LAMAR is a Foundation Language Model for RNARegulation, which achieves better or comparable performance compared to baseline models in various
RNA regulation tasks, helping to decipher the rules of RNA regulation. LAMAR was developed by Rnasys Lab and Bio-Med Big Data Center, Shanghai Institute of Nutrition
and Health (SINH), Chinese Academy of Sciences (CAS).
This repository contains pretrained and fine-tuned weights for RNA foundation language model LAMAR.
LAMAR is pretrained on approximately 15 million sequences from both genome and transcriptome of 225 mammals and 1569 viruses, and further fine-tuned with labeled
datasets for various tasks. Considering the sequence length of genes/transcripts and the available computational resources, we pretrain two models with the contextual
length of up to 2048 and 4096 tokens, named LAMAR-2k and LAMAR-4k.
mammalian80D_2048len1mer1sw_80M: Pretrained weights of LAMAR-2k
mammalian80D_4096len1mer1sw_80M: Pretrained weights of LAMAR-4k
LAMAR is fine-tuned to predict the splice site, mRNA translation efficiency, mRNA degradation rate and internal ribosome entry site (IRES).
SpliceSitePred: Weight of fine-tuned LAMAR predict splice site of pre-mRNA
UTR5TEPred: Weight of fine-tuned LAMAR predict translation efficiency of mRNA based on 5' UTR
UTR3DegPred: Weight of fine-tuned LAMAR predict degradation rate of mRNA based on 3' UTR
IRESPred: Weight of fine-tuned LAMAR predicting internal ribosome entry site (IRES)
