Validation of novel identification algorithms for major adverse cardiovascular events in a Japanese claims database.

Predicting clinical outcomes can be difficult, particularly for life-threatening events with a low incidence that require numerous clinical cases. Our aim was to develop and validate novel algorithms to identify major adverse cardiovascular events (MACEs) from claims databases. We developed algorithms based on the data available in the claims database International Classification of Diseases, Tenth Revision (ICD-10), drug prescriptions, and medical procedures.

Differential impact of diabetes mellitus on antiplatelet effects of prasugrel and clopidogrel.

Background: Although prasugrel exerts stronger antiplatelet effects compared with clopidogrel, the factors affecting platelet reactivity under prasugrel have not been fully determined. This study aimed to find the novel mechanistic differences between two thienopyridines and identify the factor that influence platelet reactivity to each drug.

Methods: Forty patients with stable angina who underwent elective percutaneous coronary intervention were randomly assigned to receive either prasugrel (20 mg) or clopidogrel (300 mg) as a loading dose.

Long sleep duration: a nonconventional indicator of arterial stiffness in Japanese at high risk of cardiovascular disease: the J-HOP study.

Long and short sleep durations were reported as independently associated with hypertension, aortic stiffness, and cardiovascular disease. High-sensitivity C-reactive protein (hs-CRP) was shown to be associated with increased aortic stiffness. Here, we investigated the relationship between self-reported sleep duration and pulse wave velocity (PWV) in the elderly at high risk of cardiovascular disease.

CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions.

With advancements in high-throughput technologies and open availability of bioassay data, computational methods to generate models, that zoom out from a single protein with a focused ligand set to a larger and more comprehensive description of compound-protein interactions and furthermore demonstrate subsequent translational validity in prospective experiments, are of prime importance. In this article, we discuss some of the new benefits and challenges of the emerging computational chemogenomics paradigm, particularly with respect to compound-protein interaction. Examples of experimentally validated computational predictions and recent trends in molecular feature extraction are presented.

Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants.

Chemical genomics research has revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale.

Dissecting kinase profiling data to predict activity and understand cross-reactivity of kinase inhibitors.

The development of selective and multitargeted kinase inhibitors has received much attention, because cross-reactivity with unintended targets may cause toxic side effects, while it can also give rise to efficacious multitargeted drugs. Here we describe a deconvolution approach to dissecting kinase profiling data in order to gain knowledge about cross-reactivity of inhibitors from large-scale profiling data. This approach not only enables activity predictions of given compounds on a kinome-wide scale, but also allows to extract residue-fragment pairs that are associated with activity.

Platinum agent-induced hypersensitivity reactions: data mining of the public version of the FDA adverse event reporting system, AERS.

Objective: Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to confirm the platinum agent-associated mild, severe, and lethal hypersensitivity reactions.

Methods: Authorized pharmacovigilance tools were used for quantitative signal detection, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Excess2, given by the multi-item gamma Poisson Shrinker algorithm, was used to evaluate the effects of dexamethasone and diphenhydramine on oxaliplatin-induced hypersensitivity reactions.

Analysis of multiple compound-protein interactions reveals novel bioactive molecules.

The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound-protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored.

Cross-target view to feature selection: identification of molecular interaction features in ligand-target space.

There is growing interest in computational chemogenomics, which aims to identify all possible ligands of all target families using in silico prediction models. In particular, kernel methods provide a means of integrating compounds and proteins in a principled manner and enable the exploration of ligand-target binding on a genomic scale. To better understand the link between ligands and targets, it is of fundamental interest to identify molecular interaction features that contribute to prediction of ligand-target binding.

Laplacian linear discriminant analysis approach to unsupervised feature selection.

Until recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis.

GEM-TREND: a web tool for gene expression data mining toward relevant network discovery.

Background: DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In recent years, vast amounts of gene expression data have been collected, much of which are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene-expression patterns are not considered when retrieving such data.

GLIDA: GPCR--ligand database for chemical genomics drug discovery--database and tools update.

G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GLIDA is a public GPCR-related Chemical Genomics database that is primarily focused on the integration of information between GPCRs and their ligands. It provides interaction data between GPCRs and their ligands, along with chemical information on the ligands, as well as biological information regarding GPCRs.

Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE.

Background: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin.

Multiclass molecular cancer classification by kernel subspace methods with effective kernel parameter selection.

Microarray techniques provide new insights into molecular classification of cancer types, which is critical for cancer treatments and diagnosis. Recently, an increasing number of supervised machine learning methods have been applied to cancer classification problems using gene expression data. Support vector machines (SVMs), in particular, have become one of the most effective and leading methods.