אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used in practice. Most of the machine learning methods applied to this problem reduce it to a collection of binary classification problems: whether a protein performs a particular function, sometimes with a post-processing step to combine the binary outputs. I will present the GOstruct method that directly predicts the Gene Ontology terms associated with a protein by modeling the hierarchical structure of the Gene Ontology in the framework of kernel methods for structured-output spaces. Empirical results show improved performance over several algorithms that were recently benchmarked on the Mousefunc dataset.

Host: Tomer Shlomi