Title: Modeling of Functional Gene Regulation Through Machine Learning and Deep Learning Methods.
Abstract: Genes are specific sequences of nucleotides within the genome that undergo transcription to generate products that can diffuse throughout the cell. These gene products can take the form of either proteins or RNAs and play crucial roles in virtually all cellular functions, encompassing structural support, enzymatic activities, and signaling functions. An essential focus of contemporary biological research revolves around unraveling the functions of genes and their resulting products across different organisms. One primary means to conduct this research is through the regulation of gene expression.
MicroRNAs (miRNAs), short single-stranded non-coding RNAs, play vital roles in gene expression regulation. Both computational and experimental analyses indicate that most human protein-coding genes are regulated by one or more miRNAs. For functional miRNA analysis, one critical first step is to identify genes targeted by the miRNA. We have identified key characteristics of functional miRNA-target pairs and developed a machine learning prediction algorithm for genome-wide miRNA target prediction accordingly. This tool has gained significant popularity in the field, with hundreds of daily usages.
Additionally, the CRISPR (clustered regularly interspaced short palindromic repeats) Cas system is a rapidly advancing technology associated with gene regulation. It enables gene expression activation, repression, or knockout in mammalian systems. Previous studies indicate the efficiency of CRISPR/Cas system is dependent on one of its core components, guide RNA (gRNA). Leveraging innovative experimental designs and an ensemble learning framework, we have created a state-of-the-art gRNA design algorithm, offering superior performance for predicting CRISPR efficiency.
Importantly, these algorithms have been implemented as web-accessible applications (miRDB.org and CRISPRDB.org) to enable flexible bioinformatics analyses of miRNA target regulation and CRISPR design by the research community.
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