Protein target prediction using random forests and reliability-density neighbourhood analysis
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Updated
May 6, 2020 - Python
Protein target prediction using random forests and reliability-density neighbourhood analysis
A cheminformatics package to perform Applicability Domain of molecular fingerprints based in similarity calculation.
Classification models for hemolytic nature and hemolytic activity predictions in peptide/protein sequences
Reference implementation of the Distance-Based Boolean Applicability Domain for HTS datasets
Allows to visualize and analyze if the molecules of the test set and of an external set are contained in the convex hull defined by the molecules of the training set.
Reference implementation of the Vanishing Ranking Kernels (VRK) method
pDILI_v1 is a python package that allows users to predict the association of drug-induced liver injury of a small molecule (1 = RISKy, 0 = Non-RISKy) and also visualize the molecule.
This contains an end-to-end ML pipeline that predicts the activity (active vs inactive) of chemical compounds against the Human Angiotensin-Converting Enzyme, a major hypertension target.
Applicability Domain Methods of Viral Load and CD4 Lymphocytes.
Why mode-choice models trained in city A fail in city B: a structural lower bound on transferability. Empirical instantiation, on the 34,858 French commune mobility panel, of Theorem 1 of the sibling materials-applicability-bound paper. Target: Transportation Research Part B (TR-B).
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