The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 20-713
Full Name: Gabriela Czibula
Position: Professor
Age: ON
Sex: Female
Address: 1, M. Kogalniceanu street
Country: ROMANIA
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E-mail address: gabis@cs.ubbcluj.ro
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Title of the Paper: Solving the Protein Folding Problem Using a Distributed Q-Learning Approach
Authors as they appear in the Paper: Gabriela Czibula, Maria-Iuliana Bocicor, Istvan-Gergely Czibula
Email addresses of all the authors: gabis@cs.ubbcluj.ro, iuliana@cs.ubbcluj.ro, istvanc@cs.ubbcluj.ro
Number of paper pages: 10
Abstract: The determination of the three-dimensional structure of a protein, the so called protein folding problem, using the linear sequence of amino acids is one of the greatest challenges of bioinformatics, being an important research direction due to its numerous applications in medicine (drug design, disease prediction) and genetic engineering (cell modelling, modification and improvement of the functions of certain proteins). We are introducing in this paper a distributed reinforcement learning based approach for solving the bidimensional protein folding problem, an NP-complete problem that refers to predicting the bidimensional structure of a protein from its amino acid sequence. Our model is based on a distributed Q-learning approach. The experimental evaluation of the proposed system has provided encouraging results, indicating the potential of our proposal. The advantages and drawbacks of the proposed approach are also emphasized.
Keywords: Bioinformatics, Machine learning, Q-learning, Distributed reinforcement learning, Protein folding
EXTENSION of the file: .pdf
Special (Invited) Session: A Distributed Reinforcement Learning Approach for Solving Optimization Problems
Organizer of the Session: 303-132
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