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This model was finetuned on concatenated pairs of interacting proteins in much the same way as PepMLM. It is meant to generate interaction partners for proteins using the masked language modeling capabilities of ESM-2. The model is not well tested, so use with caution.

Secure text-to-visualization through standardized chart specifications

ProstT5 is a protein language model (pLM) which can translate between protein sequence and structure. !ProstT5 pre-training and inference

DGL-LifeSci is a [DGL](https://www.dgl.ai/)-based package for various applications in life science with graph neural network.

Question Answering Model for the PathoTHREAT Project

Write-once-read-many table for large datasets.

First foundation model for weather and climate by Microsoft, Vision Transformer-based architecture trained on heterogeneous datasets (ICML 2023)

MentaLLaMA-chat-7B is part of the MentaLLaMA project, the first open-source large language model (LLM) series for interpretable mental health analysis with instruction-following capability. This model is finetuned based on the Meta LLaMA2-chat-7B foundation model and the full IMHI instruction…

A VCF Parser for Python.

First vision-and-language foundation model for pathology AI, fine-tuned from CLIP on 249K image-caption pairs, enabling open-ended visual-semantic search and zero-shot diagnosis across histopathology (Pathology Foundation, 376+ stars)

This model is a fine-tuned model based on the Llama 2_7b architecture. It has been specifically trained on a dataset comprising USMLE (United States Medical Licensing Examination) questions and answers, as well as conversations between doctors and patients.

Screen a bacterial assembly (contigs/CDS or proteins) for nucleotide or protein sequences. Pipeline that screens for presence of genes of interest (GOI) in bacterial assemblies. Generates multiple CSVs and plots that describe which genes are present and how variable their sequence is. Can use DNA or protein query sequences (GOIs) and DNA contigs/fastas or protein fastas as database (db) to search in.

An open, extensible Python framework for GPU-accelerated alchemical free energy calculations.

This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).

I present a demo showcasing retinal vessel segmentation using the U-Net model, which is a well-known and widely used model in medical image segmentation. The model was trained on the DRIVE dataset, and the training process was conducted on Google Colab.

datasets: - UMLS

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

Easily submitting PBS jobs with script template. Multiple input files supported.

A Library for Deep Learning in Biology and Chemistry.

A deep learning framework (based on Chainer) with applications in Biology and Chemistry.

Enables machine learning on three-dimensional molecular structure.

NuclearPhaser is a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs. This is an overview of the phasing pipeline for dikaryons.

a robust molecular representation learning framework against distribution shifts.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e.

The Science Data Discovery Ontology (sddo) is being developed to provide a semantic foundation for the discovery of information managed by NASA's Science Mission Directorate. This information spans many scientific disciplines, fields and subfields, including heliophysics, earth science, planetary science, astrophysics, biology, astrobiology, and physical science. [from repository]

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Filtering and trimming of long read sequencing data.

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

Toolkit for processing molecules, reactions and condensed graphs of reactions. Can be used for chemical standardization, MCS search, tautomers generation with backward compatibility to RDKit and NetworkX.

Go Get Data; A command line interface for obtaining genomic data.

A cookiecutter template for bioinformatics projects, with a focus on building bioinformatics workflows that can run on the MPI-IE cluster according to FAIR principles.

Hierarchical Generation of Molecular Graphs using Structural Motifs.

Learning nonlinear operators

# ChemGPT 1.2B ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

# ChemGPT 19M ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

# ChemGPT 4.7M ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

Algorithm Metadata Vocabulary is a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). There are uncountable algorithms present in every area (e.g., Computer Science, Mathematics), which makes it hard for specialists, academicians, application engineers, and so forth to discover, distinguish, select, and reuse them. [from repository]

AI for chemical reaction prediction and synthesis planning

Spherical CNNs for astronomy