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Modeling and prediction of drug toxicity from chemical structure
Predicting compound toxicity using DeepChem and the Tox21 dataset
Collect datasets for computational toxicology
Package of predictive models for targets of toxicological interest to the HeCaToS project.
Bootstrap Methods for ToxCast High Throughput Screening Data
Differential analysis of Toxcast results for drug pair with different levels of liver toxicity - Troglitazone (mostDILI) vs Rosiglitazone Maleate (lessDILI)
Evaluation of value added by Toxcast assay "read-outs" to prediction of DILI/hepatoxicity class of pharmaceutical drugs
ToxDL: Toxicity predictor with deep learning
The toxEval R-package includes a set of functions to analyze, visualize, and organize measured concentration data as it relates to chosen biological effects benchmarks. See http://usgs-r.github.io/toxEval for more details
Code for paper "ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning"
https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification
Collection of machine learning models for predicting toxicity of molecules
A Graph convolution neural network based toxicity prediction, project of SJTU-CS410 Artificial Intelligence(B),2020 Fall
Predicting whether chemicals are toxic or not by using the best machine learning model.
Computational hazard assessment of MeOx nanoparticle toxicity using machine learning
With new chemicals being synthesized every day, toxicity prediction of newly synthesized chemicals is mandatory before they could be released in the market. For a long time, *in-vivo* methods have been used for toxicity prediction which involves studying bacteria, human cells, or animals. With thousands of new chemicals being synthesized every day, it is not feasible to detect toxicity with traditional laboratory animal testing. One great alternative for *in-vivo *methods is the *in-silico* techniques that have great potential to reduce time, cost, and animal testing involved in detecting toxicity. ToxCast dataset is one of the greatest data available in the field of toxicogenomics. ToxCast has data for approximately 9,000 chemicals with more than 1500 high-throughput assay endpoints that cover a range of high-level cell responses. In this challenge, you will have access to a prepared subset of data from ToxCast.
A statistical package, in R, used in cancer research to analyze toxicity of drugs on cancer gene expression across large-scale toxicogenomic datasets.
This repo contains the code for generating the ToxiGen dataset, published at ACL 2022.
ToxiM: A Toxicity Prediction Tool for Small Molecules
Toxicity prediction models
ToxTree is a machine learning based model to predict hERG and Nav1.5 cardiotoxicity of a molecular compound at the outset of drug development process.
Python code for learning Molecular Dynamics simulations
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.