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Electron app for performing HLA antibody analysis of reactivity panels
Amino acid embedding and Convolutional Neural Network for HLA Class I-peptide binding prediction
Fast HLA type inference from whole-genome data
HLA epitope prediction from WES.
HMD (Hyperfast Molecule Deriver) is a molecular structure generator.
HMMER: biological sequence analysis using profile HMMs
Hidden Markov model based peptide fragmentation predictor
Header-only C++/python library for fast approximate nearest neighbors
Machine learning model for predicting Human Oral Bioavailability
Aimed to generate new molecules from multiple data set inputs
HotNet2 is an algorithm for finding significantly altered subnetworks in a large gene interaction network
Heterogeneous Predictors Ensembling for Quantitative Toxicity Prediction
Automated HPLC data analysis and visualization. Intermediate step to provide feedback for looped reaction optimization.
HTMD: Programming Environment for Molecular Discovery
Reference implementation of the Distance-Based Boolean Applicability Domain for HTS datasets
A web app for exploratory data analysis of high-throughput screens (HTS)
ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability
Supporting code for the paper «Leveraging molecular structure and bioactivity with chemical language models for drug design»
To accurately predict the regional spread of COVID-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia. The results show that the proposed model closely replicates test data. It not only provides accurate predictions but also estimates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model accounting for data limitation. The parameters of the hybrid models were optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict COVID-19 spread under consideration of containment policies, is capable of being used for policy assessment, planning and decision-making.
In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.
Official code for the publication "HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder".
Hyperparameters optimization using genetic algorithms
Distributed Asynchronous Hyperparameter Optimization in Python
A collection of useful functions and datasets for the Data Science Course at IBAW in Lucerne.
Jupyter notebook for the analysis of cell viability assays. Generates bar graphs for %cell survival and uses a sigmoid regression to estimate the IC50.
Fast Spectral Clustering based on RandomWalk Laplacian (FRWL) for Large Scale Clustering- ICASSP 2020
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (ICLR 2019)
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
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.