DPI_CDF: druggable protein identifier using cascade deep forest.

ABSTRACT:

Background

Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

Methods

In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and composit

SUBMITTER: Arif M 

PROVIDER: S-EPMC11334562 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature