Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for e...

We start from a simple asymptotic result for the problem of online regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a “leading prediction strategy”, which not only asymptotically performs at least as well as any continuous limited-memory strategy but also satisfies the property that the excess loss of any continuous limited-memory ...

Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk predicti...

The topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on vario...

Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an effort to investigat...

Ginsenoside F1 is a biologically active compound identified potential from Korean Panax ginseng Meyer. In the present study, the potential targets of ginsenoside F1 were investigated by computational target fishing approaches including ADMET prediction, biological activity prediction from chemical structure, molecular docking, and molecular dynamics methods. Results were suggested to express th...

At the mo ment, S upport Ve ctor Machine ( SVM) has been widely u sed i n t he study of stock investment related topics. Stock investment can be further divided into three s trategies such as: buy, sell and hold. Using data concerning China Steel Corporation, this article adopts genetic algorithm for the search of the best SVM parameter and the selection of the best SVM prediction variable, the...

A prediction method of protein disulfide bond based on support vector machine and sample selection is proposed in this paper. First, the protein sequences selected are encoded according to a certain encoding, input data for the prediction model of protein disulfide bond is generated; Then sample selection technique is used to select a portion of input data as training samples of support vector ...

To understand the brain mechanisms involved in reward prediction on different time scales, we developed a Markov decision task that requires prediction of both immediate and future rewards, and analyzed subjects’ brain activities using functional MRI. We estimated the time course of reward prediction and reward prediction error on different time scales from subjects' performance data, and used ...

Protein structure prediction from sequence remains a premiere computational problem for modern molecular biology. Just as protein structure prediction may be divided into sub-problems of main-chain and side-chain placement, so the protein structure prediction track this year has been divided into sub-tracks of protein threading (organized by Daniel Fischer and Adam Godzik) and side-chain packin...