Precision Medicine Target-Drug Selection in Cancer
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Updated
Aug 8, 2017 - Python
Precision Medicine Target-Drug Selection in Cancer
Subclassification of disease states based on the intersection of literature and expression
TractLearn is a Manifold Learning Toolbox for precision medicine. The first application is for Diffusion-Weighted MRI quantitative analysis.
Neural text summarization for document retrieval
Text mining cancer biomarkers for the CIVIC database
Code for paper Multiomics dynamic learning enables personalized diagnosis and prognosis for pan-cancer and cancer-subtypes
An Artificial Neural Network-based discriminator for validating clinically significant genomic variants
Easy to use web interface for biologists to look for genetic variants and understand their deleteriousness using DITTO scores.
mity: A highly sensitive mitochondrial variant analysis pipeline for whole genome sequencing data
Patient-Specific Modeling in Python
Code to accompany the paper: ”ProteinNetworkSight efficiently transforms co-expressed protein lists into interactive networks and offers suggestions for their modifications”
Computational phenotyping used in the paper "Explainable AI on H&E Predicts Docetaxel Benefit for High-Risk Localized Prostate Cancer in RTOG 0521"
Services and guidelines for normalizing disease terms
Services and guidelines for normalizing drug and other therapy terms
Services and guidelines for normalizing genes
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