Learn More
High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit(More)
OBJECTIVE The safety profiles of oral fluoropyrimidines were compared with 5-fluorouracil (5-FU) using adverse event reports (AERs) submitted to the Adverse Event Reporting System, AERS, of the US Food and Drug Administration (FDA). METHODS After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving 5-FU and oral(More)
High-throughput assays challenge us to extract knowledge from multi-ligand, multi-target activity data. In QSAR, weights are statically fitted to each ligand descriptor with respect to a single endpoint or target. However, computational chemogenomics (CG) has demonstrated benefits of learning from entire grids of data at once, rather than building(More)
In this paper, we present several methods for computing a solution to the protein side chain packing problem, with all methods having a common solution approach of breaking the polymer into subpolymers and using maximum edge weight cliques to prune the search space for the optimal side chain packing. We characterize the graph sizes generated for each method(More)
OBJECTIVE Prognoses of ovarian cancer (OC) have improved with the paclitaxel-carboplatin regimen. However, it remains unclear which cases exhibit a genuine benefit from taxane or from platinum. We aimed to predict taxane and platinum sensitivity in OC via gene expression. METHODS We identified differentially expressed genes in responsive and resistant(More)
The protein side-chain packing problem is computationally known to be NP-complete [1]. A number of approaches has been proposed for side-chain packing. As the size of the protein becomes larger, the sampling space increases exponentially. Hence, large scale protein side-chain packing In this regard, we had also presented a maximum edge-weight clique based(More)
The protein side-chain packing problem (SCPP) is known to be NP-complete. Various graph theoretic based side-chain packing algorithms have been proposed. However as the size of the protein becomes larger, the sampling space increases exponentially. Hence, one approach to cope with the time complexity is to decompose the graph of the protein into smaller(More)
DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic(More)
AIM Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively(More)
The identification of new compound-protein interactions has long been the fundamental quest in the field of medicinal chemistry. With increasing amounts of biochemical data, advanced machine learning techniques such as active learning have been proven to be beneficial for building high-performance prediction models upon subsets of such complex data. In a(More)