Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 5 September 2024 / Approved: 17 September 2024 / Online: 18 September 2024 (07:13:19 CEST)

How to cite: Sinha, S. Machine Learning Discoveries of Interleukin-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024091353. https://doi.org/10.20944/preprints202409.1353.v1 Sinha, S. Machine Learning Discoveries of Interleukin-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024091353. https://doi.org/10.20944/preprints202409.1353.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 Inter- leukin (IL)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which IL-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the IL and X. In this research work, we cover combinations of IL with nuclear factor κ B (NFκB), Potassium ion channel sub- family members (KCN), mucin (MUC), TP53, STAT, TNF receptor as- sociated factor (TRAF), STEAP4 metalloreductase, STEAP3 metalloreductase, ATP- binding cassette (ABC) transporters and tumor necrosis factor (TNF).

Keywords

  Interleukin(IL),PorcupineinhibitorETC-1922159,Sensitivityanalysis, Colorectal cancer.  

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

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