ONE, (2012) 7(1): e29021. doi:10.1371/journal.pone.0029021.
PubMed

Yi Jin, Cheng Wang, Xiqiang Liu, Wenbo Mu, Zujian Chen, Dongsheng Yu, Anxun Wang, Yang Dai, and Xiaofeng Zhou. “Molecular Characterization of the MicroRNA-138-Fos-like Antigen 1 (FOSL1) Regulatory Module in Squamous Cell Carcinoma.” Journal of Biological Chemistry (2011) 286 (46) 40104-40109.
PubMed

Damian Roqueiro, Lei Huang, Yang Dai. “Identifying Transcription Factors and microRNAs as Key Regulators of Pathways Using Bayesian Inference on Known Pathway Structures.” Proceedings of IEEE International Conference in Biomedicine (BIBM) (2011) 228-233. doi: 10.1109/BIBM.2011.120.
PubMed

Lei Huang, Shuangping Zhao, Jonna Frasor and Yang Dai. “An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F actvity as distinguishing characteristics of tamoxifen resistant breast tumors.” PLoS ONE (2011) 6(7): e22274. doi:10.1371/journal.pone.0022274.
PubMed

Xiqiang Liu, Cheng Wang, Zujian Chen, Yi Jin, Yun Wang, Antonia Kolokythas, Yang Dai and Xiaofeng Zhou.
“MicroRNA-138 suppresses epithelial-mesenchymal transition in squamous cell carcinoma cell lines.” Biochemical Journal (2011). doi:10.1042/BJ20111006.
PubMed

Joel Fontanarosa and Yang Dai. “Using lasso regression to detect predictive aggregate effects in genetic studies.” BMC Proceedings (2011) 5(Suppl 9):S69. doi:10.1186/1753-6561-5-S9-S69.
BioMed Central

Cheng Wang, Xiqiang Liu, Hongzhang Huang, Huibin Ma, Weixin Cai, Jingsong Hou, Lei Huang, Yang Dai, Tianwei Yu, Xiaofeng Zhou. “Deregulation of Snai2 is associated with metastasis and poor prognosis in tongue squamous cell carcinoma.” International Journal of Cancer (2011) doi: 10.1002/ijc.26226.
PubMed

Hong Hu, Damian Roqueiro, Yang Dai. “Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models.” Proceedings of Annual International Conference of the IEEE in Medicine and Biology Society (EMBC) (2011) pp.6853-6856. doi: 10.1109/IEMBS.2011.6091690.
IEEE Xplore Digital Library.

Joel Fontanarosa and Yang Dai. “An evolutionary optimization strategy using graphics processing units to efficiently investigate gene-gene interactions in genetic association studies.” Proceedings of Annual International Conference of the IEEE in Medicine and Biology Society,EMBC (2011) pp.5547-5550. doi: 10.1109/IEMBS.2011.6091415.
PubMed.

Lei Huang, Damian Roqueiro, Yang Dai. “Analyzing mRNA and microRNA co-expression profiles to identify pathways and their potential regulators in ER and ER- breast tumors.” Proceedings of Annual International Conference of the IEEE in Biology Society,EMBC (2011) pp.932-935. doi: 10.1109/IEMBS.2011.6090210.
PubMed.

Lu Jiang, Yang Dai, Xiqiang Liu, Cheng Wang, Anxun Wang, Zujian Chen, Caroline E. Heidbreder, Antonia Kolokythas and Xiaofeng Zhou. “Identification and functional validation of G protein alpha inhibiting activity polypeptide 2 (GNAI2) as a microRNA-138 target in tongue squamous cell carcinoma.” Human Genetics (2011) 129(2) 189-97.
PubMed

Joel Fontanarosa and Yang Dai. “A block-based evolutionary optimization strategy to investigate gene-gene interactions in genetic association studies.” Proceedings of IEEE International conference on Bioinformatics and Biomedicine Workshop (2010) 330-335.
IIEEE Xplore Digital Library

Oleksiy Karpenko and Yang Dai. “Relational Database Index Choices for Genome Annotation Data.” Proceedings of Bioinformatics and Biomedicine Workshops (BIBMW) (2010) pp.264-268 (2010) doi: 10.1109/BIBMW.2010.5703810.
IEEE Xplore Digital Library

Damian Roqueiro, Jonna Frasor and Yang Dai.
“bindSDb: A Binding-information Spatial Database.” Proceedings of Bioinformatics and Biomedicine Workshops (2010) pp.573-578 (2010) doi: 10.1109/BIBMW.2010.5703864.
IEEE Xplore Digital Library

Peter Larsen, Frank Collart and Yang Dai. “Incorporating network topology improves prediction of protein interaction networks from transcriptomic data.” International Journal of Knowledge discovery and Bioinformatics (2010) 1(3), pp.1-19.
IGI-Global

X. Liu, A. Wang, CE. Heidbreder, L. Jiang, J. Yu, A. Kolokythas, L. Huang, Y. Dai, X. Zhou. “MicroRNA-24 targeting RNA-binding protein DND1 in tongue squamous cell carcinoma.” FEBS letters (2010) 58(18), pp.4114-4120.
PubMed

L. Jiang, X. Liu, Z. Chen, Y. Jin, C. E. Heidbreder, A. Kolokythas, A. Wang, Y. Dai and X. Zhou.
“MicroRNA-7 targets insulin-like growth factor 1 receptor (IGF1R) in tongue squamous cell carcinoma cells.” Biochemical Journal (2010) 432, pp.199\96205.
PubMed

Yang Dai and Xiaofeng Zhou. “Computational methods for the identification of microRNA targets.” Open Access Bioinformatics (2010) 2:29-39.
PubMed

Szilard Asztalos, Peter Gann, Meghan Hayes, Larisa Nonn, Craig Beam, Yang Dai, Elizabeth Wiley, and Debra Tonetti. “Gene expression patterns in the human breast after pregnancy.” Cancer Prevention Research. (2010) 3(3):301-311,2010.
PubMed

Cavallari H. Larisa, Vicki L. Groo, Marlos A. Viana, Yang Dai, Shitalben R. Patel, and Thomas D. Stamos. “Circulating aldosterone and mineralocorticoid receptor genotype are predictive of potassium response to spironolactone in heart failure”, Pharmacotherapy (2010) 30(1):1-9, 2010.
PubMed

Jonna Frasor, Aisha Weaver, Madhumita Pradhan, Yang Dai, Lance D. Miller, Chin-Yo Lin, and Adina Stanculescu. “Positive Crosstalk between Estrogen Receptor and NFKB in Breast Cancer” , Cancer Research (2009) 69(23):8918-8925.
PubMed

Peter Larsen and Yang Dai. “Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks.”, Proceedings of the 5th International Symposium on Bioinformatics Research and Applications (eds. I. Mandoiu, G. Narasimhan, and Y. Zhang). Lecture Notes in Computer Science, Springer Verlag, (2009) 5542 pp. 40-51.
To Springer

Shantanu Dutt, Yang Dai, Huan Ren, and Joel Fontanarosa. “Selection of Multiple SNPs in Case-Control Association Study Based on a Discretized Network Flow Approach.”, Proceedings of the First Conference of Bioinformatics and Computational Biology (BICoB 2009) , Lecture Notes in Computer Science, Springer Verlag, Vol. 5462, pp.1611-3349, 2009.
PDF

Yang Dai, Eyad Almasri, Peter Larsen, Guanrao Chen. “Structure Learning of Genetic Regulatory Networks Based on Knowledge Derived from Literature and Microarray Gene Expression Measurements.” , Book Chapter, Computational Methodologies in Gene Regulatory Networks (S. Das, D. Caragea, W. H. Hsu, S. M. Welch eds.), IGI Global, pp. 289-309, 2009.
To IGI Global

Peter Larsen, Eyad Almasri, Guanrao Chen and Yang Dai. “Incorporating Knowledge of Topology Improves Reconstruction of Interaction Networks from Microarray Data.” Proceedings of the 4th International Symposium on Bioinformatics Research and Applications, Lecture Notes in Computer Science (eds.by I.I. Mandoiu, Raj Sunderraman, and A. Zelikovsky), Springer Verlag, Vol. 4983, pp. 434-443, 2008.
To Springer

Eyad Almasri, Peter Larsen, Guanrao Chen and Yang Dai, “Incorporating Literature Knowledge in Baysian Network for Inferring Gene Networks with Gene Expression Data.” Proceedings of the 4th International Symposium on Bioinformatics Research and Applications , Lecture Notes in Computer Science (eds. by I.I. Mandoiu, Raj Sunderraman, and A. Zelikovsky), Springer Verlag, Vol. 4983, pp.184-195, 2008.
ACM Digital Library

Guanrao Chen, Peter Larsen, Eyad Almasri,and Yang Dai. “Rank-based edge reconstruction for scale-free genetic regulatory networks.” BMC Bioinformatics , 9:75, 2008.
PubMed

Oleksiy Karpenko, Lei Huang and Yang Dai. “A probabilistic meta-predictor for the MHC class II binding peptides.” Immunogenetics, 60:1, pp.25-36, 2008.
PubMed

Peter Larsen, Eyad Almasri, Guanrao Chen and Yang Dai. “A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments.” BMC Bioinformatics 8:317, 2007.
PubMed

Lei Huang, Oleksiy Karpenko, Naveen Murugan and Yang Dai. “Building a Meta-predictor for MHC Class II-Binding Peptides.”, Book Chapter, Immunoinformatics: Predicting Immunogenicity In Silico (Flower, D.R. ed.), Humana Press Inc., Totowa, NJ. pp. 355-364, 2007.
PubMed

Zhengdeng Lei and Yang Dai. “Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction.”, BMC Bioinformatics , 7:491, 2006.
PubMed

Peter Larsen, Eyad Almasri, Guanrao Chen and Yang Dai. “Correlated discretized expression score: a method for identifying gene interaction networks from time-course microarray expression data.” , Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp.5842-5845.
PubMed

Guanrao Chen, Peter Larsen, Eyad Almasri and Yang Dai. “Sample scale-free gene regulatory network using gene ontology.” , Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp.5523-5526.
PubMed

Robert E. Langlois, Alice Diec, Ognjen Perisic, Yang Dai and Hui Lu. “Improved protein fold assignment using support vector machines.”, International Journal of Bioinformatics Research and Applications , 1(3):319-334, 2006.
To Journal Site

Lei Huang and Yang Dai. “Direct prediction of T cell epitopes using SVM with novel sequence encoding schemes.” , Journal of Bioinformatics and Computational Biology , 4(1):93-107, 2006.
PubMed

Naveen Murugan and Yang Dai. “Prediction of MHC Class II binding peptides based on an iterative learning model.” , Immunome Research , 1:6, 2005.
PubMed

Zhengdeng Lei and Yang Dai. “An SVM-based system for predicting protein subnuclear localizations.”, BMC Bioinformatics, 6:291, 2005.
PubMed

J. Balcazar, Y. Dai and O. Watanabe. “Provably fast training algorithms for support vector machines.”;, Theory of Computing Systems, 2008. Vol.42. No.4, pp. pp 568–595.
To Springer

S. Kuroda, A.S. Virdi, Y. Dai, S, Shott and D. R. Sumner. “Patterns and localization of gene expression during intramembranous bone regeneration in the rat femoral marrow ablation model.” , Calcified Tissue International, 77(4):212-25, 2005.
PubMed

Zhengdeng Lei and Yang Dai. “A class of new kernels based on a matrix of high-scored pairs of k-peptides and its applications in prediction of protein sub-cellular localization.” , LNCS Transactions on Computational Systems Biology II, Springer-Verlag, pp.48-58, 2005.
PDF

Oleksiy Karpenko, Jianming Shi and Yang Dai. “Prediction of MHC class II binders using the ant colony search strategy.” Artificial Intelligence in Medicine, Vol. 35, pp.147-156, 2005.
PubMed

Aladino De Ranieri, Amarjit S. Virdi, Shinji Kuroda, S. Shott, Yang Dai and Dale R. Sumner. “Local application of rhTGF-beta2 modulates dynamic gene expression in a rat implant model.”, Bone , 36(5) pp.931-940, 2005.
PubMed

Zhengdeng Lei and Yang Dai. “A new kernel based on a matrix of high-scored pairs of tri-peptides and its applications in prediction of protein sub-cellular localization.” Proc. of International Workshop on Bioinformatics Research and Applications, Lecture Notes in Computer Science (LNCS), Springer-Verlag, Berlin, No.3515, pp.903-910, 2005. ACM Digital Library

Y. Dai, J. M. Shi and W.S. Yang. “Conical partition algorithm for maximizing the sum of several dc ratios”, Journal of Global Optimization , Vol.31, pp.253-270, 2005.
To Springer

L. Huang and Y. Dai. “A support vector machine approach for prediction of T cell epitopes.”, Proc. of the Third Asia-Pacific Bioinformatics Conference (APBC2005), Singapore, Jan. 17-21, 2005. pp.312-328, Imperial College Press.
PDF

D. Z. Chen, O. Daescu, Y. Dai, N. Katoh, J. Xu and X. Wu. “Efficient algorithms and implementations for optimizing the sum of linear fractional functions, with applications.”, Journal of Combinatorial Optimization , Vol.9, pp.69-90, 2005.
To Springer

A.S. Virdi, A.D. Ranieri, S. Kuroda, Y. Dai and D.R. Sumner. “Anabolic agents and gene expression around the bone implant interface.”, Journal of Musculoskeletal & Neuronal Interactions , 4(4), pp.388-389, 2004.
PubMed

R.E. Langlois, A. Diec, Y. Dai and H. Lu. “Kernel-based approach for protein fold prediction from Sequence.”, Proc. of the 26th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS2004), 2004.
IEEE

G. Chen and Y. Dai. “A new distance measurement for clustering time-course gene expression data.”, Proc. of the 26th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS2004) (CD-ROM), 2004.
IEEE Xplore Digital Library

Z. Lei and Y. Dai. “A novel approach for prediction of protein subcellular localization from sequence using fourier analysis and support vector machines”, Proc. of 4th ACM SIGKDD Workshop on Data Mining in Bioinformatics, Seattle, August 22, 2004, pp. 11-17.
ACM Digital Library

B. Liu, Y. Dai, X. Li, W. S. Lee and P. Yu. “Building text classifiers using positive and unlabeled examples”, Proc. of the Third IEEE International Conference on Data Mining(ICDM’03), Melbourne, Florida, November 19-22, 2003, pp.179-188.
ACM Digital Library

H-M. Lu, S. Gupta and Y. Dai. “Reduce large diagonals in kernel matrices through semidefinite programming”, Proc. of 3rd ACM SIGKDD Workshop on Data Mining in Bioinformatics, 2003, pp.57-62.

H-M. Lu, L. Huang and Y. Dai. “Application of support vector regression to Quantitative Structure-Activity Relationships(QSAR)”, Proc. of the 5th International Conference on Computational Biology and Genome Informatics, Carey, North Carolina, USA, September 26-30, 2003.

L. Huang, H-M. Lu and Y. Dai. “Feature selection of support vector regression for Quantitative Structure-Activity Relationships(QSAR)”, Proc. of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS ’03), Las Vegas, Nevada, June, 2003.

Y. Dai, S. Kim and M. Kojima. “Computing all nonsingular solutions of cyclic-n polynomial using polyhedral homotopy continuation methods”, Journal of Computational and Applied Mathematics , Vol.152, No.1-2, 2003, pp.83-97.
To Journal Site

Y. Dai and Mike Landis. “Feature selection in tumor classification using microarray gene expression data”, Proc. of International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS ’02), Las Vegas, Nevada, June 2002. PDF

A. Sutou and Y. Dai. “Global optimization approach to unequal sphere packing problems in 3D”, Journal of Optimization Theory and Applications, Vol.114, No.3, 2002, pp.671-694. To Springer

J. Balcazar, Y. Dai and Osamu Watanabe. “Provably fast support vector regression using random sampling”, Proc. of SIAM Workshop in Discrete Mathematics and Data Mining, Arlington, VA, April 2002, pp.19-29.
PDF

J. Balcazar, Y. Dai, and O. Watanabe. “Provably fast training algorithms for support vector machines”, Proc. of the first IEEE International Conference on Data Mining, IEEE Computer Society, 2001, pp.43–50.
IEEE Xplore Digital Library

J. Balcazar, Y. Dai, and O. Watanabe. “An application of a random sampling technique to primal-form maximal-margin classifiers”, Proc. of the 12th International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence, LNAI 2225, Springer-Verlag, 2001, pp119-134.
To Springer

Y. Dai and N. Katoh. “Generalized LMT-heuristics for several new classes of optimal triangulations”, Computational Geometry: Theory and Application 17, 2000, pp.51-68.
PDF

D. Z. Chen, O. Daescu, Y. Dai, N. Katoh, J. Xu and X. Wu. “Efficient algorithms and implementations for optimizing the sum of linear fractional functions, with applications”, Proc. of the 11th Annual SIAM-ACM Symposium on Discrete Algorithms (SODA), San Francisco, 2000, pp.707-716.
To Springer

A. Takeda, Y. Dai, M. Kojima and M. Fukuda. “Towards implementations of successive convex relaxation methods for nonconvex quadratic optimization problems”, in Approximation and Complexity in Numerical Optimization: Continuous and Discrete Problems, (P. M. Pardalos, Eds), 2000, pp. 489-510, Kluwer Academic Publishers.
To Springer