chen bo lin


http://teacher.nwpu.edu.cn/en/blchen

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The basic information

chen bo lin

School of Computer Science

Postgraduate

Doctor

Associate Professor

blchen@nwpu.edu.cn

Education And Teaching


Course # for international Master students: M10M22031

Name: Introduction to Bioinformatics

Previous course: Data structure

Introduction: Bioinformatics is a new interdisciplinary course, which is related to computer science, mathematics and biology and so on. It belongs to one of the frontiers of natural science and information science. This course introduces the basic knowledge, research methods, and their applications in the area of computational biology. The course is not a simple combination of computer science, mathematics and biology, but rather a novel method to learn computer science based on the basic biological knowledge and mathematic tools. The course aims at helping students understand the hot issues and research methods in the field of life science. Furthermore, it focuses on helping students learn how to analyze problems and how to solve those problems, thereby improving students' independent research ability. The course is open to postgraduate students (both Master and PhD) who are enrolled to do a research degree in the School of Computer Science.  

Reference:

[1] Neil C. Jones and Pavel A. Pevzner. An introduction to bioinformatics algorithms. The MIT press, 2004.

[2] Mourad Elloumi and Albert Y. Zomaya Algorithms in computational molecular biology, Wiley, 2011. 

Admission Information

Positions for master students in Computational Biology are now available. I am interested in recruiting high-caliber undergraduate students who have expertise in the areas of computer science, or applied mathematics. The students who are self-motivated and have publications in relevant scientific journals are encourages to apply.


Scientific Research

Research areas

Research areas include but not limit to computational and systems biology, genomic and proteomic data analysis, disease gene identification, protein complex identification, dynamic network analysis, gene methylation data analysis etc.


Grant Information

[4] The mechanism of carcinogenesis and the identification of driving factors based on dynamic network analyses

supported by the National Natural Science Foundation of China (NSFC), No. 61972320, CNY 620K, 2020-2023

[3] Feature self-learning method for complex disease gene identification

supported by the National Natural Science Foundation of China (NSFC), No. 61602386, CNY 190K, 2017-2019

[2] Identifying disease related genes by using multiple data integration and logistic regression

supported by the Northwestern Polytechnical University (NPU) under the name of the fundamental research funds for the central Universities (as the additional support for the startup fund for young professionals), No. 3102015JSJ0011, CNY 100K, 2015-2016

[1] Identifying disease related genes by using multiple data integration and logistic regression

supported by the Northwestern Polytechnical University (NPU) under the name the startup fund for young professionals, No. 3102015QD029, CNY 80K, 2015-2016


Academic Achievements

Refereed Journal  Papers


[18] B Chen, L Gao, XQ Shang*. A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship. BMC Genomics (accepted, Impact factor: 3.730)

[17] B Chen, MT Yang, L Gao, T Jiang, XQ Shang. A functional network construction method to interpret the pathological process of colorectal cancer. International Journal of Data Mining and Bioinformatics 2020, 23(3), 251-264 (Impact factor: 0.772)

[16] C Aouiche(#), B Chen(#,*), XQ Shang. Predicting Stage-Specific Recurrent Aberrations From Somatic Copy Number Dataset. Frontiers in Genetics 2020, 11, 160 (Impact factor: 3.258)

[15] C Aouiche(#), B Chen(#,*), XQ Shang. Predicting stage-stepcific cancer related genes and their dynamic modules by integrating multiple datasets. BMC Bioinformatics 2019, 20, 194 (Impact factor: 2.213)

[14] C Aouiche(#), XQ Shang*, B Chen(#,*). Copy number variation related disease genes. Quantitative Biology 2018, 6(2): 99-112.

[13] B Chen, XQ Shang, M Li, JX Wang, FX Wu*. Identifying individual-cancer-related genes by rebalancing the training samples. IEEE Transactions on Nanobioscience2016, 15(4), 309-315. (Impact factor: 1.969) 

[12] XQ Shang, Y Wang, B Chen*. Identifying essential proteins based on dynamic PPI networks and RNA-Seq datasets. SCIENCE CHINA Information Science 2016, 070106. (Impact factor: 0.885)

[11] T Jiang, ZH Li, XQ Shang, B Chen, WB Li, ZL Yin. Constrained query of order-preserving submatrix in gene expression data. Frontiers of Computer Science in China. 2016. Accepted.

[10] B Chen, M Li, JX Wang, XQ Shang and FX Wu. A fast and high performance multiple data integration algorithm for identifying human disease genes. BMC Medical Genomics 2015,8(Suppl 3): S2. (Impact factor: 2.873) 

[9] B Chen, M Li, JX Wang, FX Wu. Disease gene identification by using graph kernels and Markov random fields. SCIENCE CHINA Life Sciences 2014, 57(11), 1054-1063. (Impact factor: 1.512)

[8] W Fan, B Chen, G Selvaraj and FX Wu. Discovering biological patterns from short time-series gene expression profiles with integrating PPI data. Neurocomputing 2014, 145, 3-13. (Impact factor: 2.005, EI Compendex)

[7] B Chen, JX Wang, M Li and FX Wu. Identifying disease genes by integrating multiple data sources. BMC Medical Genomics 2014, 7(Suppl 2): S2. (Impact factor: 3.914)

[6] J Sun, B Chen and FX Wu. An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs. Proteome Science 2014 12:18. (Impact factor: 1.878, equally contribution as the first author)

[5] B Chen, W Fan, J Liu and FX Wu. Identifying protein complexes and functional modules – from static PPI networks to dynamic PPI networks. Briefings in Bioinformatics2014, 15(2), 177-194. (Impact factor: 5.919)

[4] B Chen and FX Wu. Identifying protein complexes based on multiple topological structures in PPI networks. IEEE Transactions on Nanobioscience 2013,12(3), 165-172. (Impact factor: 1.768, EI Compendex)

[3] J Shi, B Chen and FX Wu. Unifying protein inference and peptide identification with feedback to updated consistency between peptides. Proteomics 2013, 13(2), 237-247. (Impact factor: 3.973)

[2] B Chen, J Shi, S Zhang and FX Wu. Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy. Proteomics2013, 13(2), 269-277. (Impact factor: 3.973)

[1] Z Yuan, J Shi, W Lin, B Chen and FX Wu. Features-based deisotoping method for tandem mass spectra. Advances in Bioinformatics 2011, Article ID 210805, 12 pages.

P.S. All the impact factors are based on the state of the publication year.


 Refereed Conference Papers:

[16] B Chen, T Wang, and X Shang*. Identification and Analysis of Genes Involved in Stages of Colon Cancer. Intelligent Computing Theories and Application (ICIC 2019), International Conference on Intelligent Computing,accepted. (EI Compendex)

[15] B Chen, L Gao, and X Shang*. A machine learning based method to identify differentially expressed genes. Intelligent Computing Theories and Application (ICIC 2020), International Conference on Intelligent Computing,accepted. (EI Compendex)

[14] B Chen, L Gao, and X Shang*. Identifying functional evolution processes according to the pathological stages of colorectal cancer. Bioinformatics and Biomedicine (BIBM), 2019 IEEE International Conference on, 193-196. (EI Compendex)

[13] B Chen, L Gao, and X Shang*. Identifying Differentially Expressed Genes Based on Differentially Expressed Edges. Intelligent Computing Theories and Application (ICIC 2019), International Conference on Intelligent Computing,105-115. (EI Compendex)

[12] B Chen(#,*), C Aouiche(#), and X Shang. Integrating multiple datasets to discover stage-specific cancer related genes and stage-specific pathways. Bioinformatics and Biomedical Engineering (IWBBIO 2019), International Conference on, 240-250. (EI Compendex)

[11] P Luo, L Tian,  B Chen, Q Xiao, and FX Wu*. Predicting disease genes from clinical single sample-based PPI networks. Bioinformatics and Biomedical Engineering (IWBBIO 2018), International Conference on, 247-258. (EI Compendex)

[10] P Luo, L Tian, B Chen, Q Xiao, and FX Wu*. Predicting gene-disease associations with manifold learning. Bioinformatics Research and Applications (ISBRA 2018), International Symposium on, 265-271. (EI Compendex)

[9] B Chen, Y Jin, and X Shang*. Net2Image: A network representation method for identifying cancer-related genes Bioinformatics Research and Applications (ISBRA 2017), International Symposium on, 337-343. (EI Compendex)

[8] B Chen, XQ Shang, M Li, J Wang and FX Wu. A two-step logistic regression based algorithm for identifying individual-cancer-related genes. Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, 195-200. (EI Compendex)

[7] B Chen, M Li, J Wang and FX Wu. A logistic regression based algorithm for identifying human disease genes.Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on, 197-200. (EI Compendex)

[6] B Chen, J Wang and FX Wu. Prioritizing human disease genes by multiple data integration. Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on, 621. (EI Compendex)

[5] B Chen, J Shi and FX Wu. Not all protein complexes exhibit dense structures in S. cerevisiae PPI network. Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on, 470-473. (EI Compendex)

[4] B Chen, Y Yan, J Shi, S Zhang and FX Wu. An improved graph entropy-based method for identifying protein complexes. Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on, 123-126. (EI Compendex)

[3] J Shi, B Chen and FX Wu. Improving accuracy of peptide identification with consistency between peptides. Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on, 191-196. (EI Compendex)

[2] B Chen, L Liu and FX Wu. Inferring gene regulatory networks from multiple time course gene expression datasets. Systems Biology (ISB), 2011 IEEE International Conference on, 12-17. (EI Compendex)

[1] B Chen. Topological patterns identification for sneak circuit analysis. Reliability, Maintainability and Safety, 2009, ICRMS 2009. 8th International Conference on, 133-137. (EI Compendex)

 


Professional Activities