Version 1
: Received: 5 September 2024 / Approved: 5 September 2024 / Online: 11 September 2024 (16:21:08 CEST)
How to cite:
Sinha, S. Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells. Preprints2024, 2024090855. https://doi.org/10.20944/preprints202409.0855.v1
Sinha, S. Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090855. https://doi.org/10.20944/preprints202409.0855.v1
Sinha, S. Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells. Preprints2024, 2024090855. https://doi.org/10.20944/preprints202409.0855.v1
APA Style
Sinha, S. (2024). Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells. Preprints. https://doi.org/10.20944/preprints202409.0855.v1
Chicago/Turabian Style
Sinha, S. 2024 "Machine Learning Discoveries of Bcl-X Synergy in etc-1922159 Treated Colorectal Cancer Cells" Preprints. https://doi.org/10.20944/preprints202409.0855.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 BCL-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which BCL-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 BCL and X. In this research work, we cover combinations of BCL with Interleukin (IL), Selenbp1, TP53, caspase (CASP), mucin (MUC) and exosome (EXOSC).
Keywords
BCL; PorcupineinhibitorETC-1922159; Sensitivity analysis;Colorectal cancer
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
