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Int. J. Knowl. Discov. Bioinform.最新文献

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Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions 基于结构域的蛋白质相互作用预测和分析方法
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/ijkdb.2014010103
M. Hayashida, T. Akutsu
{"title":"Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions","authors":"M. Hayashida, T. Akutsu","doi":"10.4018/ijkdb.2014010103","DOIUrl":"https://doi.org/10.4018/ijkdb.2014010103","url":null,"abstract":"Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"588 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419681","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}
引用次数: 7
In Silico Biosimulation of Isoflurane Effects on Brain Using Transcriptome-To-Metabolome™ Technology: Anesthesia Effects on Rat Amygdala & Cortex Metabolism 利用转录组-代谢组™技术模拟异氟醚对大脑的影响:麻醉对大鼠杏仁核和皮质代谢的影响
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2015010101
Allen K. Bourdon, C. Phelix
{"title":"In Silico Biosimulation of Isoflurane Effects on Brain Using Transcriptome-To-Metabolome™ Technology: Anesthesia Effects on Rat Amygdala & Cortex Metabolism","authors":"Allen K. Bourdon, C. Phelix","doi":"10.4018/IJKDB.2015010101","DOIUrl":"https://doi.org/10.4018/IJKDB.2015010101","url":null,"abstract":"Anesthetics are a widely used class of drugs with a fast onset and comparatively slow offset, which consequently equates to a low therapeutic index. Unfortunately, the mechanism of action for this class of drugs is considered unknown. For that reason, there is great medical need to study effects of anesthetics on the brain. In this study transcriptomes, generated 6 hours after a 15 minute exposure to isoflurane, from the rat cortex and basolateral amygdala were used to determine parameters for a deterministic biosimulation model of metabolic pathways. Metabolomic results indicated involvement of lipid pathways known for anesthetic effects on membrane function and alternate energy sources due to reduced glucose utilization. Key insights are revealed for potential mechanisms by which anesthetics block memory of the medical procedures.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115184","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}
引用次数: 0
Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer's Disease via Subgraph Mining 通过子图挖掘了解阿尔茨海默病中人类连接组的中断空间模式
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/jkdb.2012010102
Junming Shao, Qinli Yang, A. Wohlschläger, C. Sorg
{"title":"Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer's Disease via Subgraph Mining","authors":"Junming Shao, Qinli Yang, A. Wohlschläger, C. Sorg","doi":"10.4018/jkdb.2012010102","DOIUrl":"https://doi.org/10.4018/jkdb.2012010102","url":null,"abstract":"Alzheimer's disease AD is the most common cause of age-related dementia, which prominently affects the human connectome. In this paper, the authors focus on the question how they can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: fiber density and fractional anisotropy, to represent the structural brain connectivity patterns. After frequent subgraph mining, the abnormal score was finally defined to identify disrupted subgraph patterns in patients. Experiments demonstrated that our data-driven approach, for the first time, allows identifying selective spatial pattern changes of the human connectome in AD that perfectly matched grey matter changes of the disease. Their findings also bring new insights into how AD propagates and disrupts the regional integrity of large-scale structural brain networks in a fiber connectivity-based way.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"590 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133806254","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}
引用次数: 5
Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus 妊娠期糖尿病早期风险评估的数据挖掘方法
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2018010101
Saeed Rouhani, Maryam MirSharif
{"title":"Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus","authors":"Saeed Rouhani, Maryam MirSharif","doi":"10.4018/IJKDB.2018010101","DOIUrl":"https://doi.org/10.4018/IJKDB.2018010101","url":null,"abstract":"Inthisarticle,theauthorsproposedthemethodofmedicaldiagnosisingestationaldiabetesmellitus (GDM)intheinitialstagesofpregnancytofacilitatediagnosesandpreventtheaffection.Nowadays, inindustrialmodernworldwithchanginglifestylealimentalmannertheincidenceofcomplexdisease hasbeenincreasinglygrown.GDMisachronicdiseaseandoneofthemajorhealthproblemsthat isoftendiagnosedinmiddleorlateperiodofpregnancy,whenitistoolateforprediction.Ifitis nottreated,itwillmakeseriouscomplicationsandvarioussideeffectsformotherandchild.This articleisdesignedforansweringtothequestionof:“Whatisthebestapproachintimelyandaccurate predictionofGDM?”Thus,theartificialneuralnetworkanddecisiontreeareproposedtoreducethe amountoferrorandthelevelofaccuracyinanticipatingandimprovingtheprecisionofprediction. Theresultsillustratethatintelligentdiagnosissystemscanimprovethequalityofhealthcare,timely prediction,prevention,andknowledgediscoveryinbioinformatics. KEywoRDS Artificial Neural Network, Data Mining, Decision Tree, GDM, Risk Assessment","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130153103","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}
引用次数: 4
Spam Detection on Social Media Using Semantic Convolutional Neural Network 基于语义卷积神经网络的社交媒体垃圾邮件检测
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2018010102
Gauri Jain, Manisha Sharma, Basant Agarwal
{"title":"Spam Detection on Social Media Using Semantic Convolutional Neural Network","authors":"Gauri Jain, Manisha Sharma, Basant Agarwal","doi":"10.4018/IJKDB.2018010102","DOIUrl":"https://doi.org/10.4018/IJKDB.2018010102","url":null,"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, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128632755","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}
引用次数: 52
New Trends in Graph Mining: Structural and Node-Colored Network Motifs 图挖掘的新趋势:结构和节点着色网络母题
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/JKDB.2010100206
Francesco Bruno, L. Palopoli, Simona E. Rombo
{"title":"New Trends in Graph Mining: Structural and Node-Colored Network Motifs","authors":"Francesco Bruno, L. Palopoli, Simona E. Rombo","doi":"10.4018/JKDB.2010100206","DOIUrl":"https://doi.org/10.4018/JKDB.2010100206","url":null,"abstract":"Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modules across the same network (or several distinct ones) is shown to be very relevant. Indeed, several studies prove that biological networks can be often understood in terms of coalitions of basic repeated building blocks, often referred to as network motifs.This work provides a review of the main techniques proposed for motif extraction from biological networks. In particular, main intrinsic difficulties related to the problem are pointed out, along with solutions proposed in the literature to overcome them. Open challenges and directions for future research are finally discussed.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126190041","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}
引用次数: 11
Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis n-gram方法在烂番茄评论数据集情感分析中的实现
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2017010103
P. Tiwari, B. K. Mishra, Sachin Kumar, Vivek Kumar (Ph.D)
{"title":"Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis","authors":"P. Tiwari, B. K. Mishra, Sachin Kumar, Vivek Kumar (Ph.D)","doi":"10.4018/IJKDB.2017010103","DOIUrl":"https://doi.org/10.4018/IJKDB.2017010103","url":null,"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.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421101","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}
引用次数: 31
Efficient Fault Tolerant Algorithms for Internet Distributed Systems 互联网分布式系统的高效容错算法
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2017010106
Swati Mishra, S. K. Panda
{"title":"Efficient Fault Tolerant Algorithms for Internet Distributed Systems","authors":"Swati Mishra, S. K. Panda","doi":"10.4018/IJKDB.2017010106","DOIUrl":"https://doi.org/10.4018/IJKDB.2017010106","url":null,"abstract":"In the current era, Internet is the fastest growing technology. It is a global network of distributed systems that interconnects with each other to share various resources and computation. The complexity of the distributed systems is rapidly increasing, which leads to an increased susceptibility of failure to the participating computers such as clients and servers and their interconnections. Therefore, the main challenge is to address such failures and develop efficient algorithms to tolerate such failures. In this paper, four fault tolerant algorithms, namely server-based fault tolerant SFT, client-based fault tolerant CFT, client-server fault tolerant CSFT and connection fault tolerant CoFT are proposed to deal with the above challenge. The proposed algorithms are evaluated in terms of number of failures NOF, load factor LF and load standard deviation LSD and compared with an existing algorithm. The comparison results show the superior performance of the proposed algorithms.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134477636","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}
引用次数: 2
Green DRCT: Measuring Energy Consumption of an Enhanced Branch Coverage and Modified Condition/Decision Coverage Technique 绿色DRCT:测量增强分支覆盖和改进条件/决策覆盖技术的能耗
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2017010102
Sangharatna Godboley, Arpita Dutta, D. Mohapatra
{"title":"Green DRCT: Measuring Energy Consumption of an Enhanced Branch Coverage and Modified Condition/Decision Coverage Technique","authors":"Sangharatna Godboley, Arpita Dutta, D. Mohapatra","doi":"10.4018/IJKDB.2017010102","DOIUrl":"https://doi.org/10.4018/IJKDB.2017010102","url":null,"abstract":"Being a good software testing engineer, one should have the responsibility towards environment sustainability. By using green principles and regulations, we can perform Green Software Testing. In this paper, we present a new approach to enhance Branch Coverage and Modified Condition/Decision Coverage uses concolic testing. We have proposed a novel transformation technique to improve these code coverage metrics. We have named this new transformation method Double Refined Code Transformer DRCT. Then, using JoulMeter, we compute the power consumption and energy consumption in this testing process. We have developed a tool named Green-DRCT to measure energy consumption while performing the testing process.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132858210","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}
引用次数: 0
Performance Assessment of Learning Algorithms on Multi-Domain Data Sets 多领域数据集上学习算法的性能评估
Int. J. Knowl. Discov. Bioinform. Pub Date : 1900-01-01 DOI: 10.4018/IJKDB.2018010103
Amit Kumar, B. K. Sarkar
{"title":"Performance Assessment of Learning Algorithms on Multi-Domain Data Sets","authors":"Amit Kumar, B. K. Sarkar","doi":"10.4018/IJKDB.2018010103","DOIUrl":"https://doi.org/10.4018/IJKDB.2018010103","url":null,"abstract":"","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"18 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133636151","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}
引用次数: 0
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