ELISL: early-late integrated synthetic lethality prediction in cancer

Motivation<br/><br/>Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection...

Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model

Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H<sub...