1.精准健康：人类复杂疾病的遗传学研究。我们致力于解析与疾病相关的遗传学基础和功能研究，关注致病基因及遗传因子的解析。主要利用统计学、数学和数据挖掘的知识，开发生物信息学的算法与工具以研究疾病的致病机理。

2.精准健康：癌症组学研究。我们致力于开发前沿的算法以研究癌症的体细胞突变，探讨癌症成因，癌症基因的致病原理和在细胞中的影响，及开发深度学习算法以预测个性化的治疗方案。

3.脑相关疾病研究。我们关注三类脑相关疾病：精神类疾病，退行性疾病和认知相关疾病。开发及使用计算方法以分析和整合多组学数据，深入研究疾病的遗传学基础和疾病的相关性。

   

0710J3-生物信息学
0710Z2-计算生物学
0710Z1-基因组学

生物信息学

   

1999年9月-2003年7月 大连理工大学，生物工程专业，学士学位

2003年9月-2008年7月 中科院上海生命科学研究院，生物信息专业，博士学位

博士学位

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[1] Dan Shan, YinYing Wang, Yuan Chang, Hao Cui, Menghao Tao, Yixuan Sheng, Hongen Kang, Peilin Jia, Jiangping Song. Dynamic cellular changes in acute kidney injury caused by different ischemia time. ISCIENCE[J]. 2023, 26(5): http://dx.doi.org/10.1016/j.isci.2023.106646.
[2] Pan, Siyu, Kang, Hongen, Liu, Xinxuan, Lin, Shiqi, Yuan, Na, Zhang, Zhang, Bao, Yiming, Jia, Peilin. Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits. NUCLEIC ACIDS RESEARCH. 2022, 51(D1): D835-D844, http://dx.doi.org/10.1093/nar/gkac895.
[3] Wang, YinYing, Kang, Hongen, Xu, Tianyi, Hao, Lili, Bao, Yiming, Jia, Peilin. CeDR Atlas: a knowledgebase of cellular drug response. NUCLEIC ACIDS RESEARCH[J]. 2022, 50(D1): D1164-D1171, http://dx.doi.org/10.1093/nar/gkab897.
[4] Jia Peilin. Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity. Briefings in Bioinformatics. 2021, [5] Li, Bingrui, Pei, Guangsheng, Yao, Jun, Ding, Qingqing, Jia, Peilin, Zhao, Zhongming. Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types. ONCOGENE[J]. 2021, 40(28): 4686-4694, http://dx.doi.org/10.1038/s41388-021-01870-x.
[6] Pei, Guangsheng, Hu, Ruifeng, Jia, Peilin, Zhao, Zhongming. DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner. NUCLEIC ACIDS RESEARCH[J]. 2021, 49(W1): W131-W139, http://dx.doi.org/10.1093/nar/gkab429.
[7] Peilin Jia, Ruifeng Hu, Guangsheng Pei, Yulin Dai, YinYing Wang, Zhongming Zhao. Deep generative neural network for accurate drug response imputation. NATURE COMMUNICATIONS[J]. 2021, 12(1): https://doaj.org/article/4fb77b5018df4b45a1a0baa6a40058e4.
[8] Jia Peilin. Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.. Nucleic Acids Research. 2020, [9] Pei, Guangsheng, Hu, Ruifeng, Dai, Yulin, Zhao, Zhongming, Jia, Peilin. Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network. ONCOGENE[J]. 2020, 39(27): 5031-5041, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334101/.
[10] Jia, Peilin, Dai, Yulin, Hu, Ruifeng, Pei, Guangsheng, Manuel, Astrid Marilyn, Zhao, Zhongming. TSEA-DB: a trait tissue association map for human complex traits and diseases. NUCLEIC ACIDS RESEARCH[J]. 2020, 48(D1): D1022-D1030, https://www.webofscience.com/wos/woscc/full-record/WOS:000525956700131.
[11] Jia Peilin. Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.. Genome Research. 2020, [12] Jia, Peilin, Chen, Xiangning, Xie, Wei, Kendler, Kenneth S, Zhao, Zhongming. Mega-analysis of Odds Ratio: A Convergent Method for a Deep Understanding of the Genetic Evidence in Schizophrenia. SCHIZOPHRENIA BULLETIN[J]. 2019, 45(3): 698-708, http://dx.doi.org/10.1093/schbul/sby085.
[13] Pei, Guangsheng, Dai, Yulin, Zhao, Zhongming, Jia, Peilin. deTS: tissue-specific enrichment analysis to decode tissue specificity. BIOINFORMATICS[J]. 2019, 35(19): 3842-3845, http://dx.doi.org/10.1093/bioinformatics/btz138.
[14] Jia, Peilin, Pei, Guangsheng, Zhao, Zhongming. CNet: a multi-omics approach to detecting clinically associated, combinatory genomic signatures. BIOINFORMATICS[J]. 2019, 35(24): 5207-5215, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954662/.
[15] Jia, Peilin, Zhao, Zhongming. Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types. CELL REPORTS[J]. 2019, 26(2): 496-+, http://dx.doi.org/10.1016/j.celrep.2018.12.066.
[16] Guangsheng Pei, Fangfang Yan, Lukas M Simon, Yulin Dai, Peilin Jia, Zhongming Zhao. deCS: A tool for systematic cell type annotations of single-cell RNA sequencing data among human tissues. GENOMICS, PROTEOMICS & BIOINFORMATICS.