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Machine learning in prediction of ageing-related genes/proteins

in Algorithms/Databases/Machine Learning by

Ageing has a great impact on human health, when people’s age advance towards 80 years, approximately half of the proteins in the body get damaged through oxidation. The chemical degradations occurring in our body produce energy by the consumed food via oxidation in the presence of oxygen. Continue reading “Machine learning in prediction of ageing-related genes/proteins” »

Bioinformatics Challenges and Advances in RNA interference

in Algorithms/Machine Learning/Tools by

 

 

RNA interference is a post-transcriptional gene regulatory mechanism to down-regulate the gene expression either by mRNA degradation or by mRNA translation inhibition. The mechanism involves a small partially complementary RNA against the target gene. To perform the action, it also requires a class of dedicated proteins to process these primary RNAs into mature microRNAs. The guide sequence determines the specificity of the miRNA. Therefore, the knowledge of guide sequence is crucial for predicting its targets and also exploiting the sequence to create a new regulatory circuit. In this short review, we will briefly discuss the role and challenges in miRNA research for unveiling the target prediction by bioinformatics and to foster our understanding and applications of RNA interference. Continue reading “Bioinformatics Challenges and Advances in RNA interference” »

New features to predict miRNA target sites in mRNAs

in Algorithms/Machine Learning by

In order to study gene regulation, it is necessary to identify the target sites of miRNA in mRNA. miRNAs have been the main point of research as its binding to mRNA degrades the target mRNA and also prevents the translation of target mRNAs [1-4]. The identification of miRNA target sites, target mRNAs and the potential functional roles of miRNA may be assigned. Continue reading “New features to predict miRNA target sites in mRNAs” »

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