Artificial Intelligence and Heuristic 
Methods for Bioinformatics

A NATO Advanced Studies Institute

San Miniato, Italy    October 1-11, 2001

Co-directors: Paolo Frasconi (University of Florence) Ron Shamir (Tel Aviv University)

New Book: Artificial Intelligence and Heuristic Methods for Bioinformatics published by IOS Press. Table of Contents

Home
Schedule and program of lectures
Online lecture notes and links
Submitted papers
Organizing Committee and Lecturers
Participation and Application form
Location, traveling, accommodation
Sponsors
Contact: ai4bio@dsi.unifi.it

 

Artificial Intelligence and heuristic methods are extremely important for the present and future developments of bioinformatics, a very recent and strategic discipline having the potential for a revolutionary impact on biotechnology, pharmacology, and medicine. While computation has already transformed our industrial society, a comparable biotechnological transformation is on the horizon. In the last few years it has become clear that these two exponentially growing areas are actually converging. 

Molecular biologists are currently engaged in some of the most impressive data collection projects. Recent genome-sequencing projects are generating an enormous amount of data related to the function and the structure of biological molecules and sequences. Other complementary high-throughput technologies, such as DNA micro-arrays, are rapidly generating large amounts of data that are too overwhelming for conventional approaches to biological data analysis. The interpretation of this wealth of data may deeply affect our understanding of life at the molecular level, but the elicitation and the representation of biological knowledge are extremely challenging tasks, which are increasingly demanding powerful and sophisticated computational tools. 

AI and heuristic methods (in particular machine learning and data mining, cluster analysis, pattern recognition, knowledge representation) can provide key solutions for the new challenges posed by the progressive transformation of biology into a data-massive science. Important problems where AI approaches are particularly promising (and often already very successful) include the prediction of protein's structure and function, semiautomatic drug design, the interpretation of nucleotide sequences, and knowledge acquisition from genetic data. 

The application of AI to computational molecular biology requires highly interdisciplinary and complementary competencies which are rarely offered by most current academic curricula. While the need for interdisciplinarity certainly creates excitement, it is also an obstacle for the rapid development of new methodologies and algorithms, unless the AI and the computational biology communities keep very closely in touch. In order to make an optimal use of intelligent computational tools, researchers will need the expertise to combine intelligent information technology and biology in a productive way. This cannot be achieved without deepening and fostering the collaboration between experts in allied fields.

The main objective of this school is to create an environment for (1) cross-disseminating state-of-the-art knowledge both to AI researchers and computational biologists; (2) creating a common substrate of knowledge that both AI people and computational biologists can understand; (3) stimulating the development of specialized AI techniques, keeping in mind the application to computational biology; (4) fostering new collaborations among scientists having similar or complementary backgrounds.

Topics of the lectures delivered by internationally known scientists will include: 

  • Computational analysis of biological data
  • Artificial intelligence, machine learning, and heuristic methods, including neural and belief networks
  • Prediction of protein structure (secondary structure, contact maps)
  • The working draft of the human genome
  • Genome annotation
  • Computational tools for gene regulation
  • Analysis of gene expression data and their applications
  • Computer assisted drug discovery
  • Knowledge discovery in biological domains