1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Non-coding and Transposable elements discovery through artificial intelligence approaches
Paper ID : 1475-ICB10
Authors:
Alexandre Rossi Paschoal *
Associate Professor of Computer Science, Bioinfo & Pattern Recognition Group, UTFPR, BR
Abstract:
We have learned that only a tiny portion of the eukaryotic genomes are translated into proteins. In the last years, significant efforts have been devoted to differentiating pervasive transcription from functional Non-coding RNAs (ncRNAs) transcripts. In the same field, the transposable element is one of the major mechanisms to promote variability in the genome. In this talk, we will discuss how we could use and apply Big Data, Artificial Intelligence, and Data Science to Non-coding and TE problems. In particular about: (i) large-scale data integration and analysis; (i) the relationship between ncRNAs and transposable elements; and (iii) a machine learning and deep learning tool for long RNAs and TE.
Keywords:
Non-coding RNA
Status : Paper Accepted (Oral Presentation)