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AuthorHephzibah Cathryn, R.
AuthorUdhaya Kumar, S.
AuthorYounes, Salma
AuthorZayed, Hatem
AuthorGeorge Priya Doss, C.
Available date2022-12-15T08:24:43Z
Publication Date2022
Publication NameAdvances in Protein Chemistry and Structural Biology
ResourceScopus
URIhttp://dx.doi.org/10.1016/bs.apcsb.2022.05.002
URIhttp://hdl.handle.net/10576/37334
AbstractOver the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term. 2022 Elsevier Inc.
SponsorThe authors would like to thank the management of the Vellore Institute of Technology, India, and Qatar University, Qatar, for providing the necessary research facilities and encouragement to carry out this study. H.C.R. contributed to manuscript writing, interpretation of datasets, and bioinformatic analysis. U.K.S. S.Y. G.P.D.C. and H.Z. supervised the entire study and were involved in study design, the acquisition, analysis, and manuscript drafting. The manuscript was reviewed and approved by all the authors. The authors declare that the study done has no conflict of interest.
Languageen
PublisherElsevier
SubjectBioinformatics
Cancer research
Databases
Deep learning
Drug discovery
Drug interactions
Gene expression profiling
Microarray data
Microarray experiment
Microarray platforms
Protein-protein interaction, Differentially expressed genes
TitleA review of bioinformatics tools and web servers in different microarray platforms used in cancer research
TypeBook chapter
Pagination85-164
Volume Number131
dc.accessType Abstract Only


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