Journal•ISSN: 1947-9115
International Journal of Knowledge Discovery in Bioinformatics
IGI Global
About: International Journal of Knowledge Discovery in Bioinformatics is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Biological data & Biological network. It has an ISSN identifier of 1947-9115. Over the lifetime, 98 publications have been published receiving 815 citations. The journal is also known as: IJKDB & Knowledge discovery in bioinformatics.
Papers
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TL;DR: How spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network is described.
Abstract: This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, espec...
47 citations
TL;DR: 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naive Bayes, Sequential Minimal Optimization SMO, K-Nearest Neighbors KNN, Support Vector Machine SVM, and C4.5 are employed and the results show that the SMO algorithm yielded very high sensitivity 97.22% and accuracy 92.09% rates.
Abstract: One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease CAD is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naive Bayes, Sequential Minimal Optimization SMO, K-Nearest Neighbors KNN, Support Vector Machine SVM, and C4.5 and the results show that the SMO algorithm yielded very high sensitivity 97.22% and accuracy 92.09% rates.
40 citations
TL;DR: The proposed algorithm is based on transfer learning and can exploit local information effectively and is favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network.
Abstract: Inferring the relationship among proteins is a central issue of computational biology and a diversity of biological assays are utilized to predict the relationship. However, as experiments are usually expensive to perform, automatic data selection is employed to reduce the data collection cost. Although data useful for link prediction are different in each local sub-network, existing methods cannot select different data for different processes. This paper presents a new algorithm for inferring biological networks from multiple types of assays. The proposed algorithm is based on transfer learning and can exploit local information effectively. Each assay is automatically weighted through learning and the weights can be adaptively different in each local part.
34 citations
TL;DR: A novel integer programming-based method using a feedback vertex set (FVS) that can find an optimal set of reactions to be inactivated much faster than a naive IP- based method and several times faster than an flux balance-based methods.
Abstract: In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.
31 citations
TL;DR: Three distinctive machine learning calculations, for example, Support Vector Machine SVM, Maximum Entropy ME and Naive Bayes NB, have been considered for the arrangement of human conclusions.
Abstract: Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine SVM, Maximum Entropy ME and Naive Bayes NB, have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.
30 citations