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hla-antibody-analyzer

Electron app for performing HLA antibody analysis of reactivity panels

hla-bind

Amino acid embedding and Convolutional Neural Network for HLA Class I-peptide binding prediction

hla-la

Fast HLA type inference from whole-genome data

hla_epitope_prediction_from_wes

HLA epitope prediction from WES.

hmd

HMD (Hyperfast Molecule Deriver) is a molecular structure generator.

hmmer

HMMER: biological sequence analysis using profile HMMs

hmsms

Hidden Markov model based peptide fragmentation predictor

hnswlib

Header-only C++/python library for fast approximate nearest neighbors

hob

Machine learning model for predicting Human Oral Bioavailability

homoganize

Aimed to generate new molecules from multiple data set inputs

hotnet2

HotNet2 is an algorithm for finding significantly altered subnetworks in a large gene interaction network

hpe

Heterogeneous Predictors Ensembling for Quantitative Toxicity Prediction

hpka

hplc_data_analysis

Automated HPLC data analysis and visualization. Intermediate step to provide feedback for looped reaction optimization.

htmd

HTMD: Programming Environment for Molecular Discovery

hts_shrink

Reference implementation of the Distance-Based Boolean Applicability Domain for HTS datasets

htsvis

A web app for exploratory data analysis of high-throughput screens (HTS)

human-oral-bioavailability

ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability

hybridclms

Supporting code for the paper «Leveraging molecular structure and bioactivity with chemical language models for drug design»

hybridmodel-covid-19-prediction

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.

hybridtox2d

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.

hyfactor

Official code for the publication "HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder".

hyper-parameters-ga-optimization

Hyperparameters optimization using genetic algorithms

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

ibawds

A collection of useful functions and datasets for the Data Science Course at IBAW in Lucerne.

ic50_calcs

Jupyter notebook for the analysis of cell viability assays. Generates bar graphs for %cell survival and uses a sigmoid regression to estimate the IC50.

icassp_frwl

Fast Spectral Clustering based on RandomWalk Laplacian (FRWL) for Large Scale Clustering- ICASSP 2020

iclr19-graph2graph

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (ICLR 2019)

icml18-jtnn

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

icn3d-mmtf