Discover Novartis/chess-redux Open Source project by @Novartis

Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

peax

Deep survival analysis made easy

torchsurv

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

scar

Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.

cellxgene-gateway

OntoBrowser is a web-based application for managing ontologies

ontobrowser

Audit your Shiny apps at each commit. Multiple levels of testings are offered: startup and crash tests, performance tests (load test and global code profiling), reactivity audit as well as output tests. All results are gathered in an HTML report uploaded and available to everyone on any CI/CD plaform or RStudio Connect

shinyValidator

High throughput, pain-free big data pipelines.

yap

Massively multitask stacked model for predicting activity of thousands of biological assays

pQSAR

a package for streamlined multidomain data integration and translation based on cross-modal autoencoders architectures

ChemBioMultimodalAutoencoders

pqsar2cpd is a deep learning algorithm for translation of activity profiles into novel molecules.

pqsar2cpd

This package provides a function to reformat lists of genome coverage files, such as bigWig of bam files, into the directory structure of a UCSC Track Hub ready to be visualized in the genome browser. For details about it's use, please have a look at the vignette of the package.

easyTrackHubs

PISCES is a pipeline for rapid transcript quantitation, genetic fingerprinting, and quality control assessment of RNAseq libraries using Salmon.

pisces

JAEGER is a deep generative approach for small-molecule design

JAEGER

Exploratory Graphics for PKPD data

xgx

This repository contains code examples for several methods in a Causal Inference in RCTs short course.

Causal-inference-in-RCTs

patprofile

Standard patient profile for shiny apps

DRUG-seq

DRUG (Digital RNA with pertUrbation of Genes)-seq data analysis pipeline

MoaBox

A repository of compound-target annotations in support of Systematic Chemogenetic Library Assembly

railroadtracks

Railroadtracks is a Python package to handle connected computation steps for DNA and RNA Seq.

GridVar is a jQuery plugin that visualizes multi-dimensional datasets as layers organized in a row-column format.

gridvar

JavaScript

xgxr

R package for supporting exploratory graphics at http://opensource.nibr.com/xgx

Jenkins-LSCI

Jenkins for Life Science Continuous Integration

Groovy

granulator

EQP-cluster

Unix-based RNA-seq quantification pipeline

MHC-II presentation predictor

mdx-utils

TypeScript

AEGIS

An ontology of histopathological morphologies

hpath

habitat

"Where files live" - Simple object management system using AWS S3 and Elasticsearch Service to manage objects and their metadata

Tool-set to support Bayesian evidence synthesis in R

RBesT

scRNAseq_workflow_benchmark

Workflow for the analysis fo single-cell RNASeq data using R/bioconductor

div_rank

This code allows is for diversity picking across multiple different, and potentially overlapping chemical compound classes, while at the same time optimizing a property score. This algorithm has been used in the re-design of the Novartis screening deck as described in https://dx.doi.org/10.1021/acs.jmedchem.0c01332

{subpat} is a collection of modules to create subpopulations and subgroups from clinical trial data

subpat

Advanced-User-Interfaces-for-Shiny-Developers

UMM-Discovery

UMM-Discovery is a fully unsupervised deep learning method to cluster cellular images with similar phenotypes together, solely based on the intensity values.

toolscore

Script and example data for "Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery"

UNIQUE

A Python library for benchmarking uncertainty estimation and quantification methods for Machine Learning models predictions

Multi-omics batch effect remover method

MOBER

CellSIUS: Cell Subtype Identification from Upregulated gene Sets

hdf5r

CellSIUS

dms-pipeline

Snakemake pipeline for quantification of deep mutational scanning (DMS) data from overlapping paired-end reads in fastq files from amplicon sequencing

N-nitrosamine Autonomous Carcinogenic Potency Categorization Approach Calculation Tool

NDSRIs_in_silico_tool

The package analyzes chromatin and multi-omic experiments. It extends the MultiAssayExperiment object and builds a ChrawExperiment object from ENCODE’s output. It performs QC plotting, identifies differential events and other functionalities. More details in package vignettes.

chraw

Project-Mona-Lisa

Project Mona Lisa (PML): Machine-learning Assisted Diagramming Platform

JavaScript

peakCombiner

The fully R based tool peakCombiner is a user-friendly, transparent, modular and customizable package with the purpose to create a consensus peak file from genomic input regions. The aim is to allow even novice R users to create good quality combined peak sets to be used as the starting point for most downstream differential analyses.

verifyr

A package to hold R functions for comparing different version of clinical trial TLFs

Rich Text Format

TRAWLING

Description: Build TRAWLING: a Transcriptome Reference AWare of spLicING events

TAT

Transcriptomics-to-Activity Transformer (TAT) is a deep learning model to predict compound bioactivity in a dose-response assay using compound-induced transcriptomic profiles over concentration.

Water tool for molecular dynamics that calculates solvation fields for interaction between water and non-solvant molecules

nxc-chess

JavaScript

watMD

solid-tumor-CHIP

Clonal hematopoiesis detection in cancer patients using cell free DNA sequencing

Public gallery of NIBR Open Source projects

Novartis.github.io

Subgroup identification toolbox

sidtoolbox

Profile-QSAR

Massively multitask stacked model for predicting activity of thousands of biological assays

Cu-Catalyzed-Ligands-Design

By establishing machine learning (ML) models, the design of ligands and optimization of reaction conditions were effectively facilitated

Unix based RNA-seq quantification module

EQP-QM

A pipeline to identify bioactive small molecules with likely novel modes of actions and dynamic SAR from historic cell-HTS profiles, with an example application and hitlist from PubChem data

GreyChemicalMatter