The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high throughput sequencing technologies in the life sciences. It is a key question in the life sciences to identify the functions of proteins, and furthermore to identify the phenotypes that may be associated with a loss (or gain) of function in these proteins. Protein functions are generally determined experimentally, and it is clear that the experimental determination of protein functions will not scale to the current --and rapidly increasing -- the amount of available protein sequences (over 300 million). Furthermore, identifying phenotypes resulting from loss of function is even more challenging as the phenotype is modified by whole organism interactions and environmental variables. It is clear that accurate computational prediction of protein functions and loss of function phenotypes would be of significant value both to academic research and to the biotechnology industry.
Maxat Kulmanov is a Ph.D. candidate at King Abdullah University of Science and Technology. His interests are bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He develops methods for knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. His research topic is "Predicting Gene Knockout Phenotypes."
Refreshments: Light lunch will be available at 11:45 AM