Tutorial 1 - Bioinformatics Basics with Applications for Computational Intelligence

Presenters: Clare Bates Congdon and Daniel Ashlock

Description

This tutorial is aimed at researchers interested in learning about the field of bioinformatics and some of the biological background needed to undertake computational intelligence work in this area. It presumes no particular background in biology and is intended to equip newcomers with the necessary background to participate in the sessions for the Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

Topics covered will include the "central dogma" of biology, gene and protein structure, and
problems such as sequence alignment, motif inference, phylogenetics, and DNA barcoding. A brief survey of the techniques of computational intelligence in the context of bioinformatics will be included.

Biosketch of Presenters:

Clare Bates Congdon received a PhD in Computer Science and Engineering from The University of Michigan in 1995. She is now a Research Scientist in the Computer Science Department at Colby College and is funded by the NIH INBRE program for her project "Machine Learning for Phylogenetics and Genomics".

Daniel Ashlock received a PhD in Mathematics from Caltech in 1990. He is now a Professor of Mathematics and Statistics at the University of Guelph where he holds a Chair in Bioinformatics. He is currently funded
by the NSF to work on the sequencing of the maize genome and on the analysis and display of biological networks. He also is funded by the NIH to work on recombination in retroviruses. Dr. Ashlock is a senior member of the IEEE and an associate editor of the IEEE Transactions on Evolutionary Computation.

Tutorial 2 - Introduction to Chemoinformaticss

Presenter: Pierre Baldi, School of Information and Computer Sciences and the Department of Biological Chemistry, University of California, Irvine

Description

This self-contained tutorial will provide an overview of chemoinformatics, from foundations to state-of-the-art results and challenges. It will cover molecular and reaction data, data structures and the available algorithms for efficiently searching large repositories and annotating or predicting the physical, chemical, and biological properties of compounds and reactions with applications ranging from chemical genomics to drug discovery. The tutorial will leverage analogies and create synergies between bio and chemical informatics. It aims to bring bioinformaticians up to speed with the state-of-the-art in chemoinformatics methods, by exploring similarities and differences between bioinformatics and chemoinformatics. The two main driving forces behind the bioinformatics expansion have been: (1) the development of high throughput methods and the corresponding public availability of large repositories (GenBank, Swissprot, PDB, etc); and (2) the development of search algorithms (BLAST) and related statistical machine learning techniques to analyse the data. Mutatis mutandis, the same is true of chemoinformatics, with the caveats that large repositories of chemical data have started to become available only very recently, over the last two years or so. Many of the basic concepts in bioinformatics (similarity, search, alignments, kernels, data structures, etc) have applications in chemoinformatics. From a scientific standpoint, chemoinformatics is relevant to bioinformatics since it plays a key role in several applications such as chemical genomics and drug discovery/screening/design applications. The tutorial is introductory in the sense that it is self-contained, but it will also cover advanced topics and present the state-of-the-art, as well as the main open challenges, in chemoinformatics.

No prior knowledge is expected. However basic familiarity with organic chemistry is most desirable. Basic understanding of databases and/or statistics and machine learning is also desirable, but not necessary.

Biosketch of Presenter:

Pierre Baldi is a Professor in the School of Information and Computer Sciences and the Department of Biological Chemistry at the University of California, Irvine and the Director of the UCI Institute for Genomics and Bioinformatics. He received a PhD in Mathematics in 1986 from the California Institute of Technology.

He has held postdoctoral, faculty, and member of the technical staff positions at UCSD and Caltech, in the Division of Biology and the Jet Propulsion Laboratory. He was CEO of a startup company for a few years and joined UCI in 1999. He is the recipient of a 1993 Lew Allen Award at JPL and a Laurel Wilkening Faculty Innovation Award at UCI. Dr. Baldi's has published over 150 scientific articles and four book. Research in his group focuses on several areas at the intersection of the computational and life sciences, in particular the application of AI/statistical/machine learning methods to problems in bio and chemical informatics. Work in his group has resulted in several databases, software, and web servers that are widely used (www.igb.uci.edu/servers/servers.html). His main contributions include the development of Hidden Markov Models (HMMPro) for sequence analysis, recursive neural networks for de novo protein structure prediction (SCRATCH), Bayesian statistical methods for DNA microarray analysis (Cyber-T), informatics infrastructure for systems biology (SIGMOID) and, more recently, databases and tools in chemical informatics (ChemDB) for the prediction of molecular properties and applications in chemical synthesis, discovery, and drug design.

Tutorial 3 - Signal and Motif Detection

Presenter: Jagath C. Rajapaske, Nanyang Technological University, Singapore

Description

Signals of genomics sequences refer to specific sites relating to important biological phenomena, for example, transcription start sites (TSS), translation initiation sites (TIS), and splice sides (SS). The computational techniques to detect these signals are becoming popular because of the complexities and difficulties in determining these sites experimentally. This tutorial will introduce computational intelligence techniques, such as neural networks, genetic algorithms, and their hybrids for the detections of TSS, TIS, and SS.

Motifs in genomic sequences refer to short segments of DNA, which are conserved and have some important biological function. Most motifs in DNA sequences have regulatory functions, for examples, transcription factor binding sites (TFBS), promoters, and ribosome binding sites. Motif detection is a difficult problem in computational biology because the motif instances usually present in the sequences with a considerable number of degenerations. We discuss several approaches to motif detection, including profile analysis, neural network methods, the MEME approach, etc. We then proceed to discuss the graphical methods for week motif detection problem where classical techniques fail.

Biosketch of Presenter:

Dr. Jagath C. Rajapakse is an Associate Professor in the School of Computer Engineering (SCE) and the Deputy Director of the BioInformatics Research Centre (BIRC) at the Nanyang Technological University (NTU), Singapore. He is also a Visiting Professor at the Biological Engineering Division, Massachusetts Institute of Technology (MIT), USA. He received B.Sc. (Eng.) degree with First Class Honors in electronic and telecommunication engineering from the University of Moratuwa (Sri Lanka), and M.Sc. and Ph.D. degrees in electrical and computer engineering from the State University of New York at Buffalo (USA). Before joining NTU, he was a Visiting Fellow at the National Institute of Mental Health (Bethesda, USA) and a Visiting Scientist at the Max-Planck-Institute of Cognitive and Brain Sciences (Leipzig, Germany).

Dr. Rajapakse's research investigates human brain function, using imaging techniques and bioinformatics, leading to new drugs and behavioral or stem cell therapies for brain disease. His current research interests are on gene networks, protein interactions, neural systems, and pathways. He has authored over 200 research publications in refereed journals, books, and conference proceedings in the fields of brain imaging, computational biology, and machine learning. Thomson ISI Web of Science lists him among the most cited scientists of all fields over the decade 1996 2006.

He is an Associate Editor of the IEEE Transactions on Computational Biology and Bioinformatics and serves as a Symposium Co-Chair of 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2007) and the General Chair of 2nd IAPR Workshop on Pattern Recognition in Bioinformatics (PRIB 2007).