life-sciences
There are 4 repositories under life-sciences topic.
- Language:Python 481
OBOFoundry / OBOFoundry.github.io
Metadata and website for the Open Bio Ontologies Foundry Ontology Registry
Language:Python 158futianfan / clinical-trial-outcome-prediction
benchmark dataset and Deep learning method (Hierarchical Interaction Network, HINT) for clinical trial approval probability prediction, published in Cell Patterns 2022.
Language:Python 85open-life-science / open-life-science.github.io
OLS website: A not-for-profit organisation dedicated to capacity building and diversifying leadership in research worldwide
Language:HTML 61carpentries-incubator / bioc-intro
Bioconductor data science introduction
Language:R 27chupvl / awesome-ls-ventures
Awesome list of the data and AI/ML related projects with direct Life Science Companies participation
amberbiology / py4lifesci
Code repository for the book "Python for the Life Sciences" by Lancaster & Webster
Language:Python 25hyperspy / hyperspyUI
A user interface for the hyperspy package. https://hyperspy.org/hyperspyUI
Language:Python 25awslabs / guidance-for-multi-omics-and-multi-modal-data-integration-and-analysis-on-aws
This guidance creates a scalable environment in AWS to prepare genomic, clinical, mutation, expression and imaging data for large-scale analysis and perform interactive queries against a data lake. The solution also demonstrates the use of Amazon Omics for multi-modal analysis.
Language:Jupyter Notebook 23Analysis and Interpretation of Bulk RNA-Seq Data using Bioconductor
Language:R 18Introduction to the Bioconductor project
Language:R 17- Language:Rich Text Format 17
ebiwd / EBI-Icon-fonts
A collection of functional and scientific icons for EMBL-EBI sites.
Language:JavaScript 14leriomaggio / deep-learning-health-life-sciences
Workshop on Deep Learning for Health and Life Sciences
Language:Jupyter Notebook 14openpharma / brms.mmrm
R package to run Bayesian MMRMs using {brms}
Language:R 14FAIR in (biological) practice
Language:Jupyter Notebook 8carpentries-lab / metagenomics-analysis
Data Processing and Visualization for Metagenomics
Language:Python 8CSCfi / beacon-python
Python-based GA4GH Beacon API Server
Language:Python 8wlandau / rpharma2023-pipeline
R/Pharma 2023 workshop example code: leveraging {targets} and {crew} to simulate clinical trials
Language:R 8aws-samples / amazon-sagemaker-cell-segmentation-workshop
This workshop outlines a machine-learning cell segmentation architecture from scratch for the life-sciences vertical, using Amazon SageMaker, S3, Lambda and SageMaker Inference endpoints
Language:Jupyter Notebook 5fusion-jena / QuestionsMetadataBiodiv
repository for a question corpus for the biodiversity domain, scripts for analyzing metadata in data repositories
Language:Python 5R/Pharma 2023 workshop: leveraging {targets} and {crew} to simulate clinical trials
Language:HTML 5aws-quickstart / quickstart-salesforce-health-cloud-virtual-care
Salesforce Health Cloud Virtual Care AWS Partner Solution
- Language:R 4
- Language:Python 4
Life Sciences Workshop
Language:HTML 4VincentAlcazer / StatAid
StatAid is a Shiny app built in an R package to guide researchers and clinicians through data analysis
Language:R 4christophe-pouzat / YRLS2017
R and RR course material for the YRLS 2017 meeting
Language:HTML 2Semantic Search Extension for Dataset Search UI [Dai:Si]
Language:TypeScript 2jpcartailler / pancreatlas
A web application front-end for browsing and investigating collection-based bioimages
juliocesar-io / ariadne-federation-services
A federated GraphQL services example
Language:Python 2- LINK_bhb_cortisol_2019
RaquelGrosman / LINK_bhb_cortisol_2019
Programming in the Life Sciences: Work in Progress
siegstedt / predict_blood_donation
This project works with data collected from the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes its blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. The dataset, obtained from the UCI Machine Learning Repository, consists of a random sample of 748 donors. The task is to predict if a blood donor will donate within a given time window. The work contains a full model-building process: from inspecting the dataset to using the tpot library to automate your Machine Learning pipeline.
Language:Python 2- Language:Ruby 2
xtens-suite / xtens-app
A digital repository for biomedical data based on a flexible JSON data model
Language:JavaScript 2
