Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Discoveries of Wnt-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells

Version 1 : Received: 5 September 2024 / Approved: 5 September 2024 / Online: 5 September 2024 (10:56:38 CEST)

How to cite: Sinha, S. Machine Learning Discoveries of Wnt-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090453. https://doi.org/10.20944/preprints202409.0453.v1 Sinha, S. Machine Learning Discoveries of Wnt-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090453. https://doi.org/10.20944/preprints202409.0453.v1

Abstract

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Wnt-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the Wnt and X. In this research work, we cover combinations of Wnt with Achaete-scute complex homolog 2 (ASCL2), ATP- binding cassette (ABC) domain transporters, Interleukin (IL), ubiquitin conjugating enzyme E2 (UBE2) family, exosome (EXOSC), caspase (CASP), TP53 and B-cell lymphoma (BCL) family.

Keywords

WNT; PorcupineinhibitorETC-1922159; Sensitivityanalysis; Colorectal cancer

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

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