Classification of long non-coding RNAsApply
Long non-coding RNAs (lncRNAs) have been found to perform various functions in a wide variety of important biological processes. To make easier interpretation of lncRNA functions and conduct deep mining on these transcribed sequences, it is important to classify lncRNAs into different groups. lncRNA classification attracts much attention recently. The main technical difficulties are 1) the limited number of known lncRNAs (small training sample size), and 2) the very different lengths of lncRNAs. This project is to apply and further improve the string kernel algorithms developed in Prof. Gao’s group to the lncRNA classification problem.
Program - Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Center Affiliation - Computational Bioscience Research Center
Field of Study - Computer science, bioinformatics, electrical engineering, applied mathematics
Professor, Computer Science <br/>Interim Director of Computational Bioscience Research Center
Gao's research lies at the intersection between computer science and biology. His work has two main focuses: 1) developing theory and methodology in the fields of machine learning and algorithms; and 2) solving key open problems in biological and medical fields through building computational models, developing machine-learning techniques, and designing effective and efficient algorithms. In particular, he aims to solve problems that occur along the path from protein amino acid sequences to their three-dimensional structures and functions that ultimately lead to their undesirable expression in complex biological networks.
Desired Project Deliverables
The visiting student for this project is expected to finish the following deliverables:1. Give a throughout literature review on lncRNA classification methods and potential machine learning methods that can be applied to this problem. 2. Get familiar with the string kernel algorithms developed in Prof. Gao’s group. 3. Gather an lncRNA dataset to be used as the benchmark set for this research. 4. Conduct a comprehensive comparative study of the state-of-the-art methods on the benchmark set. 5. Apply the string kernel algorithms on lncRNA classification and evaluate the performance. 6. If necessary, improve the string kernel algorithms to achieve better performance.Write a report to summarize the results.