RESEARCH
Prediction of RNA-protein interaction
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Predicting RNA-binding potential of proteins using protein-protein interaction (PPI) networks. (SONAR) (Published) paper software
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Advanced RNA-binding protein classifier using protein sequences and optimized PPI network with state-of-art machine learning techniques. (HYDRA) (ongoing)
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Predicting RNA-binding regions in RNA-binding proteins with machine learning/deep learning technology. (ongoing)
Transcriptome-wide analysis of RNA-binding protein binding sites
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Identifying SRSF9 binding sites on HEK 293 cell line with eCLIP-seq protocol. (Published) paper
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Verifying novel RNA-binding proteins predicted by SONAR/SONAR+ and identifying binding sites on their target RNAs. (ongoing)
Application of statistics and AI technology in human diseases
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Predicting drug sensitivity of cancer samples from gene expression data (i.e. microarray, single-cell RNA sequencing).
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Predicting inflammatory bowel disease (IBD) from genomic and proteomic data on single cell level.
2018
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Tissue-Selective Restriction of RNA Editing of CaV1.3 by Splicing Factor SRSF9
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Huang H, Kapeli K, Jin W, Wong YP, Arumugam TV, Koh JH, Srimasorn S, Mallilankaraman K, Chua JJ, Yeo GW, Soong TW.
Nucleic Acids Research. 2018 May 4.
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Context-Dependent and Disease-Specific Diversity in Protein Interactions within Stress Granules
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Markmiller S, Soltanieh S, Server KL, Mak R, Jin W, Fang MY, Luo EC, Krach F, Yang D, Sen A, Fulzele A, Wozniak JM, Gonzalez DJ, Kankel MW, Gao FB, Bennett EJ, Lecuyer E, Yeo GW.
Cell. 2018 Jan 25;172(3):590-604.
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2016
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SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.
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Brannan KW, Jin W (co-first), Huelga SC (co-first), Banks CA, Gilmore JM, Florens L, Washburn MP, Van Nostrand EL, Pratt GA, Schwinn MK, Daniels DL and Yeo GW.
Molecular Cell. 2016 Oct 20;64(2):282-93.
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Rbfox2 function in RNA metabolism is impaired in hypoplastic left heart syndrome patient hearts.
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Verma SK, Deshmukh V, Nutter CA, Jaworski E, Jin W, Wadhwa L, Abata J, Ricci M, Lincoln J, Martin JF, Yeo GW and Kuyumcu-Martinez MN.
Scientific Reports. 2016;6.
PUBLICATION
Wenhao considers himself as a bioinformatician equipped with strong passion and good skills in machine learning. He is enthusiastic about applying machine learning technology, including deep learning, to biomedical problems.
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He set up his knowledgebase of biology, statistics and computer science in his undergraduate study in Zhejiang University, during which he also by accident developed an intense interest in machine learning algorithms. Thus, in his PhD study in National University of Singapore , he started a machine learning project on a biological problem and has so far worked on it for almost three years. This not only enhances his machine learning skills but also deepens his understanding of the power of machine learning in biological/medical scenario.
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In his free time, Wenhao plays soccer, climbing and is also an amateur marathon runner. He has completed 4 full marathon races and 1 trail run race so far.
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SUMMARY
Let's Talk
My ADDRESS
2880 Torrey Pines Scenic Dr., Room 3114/3115
La Jolla, CA 92037
Email: w2jin@ucsd.edu
Tel: +1 (858) 534-9322 (office)
For any general inquiries, please fill in the following contact form:
Passionate about machine learning and bioinformatics. And soccer.
Wenhao JIN Ph.D
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Post-Doctoral Researcher, Bioinformatics
Department of Cellular and Molecular Medicine
University of California, San Diego
PAST:
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Ph.D. Bioinformatics (CAP 4.50/5.00)
2018, National University of Singapore
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B.Sc. Bioinformatics (GPA 3.86/4.00)
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