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Int. J. Knowl. Discov. Bioinform.最新文献

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Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System 基于机器学习的计划，以防止住院和降低哥伦比亚法定医疗保健系统的成本
Int. J. Knowl. Discov. Bioinform. Pub Date : 2018-07-01 DOI: 10.4018/IJKDB.2018070103
Alvaro J. Riascos, Natalia Serna
{"title":"Machine Learning Based Program to Prevent Hospitalizations and Reduce Costs in the Colombian Statutory Health Care System","authors":"Alvaro J. Riascos, Natalia Serna","doi":"10.4018/IJKDB.2018070103","DOIUrl":"https://doi.org/10.4018/IJKDB.2018070103","url":null,"abstract":"Health-care systems that rely on hospitalization for early patient treatment pose a financial concern for governments. In this article, the author suggests a hospitalization prevention program in which the decision of whether to intervene on a patient depends on a simple decision model and the prediction of the patient risk of an annual length-of-stay using machine learning techniques. These results show that the prevention program achieves significant cost savings relative to several base scenarios for program efficacies greater than or equal to 40% and intervention costs per patient of 100,000 to 700,000 Colombian pesos (i.e., approximately 14% to 100% of the average cost per patient in Colombia statuary health care system). This article also shows how tree-based methods outperform linear regressions when predicting an annual length-of-stay and the final model achieves a lower out-of-sample error compared to those of the Heritage Health Prize.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701142","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
Augmenting Medical Decision Making With Text-Based Search of Teaching File Repositories and Medical Ontologies: Text-Based Search of Radiology Teaching Files 基于文本的教学文件库和医学本体检索增强医疗决策:基于文本的放射学教学文件检索
Int. J. Knowl. Discov. Bioinform. Pub Date : 2018-07-01 DOI: 10.4018/IJKDB.2018070102
P. Deshpande, A. Rasin, Eli T. Brown, J. Furst, S. Montner, S. Armato, D. Raicu
{"title":"Augmenting Medical Decision Making With Text-Based Search of Teaching File Repositories and Medical Ontologies: Text-Based Search of Radiology Teaching Files","authors":"P. Deshpande, A. Rasin, Eli T. Brown, J. Furst, S. Montner, S. Armato, D. Raicu","doi":"10.4018/IJKDB.2018070102","DOIUrl":"https://doi.org/10.4018/IJKDB.2018070102","url":null,"abstract":"Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many teaching files are also publicly available online, some linked to secondary sources. However, public sources offer very limited (and ad-hoc) search capabilities. Based on the previous work on data integration and text-based search, the authors extended their Integrated Radiology Image Search (IRIS 1.1) engine with a new medical ontology, SNOMED CT, and the ICD10 dictionary. IRIS 1.1 integrates public data sources and applies query expansion with exact and partial matches to find relevant teaching files. Using a set of 28 representative queries from multiple sources, the search engine finds more relevant teaching cases versus other publicly available search engines.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116490965","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
Suicide Risk on Twitter 推特上的自杀风险
Int. J. Knowl. Discov. Bioinform. Pub Date : 2018-07-01 DOI: 10.4018/IJKDB.2018070101
S. Fodeh, E. Boudreaux, Rixin Wang, Dennis Silva, R. Bossarte, J. Goulet, C. Brandt, Al-Talib Hamada
{"title":"Suicide Risk on Twitter","authors":"S. Fodeh, E. Boudreaux, Rixin Wang, Dennis Silva, R. Bossarte, J. Goulet, C. Brandt, Al-Talib Hamada","doi":"10.4018/IJKDB.2018070101","DOIUrl":"https://doi.org/10.4018/IJKDB.2018070101","url":null,"abstract":"While many studies have explored the use of social media and behavioral changes of individuals, few examined the utility of using social media for suicide detection and prevention. The study by Jashinsky et al. identified specific language patterns associated with a set of twelve suicide risk factors. The authors extended these methods to assess the significance of the language used on Twitter for suicide detection. This article quantifies the use of Twitter to express suicide related language, and its potential to detect users at high risk of suicide. The authors searched Twitter for tweets indicative of 12 suicide risk factors. This paper divided Twitter users into two groups: “high risk” and “at risk” based on two of the risk factors (“self-harm” and “prior suicide attempts”) and examined language patterns by computing co-occurrences of terms in tweets which helped identify relationships between suicide risk factors in both groups.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131436720","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: 3
Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology 熟练规范化模糊k均值初始质心方法
Int. J. Knowl. Discov. Bioinform. Pub Date : 2018-01-01 DOI: 10.4018/IJKDB.2018010104
Deepali Virmani, N. Jain, Ketan Parikh, Shefali Upadhyaya, A. Srivastav
{"title":"Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology","authors":"Deepali Virmani, N. Jain, Ketan Parikh, Shefali Upadhyaya, A. Srivastav","doi":"10.4018/IJKDB.2018010104","DOIUrl":"https://doi.org/10.4018/IJKDB.2018010104","url":null,"abstract":"This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692079","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
Sentiment Based Information Diffusion in Online Social Networks 基于情感的在线社交网络信息扩散
Int. J. Knowl. Discov. Bioinform. Pub Date : 2018-01-01 DOI: 10.4018/IJKDB.2018010105
Mohammad Ahsan, Madhu Kumari, Tajinder Singh, Triveni Lal Pal
{"title":"Sentiment Based Information Diffusion in Online Social Networks","authors":"Mohammad Ahsan, Madhu Kumari, Tajinder Singh, Triveni Lal Pal","doi":"10.4018/IJKDB.2018010105","DOIUrl":"https://doi.org/10.4018/IJKDB.2018010105","url":null,"abstract":"This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132655866","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
Alzheimer's and Parkinson's Disease Novel Therapeutic Target: The Mitochondrial Pyruvate Carrier - Ligand Docking to Screen Natural Compounds Related to Classic Inhibitors 阿尔茨海默病和帕金森病新的治疗靶点:线粒体丙酮酸载体-配体对接筛选与经典抑制剂相关的天然化合物
Int. J. Knowl. Discov. Bioinform. Pub Date : 2017-07-01 DOI: 10.4018/IJKDB.2017070104
Allen K. Bourdon, G. Villareal, G. Perry, C. Phelix
{"title":"Alzheimer's and Parkinson's Disease Novel Therapeutic Target: The Mitochondrial Pyruvate Carrier - Ligand Docking to Screen Natural Compounds Related to Classic Inhibitors","authors":"Allen K. Bourdon, G. Villareal, G. Perry, C. Phelix","doi":"10.4018/IJKDB.2017070104","DOIUrl":"https://doi.org/10.4018/IJKDB.2017070104","url":null,"abstract":"","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121494537","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
Mitochondrial Pyruvate Carrier 1 and 2 Heterodimer, In Silico, Models of Plant and Human Complexes: A Comparison of Structure and Transporter Binding Properties 线粒体丙酮酸载体1和2异源二聚体，植物和人类复合物模型:结构和转运体结合特性的比较
Int. J. Knowl. Discov. Bioinform. Pub Date : 2017-07-01 DOI: 10.4018/IJKDB.2017070102
Jason L. Dugan, Allen K. Bourdon, C. Phelix
{"title":"Mitochondrial Pyruvate Carrier 1 and 2 Heterodimer, In Silico, Models of Plant and Human Complexes: A Comparison of Structure and Transporter Binding Properties","authors":"Jason L. Dugan, Allen K. Bourdon, C. Phelix","doi":"10.4018/IJKDB.2017070102","DOIUrl":"https://doi.org/10.4018/IJKDB.2017070102","url":null,"abstract":"Theplantandhumanmitochondrialpyruvatecarrier(MPC)hadbeenstudiedinthe1970s-1990s providingmanypredictionsonfunctionalproteinstructureandmechanismsofsubstratebinding.Genes forhumanandplantMPChavebeenidentified,butnocrystalstructurehasyetbeenregisteredor depositedinaproteindatabank.Thisreportdescribesresultsforcomparisonsofstructureforhuman andplantMPC1/2heterodimerhomologymodels.Keycysteineresiduesareidentifiedforpyruvate andblockerbindingandformationofthiohemiacetalorMichaeladditionbonds.Evidenceisprovided foranalternatingaccessmodelinhuman,mouseear-cress,castorandcommonbeans,andcorn. KeywoRDS Arabidopsis Thaliana, Homo Sapien, In Silico, Phaseolus Vulgaris, Protein Homology, Protein Protein Docking, Pyruvate Docking, Ricinus Communis, Zea Mays","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930601","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
A Web Database IR-PDB for Sequence Repeats of Proteins in the Protein Data Bank 蛋白质数据库中蛋白质序列重复序列的Web数据库IR-PDB
Int. J. Knowl. Discov. Bioinform. Pub Date : 2017-07-01 DOI: 10.4018/IJKDB.2017070101
S. Selvaraj, Mary Rajathei
{"title":"A Web Database IR-PDB for Sequence Repeats of Proteins in the Protein Data Bank","authors":"S. Selvaraj, Mary Rajathei","doi":"10.4018/IJKDB.2017070101","DOIUrl":"https://doi.org/10.4018/IJKDB.2017070101","url":null,"abstract":"Amino acid repeats play significant roles in the evolution of structure and function of many large proteins. Analysis of internal repeats of protein with known structure helps to understand the importance of repeats of the protein. A database IR-PDB for repeats in sequence of the proteins in the PDB has been developed for the analysis of impact of repeats in proteins. Using the state of the art repeat detection method RADAR, internal repeats in 148202 sequences out of 285714 sequences belonging to 115031 PDB structures were detected. The identified sequence repeats were annotated with secondary structural information with a view to analyze the structural consequence and conservation of the repeats. The tertiary structure of the repeats and their functional involvements can be found out through web links to PDB, PDBsum and Pfam. IR-PDB is systematically annotated for the proteins in the PDB with sequence repeats and their structure with the possibility to access the dataset interactively through web services.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125580774","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
Genome Subsequences Assembly Using Approximate Matching Techniques in Hadoop 基于近似匹配技术的Hadoop基因组子序列组装
Int. J. Knowl. Discov. Bioinform. Pub Date : 2017-07-01 DOI: 10.4018/IJKDB.2017070105
G. Raja, U. S. Reddy
{"title":"Genome Subsequences Assembly Using Approximate Matching Techniques in Hadoop","authors":"G. Raja, U. S. Reddy","doi":"10.4018/IJKDB.2017070105","DOIUrl":"https://doi.org/10.4018/IJKDB.2017070105","url":null,"abstract":"","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127846819","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: 1
Predictive Toxicity of Conventional Triazole Pesticides by Simulating Inhibitory Effect on Human Aromatase CYP19 Enzyme 模拟对人芳香化酶CYP19的抑制作用预测三唑类农药的毒性
Int. J. Knowl. Discov. Bioinform. Pub Date : 2016-07-01 DOI: 10.4018/IJKDB.2016070104
T. Chachibaia, J. Hoskeri
{"title":"Predictive Toxicity of Conventional Triazole Pesticides by Simulating Inhibitory Effect on Human Aromatase CYP19 Enzyme","authors":"T. Chachibaia, J. Hoskeri","doi":"10.4018/IJKDB.2016070104","DOIUrl":"https://doi.org/10.4018/IJKDB.2016070104","url":null,"abstract":"15 common fungicides were evaluated to study their inhibitory effects on the human aromatase enzyme in comparison with the Letrozole LTZ, the most potent inhibitor of aromatase AI used as anti-estrogen for breast cancer treatment using AUTODOCK software for calculation of inhibition energy on CYP19A1 aromatase enzyme. Those compounds with minimal binding energy are safer in terms of toxicity and resistance of other prescription drugs like non-steroid AIs. In the authors' study, they found that four triazole fungicides compounds, Triticonazole, Tebuconazole, Metconazole and Fluquinconazole, exhibited minimal inhibition constant IC.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143625","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: 1
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