{"title":"Analysis of microRNA Regulated Seed Biology Networks in Arabidopsis","authors":"Anamika Basu, Anasua Sarkar, Piyali Basak","doi":"10.4018/IJKDB.2014070102","DOIUrl":"https://doi.org/10.4018/IJKDB.2014070102","url":null,"abstract":"Seed maturation and embryogenesis in plants are crucial event for food production of all human beings. Delayed seed maturation and abnormal embryo formation of food crops degrade the quality and quantity of food grains. By performing comparative gene analysis of different microarray experiments in different stages of embryogenesis in Arabidopsis thaliana, using as model plant, here the authors identified a gene coexpression module in preglobular stage. In this module, different genes have been studied which are over-expressed during embryogenesis related with several KEGG metabolic pathways. Analysing the gene cluster evolved from network we concluded that microRNA regulates gene expression of two genes. One of them NRMP6, a metal ion transporter protein gene and second one SKS8, has copper ion binding activity, are regulated by miR167A/B. Since these two genes are also expressed during embryogenesis of other food crops e.g. rice tomato etc, so the microRNAs regulation on gene expression during embryogenesis can be extrapolated for other economically important seeds.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predicting Protein Functions from Protein Interaction Networks","authors":"H. Chua, L. Wong","doi":"10.4018/ijkdb.2012100104","DOIUrl":"https://doi.org/10.4018/ijkdb.2012100104","url":null,"abstract":"Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of assigning functional annotations to proteins in a high-throughput manner. In this article, the authors provide an introduction to the task of automated protein function prediction. They discuss about the motivation for automated protein function prediction, the challenges faced in this task, as well as some approaches that are currently available. In particular, they take a closer look at methods that use protein-protein interaction for protein function prediction, elaborating on their underlying techniques and assumptions, as well as their strengths and limitations.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114365323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data Mining for Biologists","authors":"K. Tsuda","doi":"10.4018/ijkdb.2012100101","DOIUrl":"https://doi.org/10.4018/ijkdb.2012100101","url":null,"abstract":"In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. The author explains technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prioritizing Disease Genes and Understanding Disease Pathways","authors":"Xiaoyue Zhao, L. Iakoucheva, Michael Q. Zhang","doi":"10.4018/ijkdb.2012100103","DOIUrl":"https://doi.org/10.4018/ijkdb.2012100103","url":null,"abstract":"Genetic factors play a major role in the etiology of many human diseases. Genome-wide experimental methods produce an increasing number of genes associated with such diseases. This article introduces data sources, bioinformatics tools, and computational methods for prioritizing disease candidate genes and identifying disease pathways. The main strategy is to examine the similarity among the candidate genes and known disease genes at the functional level. The authors review different similarity measures and prevailing methods for integrating results from different functional aspects. The authors hope this article will help advocate many useful resources that the researchers can use to investigate diseases of their interest.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126145204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Protein Interactions for Functional Genomics","authors":"Pablo Minguez, J. Dopazo","doi":"10.4018/ijkdb.2012100102","DOIUrl":"https://doi.org/10.4018/ijkdb.2012100102","url":null,"abstract":"Here the authors review the state of the art in the use of protein-protein interactions ppis within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins nodes and pairwise interactions edges. These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114868061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Healthcare Data Mining: Predicting Hospital Length of Stay (PHLOS)","authors":"A. Azari, V. Janeja, A. Mohseni","doi":"10.4018/jkdb.2012070103","DOIUrl":"https://doi.org/10.4018/jkdb.2012070103","url":null,"abstract":"A model to predict the Length of Stay LOS for hospitalized patients can be an effective tool for measuring the consumption of hospital resources. Such a model will enable early interventions to prevent complications and prolonged LOS and also enable more efficient utilization of manpower and facilities in hospitals. In this paper, the authors propose an approach for Predicting Hospital Length of Stay PHLOS using a multi-tiered data mining approach. In their aproach, the authors form training sets, using groups of similar claims identified by k-means clustering and perfom classification using ten different classifiers. The authors provide a combined measure of performance to statistically evaluate and rank the classifiers for different levels of clustering. They consistently found that using clustering as a precursor to form the training set gives better prediction results as compared to non-clustering based training sets. The authors have also found the accuracies to be consistently higher than some reported in the current literature for predicting individual patient LOS. Binning the LOS to three groups of short, medium and long stays, their method identifies patients who need aggressive or moderate early interventions to prevent prolonged stays. The classification techniques used in this study are interpretable, enabling them to examine the details of the classification rules learned from the data. As a result, this study provides insight into the underlying factors that influence hospital length of stay. They also examine the authors' prediction results for three randomly selected conditions with domain expert insights.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133925184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yupu Liang, Dina Sokol, Sarah Zelikovitz, Sarah Ita Levitan
{"title":"Classification of Tandem Repeats in the Human Genome","authors":"Yupu Liang, Dina Sokol, Sarah Zelikovitz, Sarah Ita Levitan","doi":"10.4018/jkdb.2012070101","DOIUrl":"https://doi.org/10.4018/jkdb.2012070101","url":null,"abstract":"Tandem repeats in DNA sequences are extremely relevant in biological phenomena and diagnostic tools. Computational programs that discover these tandem repeats generate a huge volume of data, which is often difficult to decipher without further organization. In this paper, the authors describe a new method for post-processing tandem repeats through clustering and classification. Their work presents multiple ways of expressing tandem repeats using the n-gram model with different clustering distance measures. Analysis of the clusters for the tandem repeats in the human genome shows that the method yields a well-defined grouping in which similarity among repeats is apparent. The authors' new, alignment-free method facilitates the analysis of the myriad of tandem repeats that occur in the human genome and they believe that this work will lead to new discoveries on the roles, origins, and significance of tandem repeats.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121848585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Luis Notari, Samuel Brando Oldra, M. A. Mariani, C. Reolon, D. Bonatto
{"title":"Dis2PPI: A Workflow Designed to Integrate Proteomic and Genetic Disease Data","authors":"Daniel Luis Notari, Samuel Brando Oldra, M. A. Mariani, C. Reolon, D. Bonatto","doi":"10.4018/jkdb.2012070104","DOIUrl":"https://doi.org/10.4018/jkdb.2012070104","url":null,"abstract":"Experiments in bioinformatics are based on protocols that employ different steps for data mining and data integration, collectively known as computational workflows. Considering the use of databases in the biomedical sciences software that is able to query multiple databases is desirable. Systems biology, which encompasses the design of interactomic networks to understand complex biological processes, can benefit from computational workflows. Unfortunately, the use of computational workflows in systems biology is still very limited, especially for applications associated with the study of disease. To address this limitation, we designed Dis2PPI, a workflow that integrates information retrieved from genetic disease databases and interactomes. Dis2PPI extracts protein names from a disease report and uses this information to mine protein-protein interaction PPI networks. The data gathered from this mining can be used in systems biology analyses. To demonstrate the functionality of Dis2PPI for systems biology analyses, the authors mined information about xeroderma pigmentosum and Cockayne syndrome, two monogenic diseases that lead to skin cancer when the patients are exposed to sunlight and neurodegeneration.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125154774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Kuncheva, Juan José Rodríguez Diez, Y. Syed, C. Phillips, K. Lewis
{"title":"Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air","authors":"L. Kuncheva, Juan José Rodríguez Diez, Y. Syed, C. Phillips, K. Lewis","doi":"10.4018/JKDB.2012040101","DOIUrl":"https://doi.org/10.4018/JKDB.2012040101","url":null,"abstract":"The diagnosis of Chronic Obstructive Pulmonary Disease COPD is based on symptoms, clinical examination, exposure to risk factors smoking and certain occupational dusts and confirming lung airflow obstruction on spirometry. However, most people with COPD remain undiagnosed and controversies regarding spirometry persist. Developing accurate and reliable automated tests for the early diagnosis of COPD would aid successful management. We evaluated the diagnostic potential of a non-invasive test of chemical analysis volatile organic compounds-VOCs from exhaled breath. We applied 26 individual classifier methods and 30 state-of-the-art classifier ensemble methods to a large VOC data set from 109 patients with COPD and 63 healthy controls of similar age; we evaluated the classification error, the F measure and the area under the ROC curve AUC. The results show that classifying the VOCs leads to substantial gain over chance but of varying accuracy. We found that Rotation Forest ensemble AUC 0.825 had the highest accuracy for COPD classification from exhaled VOCs.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evidence-Based Combination of Weighted Classifiers Approach for Epileptic Seizure Detection using EEG Signals","authors":"Abduljalil Mohamed, K. Shaban, A. Mohamed","doi":"10.4018/jkdb.2012040103","DOIUrl":"https://doi.org/10.4018/jkdb.2012040103","url":null,"abstract":"Different brain states and conditions can be captured by electroencephalogram EEG signals. EEG-based epileptic seizure detection techniques often reduce these signals into sets of discriminant features. In this work, an evidence theory-based approach for epileptic detection, using several classifiers, is proposed. Within the framework of the evidence theory, each of these classifiers is considered a source of information and given a certain weight based on both its overall classification accuracy as well as its precision rate for the respective brain state. These sources are fused using the Dempster's rule of combination. Experimental work is done where five time domain features are obtained from EEG signals and used by a set classifiers, namely, Bayesian, K-nearest neighbor, neural network, linear discriminant analysis, and support vector machine classifiers. Higher classification accuracy of 89.5% is achieved, compared to 75.07% and 87.71% accuracy obtained from the worst and best used classifiers.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121889072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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