EFG | Epigenetics & Function Group

27. Tang B., Chen Y., Wang Y., Nie J. A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma. BioMed Research International, 2021:7865856.

26. Tang B., Wang Y., Chen Y., Li M., Tao Y. A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression. Frontiers in Cell and Developmental Biology, 2021(8), ArticleID: 620746. (SCI:000609417100001)

25. Jin V., Wang J., Tang B., eds. (2020). Integration of Multisource Heterogenous Omics Information in Cancer. Book Press: Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-448-4.

24. Tang B., Pan Z., Kang Y. and Khateeb A. Recent Advances of Deep Learning in Bioinformatics and Computational Biology. Frontiers in Genetics, 2019(10), ArticleID: 214. (SCI:000462600500001)

23. Tang B. Inference of crosstalk effects between DNA methylation and lncRNA regulation in NSCLC. BioMed Research International, 2018(2018), ArticleID: 7602794. (SCI:000436191800001)

22. Tang B, et al. Advances in genomic profiling and analysis of 3D chromatin structure and interaction. MDPI Genes, 2017, 8(9), 223. (SCI:000412086900014)

21. Tang B, et al. Integration of DNA methylation and gene transcription across nineteen cell types reveals cell type-specific and genomic region-dependent regulatory patterns. NPG Scientific Reports, 2017:7, ArticleID:3626. (SCI:000403314500074)

20. Tang B, et al. COPAR: a ChIP-seq Optimal Peak AnalyzeR. BioMed Research International, 2017(2017), ArticleID: 5346793. (SCI:000398684700001)

19. Tang B. Toward optimization-oriented NGS peak alignment within the context of Precision Medicine Initiative. IEEE Int. Conf. Bioinform. Biomed., 2016:1848-1850, ArticleNumber:7822799. (EI:20170803377545)

18. Tang B. META2: Intercellular DNA methylation pairwise annotation and integrative analysis. BioMed Research International, 2016(2016), ArticleID: 1597489. (SCI:000391987100001)

17. Tang B, et al. Power spectrum-based genomic feature extraction from high-throughput ChIP-seq sequences. Intelligent Computing Theories and Application, Springer LNCS 2016:439-447. (EI:20163002641471)

16. Tang B. Genomic feature extraction and comparison based on global alignment of ChIP-sequencing data. Bioengineered, 2016 Sep 30:1-8. (SCI:000402878800016)

15. Tang B. DMAK: A curated pan-cancer DNA methylation annotation knowledgebase. Bioengineered, 2016, 8(2):182-190. (SCI:000399530000015)

14. Tang B, et al. Cross-cell DNA methylation annotation and analysis for pan­cancer study. Bang. J. Pharmac., 2016. (SCI:000378855100008)

13. Tang B, et al. Inferring the interplay landscape between DNA methylation and transcription regulatory activities. P. J. of Pharmaceu. Sci., 2015. (SCI:000350521500011)

12. Tang B, et al. Inference of gene regulatory networks in human cancer. In Statistical Diagnostics for Cancer - Analyzing High-Dimensional Data. Wiley-Blackwell Press, 2013; (Book chapter)

11. Tang B, et al. Cancer omics: From regulatory networks to clinical outcomes. Elsevier Cancer Letters, 2013. (SCI:000327293400018)

10. Tang B, et al. Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes. NPG Scientific Reports, 2012. (SCI:000311349800002)

9. Kennedy BA, Deatherage DE, Gu F, Tang B, Chan MWY, Nephew KP, Huang THM, Jin VX. ChIP-seq defined genome-wide map of TGFß/SMAD4 targets: implications with clinical outcome of ovarian cancer. PLOS ONE 6: e22606, 2011. (SCI:000293172900056)

8. Tang B, et al. Integrative identification of core genetic regulatory modules via a structural model-based clustering method. I.J. Computational Biology and Drug Design, 2011. (PubMed ID:21712564)

7. Tang B, et al. Toward comprehensive feature extraction from high-throughput NGS sequences. IEEE Procs of Computational Biology & Bioinformatics, 2011.

6. Tang B, et al. A weighted structural model clustering approach for identifying and analyzing core genetic regulatory modules. IEEE Procs of Bioinformatics & Biomedicine, 2010. (EI:20110913708841)

5. Tang B, et al., Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern. BMC Systems Biology, 2010. (SCI:000208294800002)

4. Tang B, et al., An information and combinatorial theories-based framework for integrative inference and analysis of genetic regulatory networks. Lecture Notes in Operation Research, LNOR 11, 2009. (Book chapter) (SCI:000281131900050)

3. Tang B, et al., Model-based identification & adaptive control of the core module in a typical cell cycle pathway via network & system control theories. Advances in Complex Systems, 2009. (SCI:000264302200003)

2. Tang B, et al., In silico identification & adaptive control of the motif in the mammalian G1/S cell cycle pathway. IEEE Procs of iCBBE, 2008. (EI:20083711531331)

1. Tang B, et al., System stability via stepping optimal control algorithm: theory & applications. Lecture Notes in Computer Science, LNAI 4874, 2007. (Book chapter) (EI:20080411056817)

Research Fundings

8. 2019-2021: Municipal Key Science & Technology Program (No. CE20195023);

7. 2019-2021: Fundamental Research Funds for China Central Universities (No. 2019B22414);

6. 2016-2019: Natural Science Foundation of Jiangsu, China (No. BE2016655);

5. 2016-2019: Natural Science Foundation of Jiangsu, China (No. BK20161196);

4. 2016-2018: Fundamental Research Funds for China Central Universities (No. 2016B08914);

3. 2016-2018: Municipal Key Science & Technology Program (No. CE20155050);

2. 2016-2016: NSFC-Guangdong Mutual Funds for Super Computing Program (2nd Phase);

1. Consortium Project: Open Cloud Consortium sponsored project resource, which supported in part by grants from Gordon and Betty Moore Foundation and the National Science Foundation (USA) and major contributions from OCC members.

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