DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
47,133 repositories indexed
25–48 of 63 · Most stars
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Throw in the towel.
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
DiffSharp: Differentiable Functional Programming
Module for statistical learning, with a particular emphasis on time-dependent modelling
🎯 A comprehensive gradient-free optimization framework written in Python
Vision-Language Pathology Foundation Model - Nature Medicine
A Euclidean diffusion model for structure-based drug design.
A tool for semi-automatic cell type classification
Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.
A blazing fast matrix library for Elixir/Erlang with C implementation using CBLAS.
Tools for computational pathology
Descriptor computation(chemistry) and (optional) storage for machine learning
Bayesian Optimization using GPflow
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
A command-line utility for quick math.
A collection of useful mathematical functions in Elixir with a slant towards statistics, linear algebra and machine learning
A Network Integration Approach for Drug-Target Interaction Prediction
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
The V Tensor Library
Open-source software for exploring and analyzing large, high-dimensional image-derived data.
Sparse matrix formats for linear algebra supporting scientific and machine learning applications
Scientific computing in pure Crystal
Optimising chemical reactions using machine learning