Nucleotide Transformer: building and evaluating robust foundation models for human genomics
The prediction of molecular phenotypes from DNA sequences remains a longstanding
challenge in genomics, often driven by limited annotated data and the inability to transfer …
challenge in genomics, often driven by limited annotated data and the inability to transfer …
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
Motivation Deciphering the language of non-coding DNA is one of the fundamental
problems in genome research. Gene regulatory code is highly complex due to the existence …
problems in genome research. Gene regulatory code is highly complex due to the existence …
Learning the regulatory code of gene expression
Data-driven machine learning is the method of choice for predicting molecular phenotypes
from nucleotide sequence, modeling gene expression events including protein-DNA …
from nucleotide sequence, modeling gene expression events including protein-DNA …
Principles and correction of 5'-splice site selection
In Eukarya, immature mRNA transcripts (pre-mRNA) often contain coding sequences, or
exons, interleaved by non-coding sequences, or introns. Introns are removed upon splicing …
exons, interleaved by non-coding sequences, or introns. Introns are removed upon splicing …
Spliceator: multi-species splice site prediction using convolutional neural networks
Background Ab initio prediction of splice sites is an essential step in eukaryotic genome
annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene …
annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene …
pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools
Abstract We present pipeComp (https://github. com/plger/pipeComp), a flexible R framework
for pipeline comparison handling interactions between analysis steps and relying on multi …
for pipeline comparison handling interactions between analysis steps and relying on multi …
[HTML][HTML] Computational identification of N6-methyladenosine sites in multiple tissues of mammals
Abstract N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6
position, which is the most abundant RNA methylation modification and involves a series of …
position, which is the most abundant RNA methylation modification and involves a series of …
DL-m6A: Identification of N6-methyladenosine Sites in Mammals using deep learning based on different encoding schemes
N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical
function in a variety of biological processes. Although experimental approaches for …
function in a variety of biological processes. Although experimental approaches for …
Neural network analysis
Neural networks play very significant role when it comes to analysis of proteins and nucleic
acid sequences. Many of the pattern recognition software are based on neural networks for …
acid sequences. Many of the pattern recognition software are based on neural networks for …
Progress and opportunities of foundation models in bioinformatics
Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly
through foundation models (FMs), which address longstanding challenges in bioinformatics …
through foundation models (FMs), which address longstanding challenges in bioinformatics …