When less is more: sketching with minimizers in genomics
The exponential increase in sequencing data calls for conceptual and computational
advances to extract useful biological insights. One such advance, minimizers, allows for …
advances to extract useful biological insights. One such advance, minimizers, allows for …
Deep learning for genomics: From early neural nets to modern large language models
The data explosion driven by advancements in genomic research, such as high-throughput
sequencing techniques, is constantly challenging conventional methods used in genomics …
sequencing techniques, is constantly challenging conventional methods used in genomics …
iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
The rapid accumulation of molecular data motivates development of innovative approaches
to computationally characterize sequences, structures and functions of biological and …
to computationally characterize sequences, structures and functions of biological and …
Optimization of drug–target affinity prediction methods through feature processing schemes
Motivation Numerous high-accuracy drug–target affinity (DTA) prediction models, whose
performance is heavily reliant on the drug and target feature information, are developed at …
performance is heavily reliant on the drug and target feature information, are developed at …
Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis
Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to
changing environments, yet the concept of transcriptional plasticity (TP)–the variability of …
changing environments, yet the concept of transcriptional plasticity (TP)–the variability of …
BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
Recent technological advances have led to an exponential expansion of biological
sequence data and extraction of meaningful information through Machine Learning (ML) …
sequence data and extraction of meaningful information through Machine Learning (ML) …
Computational model for ncRNA research
The explosion of research on non-coding RNAs (ncRNAs) in the past few decades has
transformed the original notion of regarding such RNAs as 'transcriptional noise'[1, 2] before …
transformed the original notion of regarding such RNAs as 'transcriptional noise'[1, 2] before …
DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA–disease associations and graph convolutional networks
T Bai, K Yan, B Liu - Briefings in Bioinformatics, 2023 - academic.oup.com
MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are
involved in regulating various physiological processes by regulating the gene expression …
involved in regulating various physiological processes by regulating the gene expression …
[HTML][HTML] Cross-species enhancer prediction using machine learning
Cis-regulatory elements (CREs) are non-coding parts of the genome that play a critical role
in gene expression regulation. Enhancers, as an important example of CREs, interact with …
in gene expression regulation. Enhancers, as an important example of CREs, interact with …
Integrated Biochemical and Computational Methods for Deciphering RNA‐Processing Codes
C Du, W Fan, Y Zhou - Wiley Interdisciplinary Reviews: RNA, 2024 - Wiley Online Library
ABSTRACT RNA processing involves steps such as cap**, splicing, polyadenylation,
modification, and nuclear export. These steps are essential for transforming genetic …
modification, and nuclear export. These steps are essential for transforming genetic …