• Stars
    star
    29
  • Rank 860,307 (Top 17 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created over 3 years ago
  • Updated about 3 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

More Repositories

1

tidymodules

An Object-Oriented approach to Shiny modules
R
143
star
2

YADA

Open-source Data Ops
Java
81
star
3

peax

Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Jupyter Notebook
67
star
4

torchsurv

Deep survival analysis made easy
Python
57
star
5

scar

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
Python
48
star
6

cellxgene-gateway

Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.
Python
45
star
7

ontobrowser

OntoBrowser is a web-based application for managing ontologies
Java
38
star
8

shinyValidator

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
HTML
38
star
9

yap

High throughput, pain-free big data pipelines.
Python
30
star
10

ChemBioMultimodalAutoencoders

a package for streamlined multidomain data integration and translation based on cross-modal autoencoders architectures
Python
28
star
11

pqsar2cpd

pqsar2cpd is a deep learning algorithm for translation of activity profiles into novel molecules.
Python
28
star
12

easyTrackHubs

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.
HTML
27
star
13

pisces

PISCES is a pipeline for rapid transcript quantitation, genetic fingerprinting, and quality control assessment of RNAseq libraries using Salmon.
Python
26
star
14

JAEGER

JAEGER is a deep generative approach for small-molecule design
Python
25
star
15

xgx

Exploratory Graphics for PKPD data
HTML
25
star
16

Causal-inference-in-RCTs

This repository contains code examples for several methods in a Causal Inference in RCTs short course.
R
20
star
17

patprofile

Standard patient profile for shiny apps
R
19
star
18

DRUG-seq

DRUG (Digital RNA with pertUrbation of Genes)-seq data analysis pipeline
R
18
star
19

MoaBox

A repository of compound-target annotations in support of Systematic Chemogenetic Library Assembly
14
star
20

railroadtracks

Railroadtracks is a Python package to handle connected computation steps for DNA and RNA Seq.
Python
14
star
21

gridvar

GridVar is a jQuery plugin that visualizes multi-dimensional datasets as layers organized in a row-column format.
JavaScript
14
star
22

xgxr

R package for supporting exploratory graphics at http://opensource.nibr.com/xgx
R
13
star
23

Jenkins-LSCI

Jenkins for Life Science Continuous Integration
Groovy
12
star
24

granulator

R
11
star
25

EQP-cluster

Unix-based RNA-seq quantification pipeline
Java
10
star
26

mdx-utils

TypeScript
10
star
27

AEGIS

MHC-II presentation predictor
Python
9
star
28

hpath

An ontology of histopathological morphologies
9
star
29

habitat

"Where files live" - Simple object management system using AWS S3 and Elasticsearch Service to manage objects and their metadata
Python
9
star
30

RBesT

Tool-set to support Bayesian evidence synthesis in R
R
9
star
31

scRNAseq_workflow_benchmark

Workflow for the analysis fo single-cell RNASeq data using R/bioconductor
R
9
star
32

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
Python
9
star
33

subpat

{subpat} is a collection of modules to create subpopulations and subgroups from clinical trial data
R
8
star
34

Advanced-User-Interfaces-for-Shiny-Developers

7
star
35

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.
7
star
36

toolscore

Script and example data for "Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery"
R
7
star
37

UNIQUE

A Python library for benchmarking uncertainty estimation and quantification methods for Machine Learning models predictions
Python
7
star
38

MOBER

Multi-omics batch effect remover method
Python
6
star
39

hdf5r

C
6
star
40

CellSIUS

CellSIUS: Cell Subtype Identification from Upregulated gene Sets
R
5
star
41

dms-pipeline

Snakemake pipeline for quantification of deep mutational scanning (DMS) data from overlapping paired-end reads in fastq files from amplicon sequencing
Python
5
star
42

NDSRIs_in_silico_tool

N-nitrosamine Autonomous Carcinogenic Potency Categorization Approach Calculation Tool
Jupyter Notebook
5
star
43

chraw

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.
R
5
star
44

Project-Mona-Lisa

Project Mona Lisa (PML): Machine-learning Assisted Diagramming Platform
JavaScript
4
star
45

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.
R
4
star
46

verifyr

A package to hold R functions for comparing different version of clinical trial TLFs
Rich Text Format
4
star
47

TRAWLING

Description: Build TRAWLING: a Transcriptome Reference AWare of spLicING events
R
3
star
48

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.
Python
3
star
49

nxc-chess

JavaScript
2
star
50

watMD

Water tool for molecular dynamics that calculates solvation fields for interaction between water and non-solvant molecules
2
star
51

solid-tumor-CHIP

Clonal hematopoiesis detection in cancer patients using cell free DNA sequencing
HTML
2
star
52

Novartis.github.io

Public gallery of NIBR Open Source projects
HTML
2
star
53

sidtoolbox

Subgroup identification toolbox
R
2
star
54

Profile-QSAR

Massively multitask stacked model for predicting activity of thousands of biological assays
1
star
55

Cu-Catalyzed-Ligands-Design

By establishing machine learning (ML) models, the design of ligands and optimization of reaction conditions were effectively facilitated
Jupyter Notebook
1
star
56

EQP-QM

Unix based RNA-seq quantification module
Java
1
star
57

GreyChemicalMatter

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
Jupyter Notebook
1
star
58

mvAC50

AC50 potencies from multivariate assay readouts like gene signatures
R
1
star
59

chess-redux

JavaScript
1
star
60

mueller_et_al_2018

Processed data relating to Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. Paper: http://doi.org/10.1159/000490919 Raw data: http://doi.org/10.5281/zenodo.1443190
1
star