BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

easyPheno: a model agnostic phenotype prediction framework

Package to serve public and freely-available data from rare disease patients.

Microbial Phenotype Prediction, successor to PICA, implemented with Python 3.7 and scikit-learn

Create binary traits for UKbio using ICD/OPER/medication/self reports/age of diagnosis/visit-dates etc). The current output includes variables on history, study visit, future, time-to-first-event, episode-duration. this is a starting point to construct your favorite trait in ukb. The code was written on the go, so not super clean code..

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

Apply AI-based trait prediction from BacDive to your own genomes.

Bayesian Network Model Predicting Personal Phenotypes using Genome-Sequencing Data

A class based on COBRApy to handle quantitative mutations on metabolic models.

Using machine learning to predict E. coli phenotypes

Indonesia Exome Rare Disease Variant Discovery Pipeline. Phenotype analysis part available on Streamlit and PyPi

IdeRare Phenotype Analysis suite : Convert Indonesia SATUSEHAT terminology (SNOMED-CT, LOINC, ICD-10) to Rare Disease Terminology / Ontology (HPO, OMIM) and find the likelihood differential gene and disease explaining patient phenotype

Comp Bio fall 2017 project

Phenotype prediction pipeline

Joint Repository of GR Metrics Regression and Kinase Interaction Network (KIN) Clustering

MicrobeLLM is a Python tool that harnesses the power of large language models (LLMs) for microbial phenotype prediction.