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یادگیری پیرایش دگرسان از داده های توالی یابی آر. ان. ای
رشیدی مهرآبادی، فرید Rashidi Mehrabadi, Farid
- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 49410 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Rabiee, Hamid Reza; Motahari, Abolfazl
- Abstract:
- We construct and analyse a computational model that predicts the outcome of alternative splicing by recognizing features in RNA sequences. The computational model can be viewed as a “splicing simulator” for a range of healthy human tissues. It takes as input a pre-mRNA sequence surrounding a possibly alternatively spliced exon and estimates the inclusion level of that exon in mature RNA, after splicing occurs. The model is trained using a supervised machine learning framework where the training examples are the alternatively spliced exons, the feature vectors are derived RNA sequences near these exons, and the targets are their corresponding splicing outcomes in healthy individuals. The model is inferred from over 2.5 million synthetic mini-gene. This thesis is a step towards using artificial intelligence and large amounts of genomic data to automatically model the complex cellular mechanisms that read and process DNA. In our opinion, computational models constructed using this approach will bring significant value to genomic medicine, because they can model biological mechanisms and can be used for a wide range of sequences. As a result, the cellular effects of mutations can be predicted even if the mutation has not been observed before. This ability can be used for genetic diagnostics, studying the effects of complex diseases, and searching for treatments. In addition, it is anticipated that these computational models will improve with the growing size of genomic data data available for training. By learning the model using two step of extracting new feature such as RNA Secondary Structure and learning new model such as SVR, RandomForest, Deep Network, the person correlation coefficient between the model output and the real output was improved up into 0.73
- Keywords:
- Pattern Recognition ; Deep Learning ; Alternative Splicing Learning ; RNA Sequencing
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