Huzefa Rangwala

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MOTIVATION Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the(More)
Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and(More)
With recent advances in large scale sequencing technologies, we have seen an exponential growth in protein sequence information. Currently, our ability to produce sequence information far out-paces the rate at which we can produce structural and functional information. Consequently, researchers increasingly rely on computational techniques to extract useful(More)
Metagenomic assembly is a challenging problem due to the presence of genetic material from multiple organisms. The problem becomes even more difficult when short reads produced by next generation sequencing technologies are used. Although whole genome assemblers are not designed to assemble metagenomic samples, they are being used for metagenomics due to(More)
Determining protein function constitutes an exercise in integrating information derived from several heterogeneous high-throughput experiments. To utilize the information spread across multiple sources in a combined fashion, these data sources are transformed into kernels. Several protein function prediction methods follow a two-phased approach: they first(More)
Several studies indicate the importance of colonic microbiota in metabolic and inflammatory disorders and importance of diet on microbiota composition. The effects of alcohol, one of the prominent components of diet, on colonic bacterial composition is largely unknown. Mounting evidence suggests that gut-derived bacterial endotoxins are cofactors for(More)
Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to(More)
High-throughput experimental techniques produce several heterogeneous proteomic and genomic datasets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these data sources into different kernels or feature representations. Next, these kernels are linearly (or(More)
Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assumption that the labeled proteins are completely annotated, i.e., without any missing functions. However,(More)