Ronnie Alves received his PhD in Computer Science(Artificial Intelligence) from the University of Minho at Braga (Portugal) in April'2008, under the supervision of Prof. Dr.Orlando Belo. While pursuing his PhD studies, he also served as a Visiting Researcher in the Data Mining Group at DAIS Lab in the University of Illinois at Urbana-Champaign, USA) led by Prof. Dr. Jiawei Han and in the Bioinformatics Research Group at Pablo de Olavide University – Sevilla, Spain) led by Prof. Dr. Jesus Aguilar-Ruiz. Before starting his Ph.D, he also worked as IT Consultant on several industrial data mining projects (From Retail to Telecommunications) in Brazil and Portugal. He was awarded a Postdoctoral Fellowship (Transcriptomics) from the French National Center for Scientific Research(CNRS) in the Virtual Biology Lab at the Institute of Biology Valrose (iBV) at Nice from September'2008 to March'2010. Followed by another two-year postdoc position on Machine Learning and Bioinformatics at theInstitute of Informatics at the Federal University of Rio Grande do Sul (UFRGS). Being also a Collaborating Professor in the Department of Theoretical Informatics. He worked as Researcher Fellow (Jeune Chercheur) in the Lab. of Computer Science, Robotics and Microelectronics of Montpellier (LIRMM), being a member of the Computational Biology Institute (IBC) at the Université Montpellier (UM) from September'2014 to February'2016. He is an Associate Researcher in the Environmental Genomics Research Group at the Instituto Tecnologico Vale (ITV).
He holds the position of Collaborating Professor in the Computer Science Graduate's Program of UFPA (PPGCC-UFPA) and in the Professional Master's in Sustainable Use of Natural Resources in Tropical Regions (MProf-ITV). He supervises Ph.D and MSc students in the research area of Intelligent Systems, focusing on bioinformatics and machine learning. He is also a member of the Special Committee on Computational Biology (CE-BioComp) of the Brazilian Computer Society (SBC).
Machine Learning Meets Genome Assembly
With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved due to it, especially in the health sciences. However, many challenges which emerge from sequencing projects complexity remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that, although needing improvement, have facilitated scientific discoveries. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to solve the DNA fragment assembly problem, although still at a low scale. In this talk we present a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers — particularly the ones that use machine learning — to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.