Muhammad Nabeel Asim

"Deep dive into genomic analysis using machine learning"

With the advancement of high-throughput sequencing technologies and ultra-modern bioinformatics tools, data related to genome sequencing is increasing. As compared to the previous decade, now humongous genome-wide assays of gene expression are available publicly, deep analysis and biological interpretation of which can facilitate in acquiring profound comprehension of multifarious areas including sub-cellular location prediction of non-coding RNA, nucleosome position detection, Acetylation and methylation prediction in DNA sequences, enhancer discrimination, and Prediction of human-virus protein-protein interactions. Over the period, a number of machine learning based methodologies have been proposed to improve the predictive performance of diverse biomedical tasks mentioned earlier. However, there is still a big gap between the genome analysis and machine learning community. The aim of my thesis is to bridge this gap by developing machine learning based methodologies to substantially raise the predictive performance, overall efficiency, and adaptation for different scale genome analysis datasets.

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