Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 5 September 2024 / Approved: 11 September 2024 / Online: 12 September 2024 (02:52:34 CEST)

How to cite: Sinha, S. Machine Learning Discoveries of DNA Repair-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090885. https://doi.org/10.20944/preprints202409.0885.v1 Sinha, S. Machine Learning Discoveries of DNA Repair-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090885. https://doi.org/10.20944/preprints202409.0885.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 DNA repair gene-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings 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 determine the mechanism of functioning between the Wnt and X. In this re- search work, we cover combinations of RAD with X-ray repair cross complementing (XRCC) family, 5’-3’ exoribonuclease 2 (XRN2), NFKB repressing factor (NKRF), B cell CLL/lymphoma (BCL) family, exosome component (EXOSC) family, FA comple- mentation group (FANC) family and XRCC with EXOSC.

Keywords

DNA repair; Porcupine inhibitor ETC-1922159; Sensitivity analysis; Colorectal cancer

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

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