ArnovanHilten
Follow
Stars
Assemblies from HPP Year 1 production
Biological sequence analysis for the modern age.
A concise but complete full-attention transformer with a set of promising experimental features from various papers
Using sparse coding to find distributed representations used by neural networks.
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
A method for analyzing scATAC-seq experiments.
A template using minimal theme for GitHub pages
Fine-mapping with infinitesimal effects
Framework for Interpretable Neural Networks
This Denoising Force Field (DFF) codebase provides a Pytorch framework for the method presented in Two for one: Diffusion models and force fields for coarse-grained molecular dynamics.
Polygraph evaluates and compares groups of nucleic acid sequences based on their sequence and functional content for effective design of regulatory elements.
SCAVENGE is a method to optimize the inference of functional and genetic associations to specific cells at single-cell resolution.
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
SEACells algorithm for Inference of transcriptional and epigenomic cellular states from single-cell genomics data
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
TF MOtif Discovery from Importance SCOres
Bias factorized, base-resolution deep learning models of chromatin accessibility (chromBPNet)
Deep learning model built to quantitatively predict the activities of developmental and housekeeping enhancers from DNA sequence in Drosophila melanogaster S2 cells
A genome browser in your Jupyter notebook
Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version
Tutorials for the SCOG Virtual Workshop ‘Spatial transcriptomics data analysis in Python’ - May 23-24, 2022
A custom PyTorch layer that is capable of implementing extremely wide and sparse linear layers efficiently