# Bioinformatics

GoBERT is a model specifically designed for general gene function prediction. It can effectively capture the relationships between Gene Ontology (GO) functions by leveraging gene ontology graph information.

RNABERT is a pre-trained model based on non-coding RNA (ncRNA), employing Masked Language Modeling (MLM) and Structural Alignment Learning (SAL) objectives.

RiNALMo is a non-coding RNA (ncRNA) model pre-trained based on the masked language modeling (MLM) objective, trained through self-supervised learning on a large number of non-coding RNA sequences.

A Transformer encoder model based on BERT architecture, specifically designed for generating RNA sequence embeddings

This model is fine-tuned based on Bert-Base-Uncased and is used for the multi-label classification task of predicting protein functions based on protein amino acid sequences.

ProGen2-small is a mirrored version of the protein generation model based on Nijkamp et al.'s research, with slight adjustments to the configuration and forward propagation process.

This model generates embedding vectors for protein sequences, supporting shortened embeddings to accelerate search tasks.

A sequence classifier based on the ESM-2 protein language model, designed for zero-shot classification tasks of protein sequences.

The Nucleotide Transformer is a set of foundational language models pre-trained on whole-genome DNA sequences, integrating genomic data from over 3,200 human genomes and 850 diverse species.

ESM-1b is a Transformer-based protein language model trained via unsupervised learning on protein sequence data, capable of predicting protein structure and function.

ESM-2 is a state-of-the-art protein model trained on masked language modeling objectives, suitable for protein sequence analysis and prediction tasks

TCR-BERT is a pre-trained model based on the BERT architecture, specifically optimized for T-cell receptor (TCR) sequences through masked amino acid modeling tasks.