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Hypertension is a multifactorial and multi-gene abnormality that affects 25 percent of the world's population. Thus, in the last decades vast research has been conducted in order to determine the mechanisms of that disorder. This lead to the introduction of pathways that describe those mechanisms. Using that knowledge, scientists were able to design drugs that act directly on the pathway in order...
Selecting drug targets in pathway and genetic diseases (e.g., cancer) is a difficult problem facing the medical field and pharmaceutical industry. Because of the complex interconnections and feedback found in biological pathways, it is difficult to understand the potential effects of targeting certain portions of the network. The pharmaceutical industry has avoided novel targets for drugs, largely...
In this work we propose a hybrid learning machine, combining artificial neural networks (ANNs) and binary decision trees, to predict quantitative structure activity relationships (QSARs). This approach directly uses the structural cues from chemical compounds and has been validated for the two significant prediction problems, viz. regression and classification. For regression analysis we show the...
Many advances in medicine, but the occurrence of errors that accompany the completion of forms are inevitable for the human condition [3]. An information system is to achieve the prescription indicating possible drug interactions, reducing a large number of incidents related to medical errors. The implementation of the system also reduces the time of hospital beds and administrative costs, allowing...
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as active or inactive with respect to a specific target biological system. This paper presents a comparison of artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) in an effort to identify potentiators...
Molecular docking technology is an important tool in computer-aided drug discovery and structure prediction for the protein-ligand complex. In this work, based on the analysis of the algorithm of the widely used the docking program AutoDock, we proposed a hybrid parallel method using the message passing interface (MPI) library. The modified programs were applied to dock the small molecule XK263 to...
The prediction of biological activity of a chemical compound from its structural features, representing its physico-chemical properties, plays an important role in drug discovery, design and development. Since the biological data is highly non-linear, the machine-learning techniques have been widely used for modeling it. In the present work, the clustering, genetic algorithm (GA) and artificial neural...
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