{"title":"Relation between kinetic conversion rates and ANM mode frequencies","authors":"Beytullah Özgur, Attila Gürsoy, O. Keskin","doi":"10.1109/HIBIT.2010.5478902","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478902","url":null,"abstract":"Elastic Network Models (ENMs) are informative, fast and largely used techniques for the elucidation of the intrinsic motions of the proteins. ENM normal modes resemble the conformational changes in the ligand-free states of the proteins. According to pre-existing equilibrium model the native state of the protein forms an ensemble of substates and ligand simply selects and shifts the population dynamics of the ensemble. In this study, we investigated the relation between normal mode frequencies and kinetic conversion rates of ensemble substates between each other.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513814","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":"Determination of the correspondence between mobility (rigidity) and conservation of the interface residues","authors":"Gozde Kar, A. Gursoy, O. Keskin","doi":"10.1109/HIBIT.2010.5478887","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478887","url":null,"abstract":"Hot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133640285","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":"Supervising Event Executive","authors":"Mehmet S Ünlütürk, C. Atay","doi":"10.1109/HIBIT.2010.5478908","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478908","url":null,"abstract":"There are many advances in nurse-call devices but there is a lack of proper integration and interoperation among them. This paper presents a software application to join these devices together in a LAN environment such as Hospital Information System. This software application is called Supervising Event Executive (SEE) that is developed to provide a common, convenient, and reliable means of transferring system events (and commands). The SEE primarily supports four types of applications. First type is called event publisher that generates LAN events from devices. Second type is called event subscriber that consumes LAN events from devices. A subscriber application typically takes some action based on an event. Third type is called resource provider that provides resources or services to consumer applications. The resource provider is typically managed through commands in a request / response nature. In addition, the resource provider may also publish events regarding the status of managed resources. The final type is called resource consumer that consumes resources or services managed by `Resource Provider' applications.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132152707","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":"Physico-chemical properties of DNA in phylogeny construction","authors":"Y. Bakiş, O. U. Sezerman, H. Otu","doi":"10.1109/HIBIT.2010.5478912","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478912","url":null,"abstract":"Phylogenic analysis relies on alignment of related sequences from different species to obtain the distances between these species. The quality of the alignment and the distance measure would depend on the alignment parameters that are used. In this work, we propose to use Relative Complexity Measure (RCM) to find the distances between the sequences which is not a parameter dependent measure. We used DNA sequences from Candida species and phylogenetic trees were obtained using un-weighted pair-group with arithmetic mean method. We used three reduced alphabets for the DNA sequences which were clustered by taking into account different physicochemical properties of DNA. RCM gives as good results as the distance determination method and among the physicochemical properties, Keto/Amino grouping is found to give the most accurate tree which is topologically closest to the desired phylogeny.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129363459","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":"The clinical guidelines usage towards the diagnosis and treatment of H1N1","authors":"Serdar Koç, G. Yilmaz, Y. Kabak","doi":"10.1109/HIBIT.2010.5478903","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478903","url":null,"abstract":"In medical clinics, guidelines are the best ways for practitioners to make good decisions on patient's medical problems. There are different kinds of symptoms at all kinds of illnesses and this will affect the medicians' decision on the diagnosis/treatment. These guidelines are the results of an algorithm to follow and the ways to defeat the illness. These guidelines are developed in textual format. In order to process them in computer systems, a machine procesable format, called GLIF, is developed and this format is widely used througout the world. In this way, the clinical guidelines can be encoded in GLIF format and they can be used in hospital information systems. Currently, H1N1 pandemic flu (i.e. Swine flu) is a global threat. Only in Turkey, there are about 350 casualties as annouced by the Ministry of Health. Furthermore, Ministry of Health published a guideline towards the diagnosis and initial treatment of H1N1. However, it is in textual format; in other words it is not machine processable. In this paper, we have developed a system, called HINlDiagnose, to be used in the diagnosis of the H1N1 flu. In this respect, we first developed the GLIF format of the textual guideline and then implemented a guideline execution engine that process the guideline. In other words, this is an expert system that helps the practitioners for the analysis of patient data and make the diognosis. Indeed, the system is not specific to a single H1N1 guideline. It is generic in that it can process any guideline in GLIF format.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122038724","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":"Inhibitor peptide design for NF-кB: Markov model & genetic algorithm","authors":"E. B. Unal, A. Gursoy, B. Erman","doi":"10.1109/HIBIT.2010.5478904","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478904","url":null,"abstract":"Two peptide design approaches are proposed to block activities of disease related proteins. First approach employs a probabilistic method; the problem is set as Markov chain. The possible binding site of target protein and a path on this binding site are determined. 20 natural amino acids and 400 dipeptides are docked to the selected path using the AutoDock software. The statistical weight matrices for the binding energies are derived from AutoDock results; matrices are used to determine top 100 peptide sequences with affinity to target protein. Second approach utilizes a heuristic method for peptide sequence determination; genetic algorithm (GA) with tournament selection. The amino acids are the genes; the peptide sequences are the chromosomes of GA. Initial random population of 100 chromosomes leads to determination of 100 possible binding peptides, after 8–10 generations of GA. Thermodynamic properties of the peptides are analyzed by a method that we proposed previously. NF-кB protein is selected as case-study.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623660","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":"Microarray data analysis for cancer classification","authors":"A. Osareh, B. Shadgar","doi":"10.1109/HIBIT.2010.5478893","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478893","url":null,"abstract":"Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray data. In this work, we aim to develop an automated system for robust and reliable cancer diagnoses based on gene microarray data. Support vector machine classifiers outperform other popular classifiers, such as K nearest neighbours, naive Bayes, neural networks and decision tree, often to a remarkable degree. We choose a set of 9 publicly available benchmark microarray datasets that encompass both binary and multi-class cancer problems. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for use in gene-based cancer classification. In particular, amongst various systematic experiments carried out, best classification model is achieved using a subset of features chosen via information gain feature ranking for support vector machine classifier.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126308899","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":"Machine learning techniques to diagnose breast cancer","authors":"A. Osareh, B. Shadgar","doi":"10.1109/HIBIT.2010.5478895","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478895","url":null,"abstract":"Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest neighbours and probabilistic neural networks classifiers are combined with signal-to-noise ratio feature ranking, sequential forward selection-based feature selection and principal component analysis feature extraction to distinguish between the benign and malignant tumours of breast. The best overall accuracy for breast cancer diagnosis is achieved equal to 98.80% and 96.33% respectively using support vector machines classifier models against two widely used breast cancer benchmark datasets.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125275106","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":"Toward effective medical search engines","authors":"M. A. Zamil, Aysu Betin Can","doi":"10.1109/HIBIT.2010.5478911","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478911","url":null,"abstract":"In this paper, we present a domain specific search engine that relies on extracting the semantic relation among medical documents. Our goal is to maximize the contextual retrieval and ranking performance with minimum input from users. We have performed experiments to measure the effectiveness of the proposed technique by evaluating the performance of the retrieval process in terms of recall, precision and topical ranking. The results indicated that the proposed medical search engine achieved higher average precision in compare with highest scored runs submitted to TREC-9.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133331649","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":"Coevolution based prediction of protein-protein interactions with reduced training data","authors":"Bahar Pamuk, Tolga Can","doi":"10.1109/HIBIT.2010.5478884","DOIUrl":"https://doi.org/10.1109/HIBIT.2010.5478884","url":null,"abstract":"Protein-protein interactions are important for the prediction of protein functions since two interacting proteins usually have similar functions in a cell. In this work, our aim is to predict protein-protein interactions with a known portion of the interaction network when there are large numbers of protein interactions in the data set. Phylogenetic profiles of proteins form the feature vectors for training Support Vector Machine (SVM). To reduce the training time of SVM we reduced the data size by k-means and MEB clustering techniques and we applied feature selection methods by selecting most representative features by phylogenetic tree and Fisher's Exact Test methods. The training data clustered by the k-means method gave superior results in prediction accuracies.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129920202","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}