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Neural network aided approximation and parameter inference of non-Markovian models of gene expression.

Nature communications
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parame...

Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer-promoter contact.

Nature communications
Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these comple...

Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Proceedings of the National Academy of Sciences of the United States of America
Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of ...

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.

Briefings in bioinformatics
Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet...

Representation learning of RNA velocity reveals robust cell transitions.

Proceedings of the National Academy of Sciences of the United States of America
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-d...

Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning.

International journal of molecular sciences
Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing ...

Designer genes courtesy of artificial intelligence.

Genes & development
The core promoter determines not only where gene transcription initiates but also the transcriptional activity in both basal and enhancer-induced conditions. Multiple short sequence elements within the core promoter have been identified in different ...

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Environmental science & technology
Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance ...