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Conserved Sequence

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Deciphering epigenomic code for cell differentiation using deep learning.

BMC genomics
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...

The use of knowledge management tools in viroinformatics. Example study of a highly conserved sequence motif in Nsp3 of SARS-CoV-2 as a therapeutic target.

Computers in biology and medicine
Knowledge management tools that assist in systematic review and exploration of scientific knowledge generally are of obvious potential importance in evidence based medicine in general, but also to the design of therapeutics based on the protein subse...

Predicting antimicrobial resistance using conserved genes.

PLoS computational biology
A growing number of studies are using machine learning models to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these studies are showing promise, the models are typically trained using features de...

Improvement of Neoantigen Identification Through Convolution Neural Network.

Frontiers in immunology
Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are lim...

TwinCons: Conservation score for uncovering deep sequence similarity and divergence.

PLoS computational biology
We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one ...

Improved Predicting of The Sequence Specificities of RNA Binding Proteins by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-binding proteins (RBPs) have a significant role in various regulatory tasks. However, the mechanism by which RBPs identify the subsequence target RNAs is still not clear. In recent years, several machine and deep learning-based computational mode...

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Nature genetics
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Dro...

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

International journal of molecular sciences
Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature () of enzymes is crucial for optimizing reaction cond...