jmschrei/yabn

Stars
2
Language
Python
License
MIT License
Created about 10 years ago
Updated about 10 years ago

jmschrei/yabn

jmschrei

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Yet Another Bayesian Network

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

apricot

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

Jupyter Notebook

yahmm

Yet Another Hidden Markov Model repository.

avocado

Avocado is a multi-scale deep tensor factorization model that learns a latent representation of the human epigenome and enables imputation of epigenomic experiments that have not yet been performed.

Jupyter Notebook

torchegranate

A temporary repository hosting a pomegranate re-write using PyTorch as the backend.

Jupyter Notebook

ledidi

Ledidi turns any machine learning model into a biological sequence editor, allowing you to design sequences with desired properties.

Jupyter Notebook

tfmodisco-lite

A lite implementation of tfmodisco, a motif discovery algorithm for genomics experiments.

notebooks

Various notebooks and tutorials on subjects of interest.

Jupyter Notebook

PyPore

Tools used to analyze data from nanopore-based experiments.

dragonnfruit

A method for analyzing scATAC-seq experiments.

rambutan

Prediction of the 3D structure of the genome through statistically significant Hi-C contacts.

Jupyter Notebook

bpnet-lite

This repository hosts a minimal version of a Python API for BPNet.

constraint_graphs

Repository for the data and associated notebook for the manuscript "Finding the optimal Bayesian network given a constraint graph."

Jupyter Notebook

antz

Bioinformatic tools for biologists, made pythonic!

bam2bw

A command-line tool for reading SAM/BAM files and converted them directly to bigwig files.

jmschrei.github.io

Abada

GUI for analyzing nanopore data, based on the Abada package.

discern

kiwano

Kiwano implements an approach for prioritizing epigenomic and transcriptomic characterization based on submodular selection and imputed experiments