The International Conference on Intelligent Biology and Medicine (ICIBM) 2020: Data-driven analytics in biomedical genomics-Reference-Cited by-同舟云学术

The International Conference on Intelligent Biology and Medicine (ICIBM) 2020: Data-driven analytics in biomedical genomics

Published:2020-12 Issue:S11 Volume:13 Page:
ISSN:1755-8794
Container-title:BMC Medical Genomics
language:en
Short-container-title:BMC Med Genomics

Author:

Shi Xinghua,Zhao Zhongming,Wang Kai,Shen Li

Abstract

AbstractThis editorial summarizes eight research articles included in this supplement issue for the 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) conference, that was held on August 9-10, 2020 (virtual conference), with a topic on data-driven analytics in biomedical genomics. These articles cover a wide range of topics in medical genomics that focus on integrative analysis of genomics data together with other types of data toward understanding complex human diseases, including cancer. With the growing importance of data analytics in biomedical science, we expect this collection of research articles provides scientific discussions in this direction.

Publisher

Springer Science and Business Media LLC

Subject

Genetics(clinical),Genetics

Link

http://link.springer.com/content/pdf/10.1186/s12920-020-00833-7.pdf

Reference8 articles.

1. Zhan X, Liu Y, Yu CY, Wang T-F, Zhang J, Ni D, Huang K. A pan-kidney cancer study identifies subtype specific perturbations on pathways with potential drivers in renal cell carcinoma. BMC Med Genomics. 2020;13(S11). https://doi.org/10.1186/s12920-020-00827-5.

2. Li B, Dong J, Yu J, Fan Y, Shang L, Zhou X, Bai Y. Pinpointing miRNA and genes enrichment over trait-relevant tissue network in Genome-Wide Association Studies. BMC Med Genomics. 2020;13(S11). https://doi.org/10.1186/s12920-020-00830-w.

3. Dai Y, O’Brien TD, Pei G, Zhao Z, Jia P. Characterization of genome-wide association study data reveals spatiotemporal heterogeneity of mental disorders. BMC Med Genomics. 2020;13(S11). https://doi.org/10.1186/s12920-020-00832-8.

4. Ahmed KT, Park S, Jiang Q, Yeu Y, Hwang T, Zhang W. Network-based drug sensitivity prediction. BMC Med Genomics. 2020;13(S11). https://doi.org/10.1186/s12920-020-00829-3.

5. Jin Y-J, Aycheh HM, Han S, Chamberlin J, Shin J, Byun S, Lee Y. Differential alternative splicing between hepatocellular carcinoma with normal and elevated serum alpha-fetoprotein. BMC Med Genomics. 2020;13(S11). https://doi.org/10.1186/s12920-020-00836-4.

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