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High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically,...
Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissuefree biological networks that lacks phenotypic...
Background The earliest whole protein order/disorder predictor (Uversky et al., Proteins, 41: 415-427 (2000)), herein called the charge-hydropathy (C-H) plot, was originally developed using the Kyte-Doolittle (1982) hydropathy scale (Kyte & Doolittle., J. Mol. Biol, 157: 105-132(1982)). Here the goal is to determine whether the performance of the C-H plot in separating structured and disordered...
Some target tracking occasions often requires to tracking a kind of target, such as human face, automobile and so on. A specific target tracking algorithm based on support vector machine (SVM) and AdaBoost is proposed. Moreover, the characteristic data of SVM is a critical factor to success to detecting target. The method selects part of Harr wavelet characters by AdaBoost as input data of SVM training...
It is very important to target detecting hardware and algorithm design in the field of target tracking field. A kind of detection algorithm using SVM (support vector machine) and AdaBoost which selects representative Harr characters is proposed. SVM uses selected Harr wavelet characters as input data in training and classifying procedure. In order to accelerate SVM classify and detect speed, the cascade...
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