Indika Kahanda

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Many scientific fields analyzing and modeling social networks have focused on manually-collected datasets where the friendship links are sparse (due to the costs of collection) but relatively noise-free (i.e. they indicate strong relationships). In online social networks, where the notion of “friendship” is broader than what would generally be considered in(More)
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for(More)
Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine(More)
The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes.(More)
BACKGROUND The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins(More)
Suggesting friends is a very important aspect in any online social network. In this paper, we present a relational similarity model for suggesting friends in online social networks, which uses relational features as opposed to the non-relational features that are used in current friend suggestion applications. We take a supervised learning approach and(More)
We consider the problem of identifying regions within a pan-genome de Bruijn graph that are traversed by many sequence paths. We define such regions and the subpaths that traverse them as frequented regions (FRs). In this work we formalize the FR problem and describe an efficient algorithm for finding FRs. Subsequently, we propose some applications of FRs(More)
Automated Protein Function Prediction is the task of automatically predicting functional annotations for a protein based on gold-standard annotations derived from experimental assays. These experiment-based annotations accumulate over time: proteins without annotations get annotated, and new functions of already annotated proteins are discovered. Therefore,(More)
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