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adaptedgraphdta

adhce

arrayedcrisprscreener

The goal of arrayedCRISPRscreener is to simulate arrayed CRISPR screening data for the purpose of benchmarking data analysis tools as well as power calculation.

awesome-drug-discovery-knowledge-graphs

A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge graph perspective" (Briefings in Bioinformatics, 2022)

awesome-drug-pair-scoring

Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)

awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

biology-for-ai

learning biology syllabus, geared for machine learning folks

biomedical-kg-topological-imbalance

Code to accompany the "Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs" (Briefings in Bioinformatics, 2022)

chemicalx

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

ckd_molecular_stratification

convcaps-dr

Tensorflow-Keras implementation of deep Convolutional Capsule Networks with Dynamic Routing algorithm

ctelc-patient-attrition-model

Clinical Trial Enrollment Life Cycle (CTELC) modeling project aims to leverage "industry-wide" data to understand key drivers and build predictive models. Patient attrition, also referred to as dropout or patient withdrawal, occurs when patients enrolled in a clinical trial either withdraw or are lost to follow-up by the clinical site and trial sponsor.

data-science-python-course

detectis

A pipeline to rapidly detect exogenous DNA integration sites using DNA or RNA paired-end sequencing data

dpp_imp

Improved clinical data imputation via classical and quantum determinantal point processes

fragler

gim

gene interaction matrices, a novel approach to using ConvNets on gene expression data

gonogo

Implement Go/No-Go policies using multiple endpoints, and simulate the outcome under different scenarios.

hsqc_structure_elucidation

Implementation of the SGNN graph neural network for 1H and 13C NMR prediction and a tool for distinguishing different molecules based on HSQC simulations

ibd-interpret

We trained high performing open source models on image scans of tissue biopsies to predict endoscopic categories in inflammatory bowel disease. These predictive models can help us better understand the disease pathology and represent a step towards automated clinical recruitment strategies.

inspectumours

This is a shiny tool to classify and analyse pre-clinical tumour data automatically.

jazzy

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules.

judgyprophet

Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).

kallisto

Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.

kazu

Fast, world class biomedical NER

kgem-in-drug-discovery

Code to accompany the "Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery" manuscript (Artificial Intelligence in the Life Sciences, 2022)

lung-tumour-mice-mri

machine-learning-for-predicting-targeted-protein-degradation

The code was developed for training diverse ML and DL models to predict PROTACs degradation. Data cleaning for two public datasets, PROTAC-DB and PROTACpedia, are also included. PROTACs are of high interest for all disease areas of AZ and thus predicting their degradation is of general interest.

magnus-extensions

Extensions packages for magnus