IPM-NUS Workshop on Analysis and Application of Protein Interaction Networks
Shahid Beheshti University
17 & 18 November 2008

Full program of the workshop / A ppt about the School of Computing at NUS.

I gave several invited talks and a tutorial at the workshop above. Here are the details...

Talk A: Increasing Confidence of Protein-Protein Interactomes

High-throughput experimental methods, such as yeast-two-hybrid and phage display, have fairly high levels of false positives (and false negatives). Thus the list of protein-protein interactions detected by such experiments would need additional wet laboratory validation. Advances in computational techniques for assessing the reliability of protein-protein interactions detected by such high-throughput methods are reviewed in this talk, with a focus on techniques that rely only on topological information of the protein interaction network derived from such high-throughput experiments.

Talk B: Identifying Protein Complexes from Protein Interactome Maps

Protein complexes are fundamental for understanding principles of cellular organizations. However, most protein interactome maps are still essentially an in vitro scaffold. Further these protein interactome maps contain a significant amount of noise interactions, as well as missing many real interactions. It is thus an important challenge to reliably deduce in vivo protein interactions and to identify membership in the same protein complexes. In this talk, we describe recent progress in computational techniques for protein complex prediction from noisy protein interaction network data.

Talk C: Guilt by Association of Common Interaction Partners

A central problem of computational biology is the inference of the function of a protein. The traditional computational approach to this problem is based on the principle of "guilt by association" of sequence similarity. This approach works for about 40-60% of the proteins in a typical proteome. In this talk, we discuss the inference of function for the other proteins that lack informative sequence similarity to proteins with known function. We present guilt by association of common friends --- that two proteins sharing a large number of common interaction partners are likely to share a common function. Furthermore, we develop a means to exploit this property to effectively assign functions to proteins in the absence of sequence similarity. In order to fully exploit additional information that is available on some proteins, we also develop an efficient powerful information fusion technique to infer protein functions through guilt by association of multiple information types.

Guilt by Association: A Tutorial on Data Mining Techniques for Protein Function Inference

One of the most important problems in computational biology is the reliable assignment of functions to protein sequences. This tutorial on protein function prediction has two objectives. The first objective is to show the attendees the breadth of the many different machine learning and data mining approaches that have been developed for this problem, and also to highlight the golden thread of ``guilt by association'' that runs through all of these alternative approaches. The second objective is to illustrate some of the important interpretational skills that a good bioinformaticist must have in order to properly draw conclusions from the output of such computational algorithms.

Full program of the workshop / A ppt about the School of Computing at NUS.

I also visited the University of Tehran Institute of Biochemistry and Biophysics. Here is the talk I gave there:

Adventures of a Logician-Engineer: A Journey through Logic, Engineering, Medicine, Biology

Whenever a programmer writes a loop, or a mathematician does a proof by induction, an invariant is involved. The discovery and understanding of invariants often underlies problem solving in many domains. I will discuss here my search for powerful invariants over the past decade. My search was/is motivated by a broad spectrum of problems: understanding query languages, engineering data integration systems, optimising disease treatments, recognizing DNA feature sites, and discovering reliable patterns. In the course of my talk, you will discover some of the most powerful and unexpected invariants in logic, engineering, medicine, and biology.