International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing最新文献

{"title":"Mapping Transcription Factors from a Model to a Non-model Organism","authors":"Rachita Sharma, Patricia A. Evans, V. Bhavsar","doi":"10.1109/IJCBS.2009.124","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.124","url":null,"abstract":"Identification of regulatory elements, such as transcription factors, is useful in construction of regulatory networks and to understand gene regulation. These transcription factors have already been recognized for model organisms based on extensive experiments but have not been as heavily investigated for non-model organisms. This paper proposes to use Basic Local Alignment Search Tool (BLAST) to map the transcription factors from a model to a non-model organism. Experiments are performed on bacterial organisms based on evolutionary distance to compare the results. Analysis of the results suggest that transcription factors can be mapped from one bacterial organism to another as transcription factor motifs are well preserved among these organisms. Results are also analyzed to determine the best suitable threshold for the e-value parameter of BLAST that can be used to map transcription factors, determine to be the e-value thresholds of 0.01 and 0.1. Both the BLAST e-value threshold and evolutionary distance from the model organism used for mapping have significant impact on the quality of results.","PeriodicalId":133764,"journal":{"name":"International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121019937","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":"Kalman Filtering Design Based on Real-Time Updating of Noise Matrix","authors":"Wei Jiang, Xia Zaho, Qijun Chen","doi":"10.1109/IJCBS.2009.80","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.80","url":null,"abstract":"At present, Kalman filtering is widely used in dynamic positioning. With continuous development of deep-sea exploration, the accuracy of Kalman filtering is affected by more complex oceanic condition. As erratic performance of Kalman filtering with fixed system noise matrix and measurement noise matrix in complex oceanic condition, a new filtering method called Kalman filtering based on real-time updating of noise matrix is put forward. The algorithm obtains real-time calculation results of the system noise and measurement noise which are devoted to revising variance of noise array. Compared with Kalman filtering, it is more accurate and more efficacious in improving the performance of dynamic positioning in complex oceanic condition proved by computer simulation.","PeriodicalId":133764,"journal":{"name":"International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing","volume":"89 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":"124549922","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":"Concordance of Diagnosis Based on Zangfu-Organs Syndrome Differentiation by Clinicians of Traditional Chinese Medicine","authors":"Guoping Liu, Yiqin Wang, Nai-Qing Zhao, Zhaoxia Xu, Fufeng Li, Ying Dong, P. Qian","doi":"10.1109/IJCBS.2009.26","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.26","url":null,"abstract":"Objective: We aimed to assess concordance of Zangfu-organs Syndrome Differentiation by evaluating the concordance of successive diagnosis by the same clinician of traditional Chinese medicine (TCM) and that of diagnosis by different clinicians of TCM. Methods: Twenty-five cases were randomly selected and 2 copies were made from the numerical data collected through Inquiry Scale for Diagnosis of Heart System Syndromes made by our research team. The copies were disorganized and renumbered form 1 to 50. In order to evaluate the concordance of successive diagnosis by the same clinician, 2 TCM experts were invited to differentiate the syndrome and diagnose for the 50 cases by double-blind method. Fifteen cases were randomly selected. Three TCM clinicians were invited to make a diagnosis basing on Zangfu-organs syndrome differentiation, in order to evaluate the concordance of diagnosis by different clinicians. Kappa test was adopted. Results: For the same case scale, the results of successive diagnosis by the same clinician showed that, Kap concordance coefficient for syndrome of deficiency of heart qi was 0.69, that for syndrome of deficiency of heart yang was 0.80, that for syndrome of deficiency of heart yin was 0.28, that for syndrome of deficiency of heart blood was 0.33, that for syndrome of turbid phlegm was 0.78, that for syndrome of blood stasis was 0.33, that for syndrome of qi stagnation was 0.54, that for syndrome of hyperactivity of heart fire was 0.52, and that for syndrome of cold coagulation was 0.72. The results of diagnosis by different clinicians showed that, Kap concordance coefficient for syndrome of deficiency of heart qi was 0.63, that for syndrome of deficiency of heart yang was 0.72, that for syndrome of deficiency of heart yin was 0.48, that for syndrome of turbid phlegm was 0.03, that for syndrome of blood stasis was 0.14, and that for syndrome of qi stagnation was 0.17. Conclusion: Diagnosis by the same clinician and different clinicians varied, indicating a poor concordance of diagnosis basing on Zangfu-organs syndrome differentiation.","PeriodicalId":133764,"journal":{"name":"International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing","volume":"9 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":"121285603","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":"A Collaborative Car Auto-Navigation Framework Based on Intelligent Trajectory Mining","authors":"Tong Ruan, Zhanquan Wang, T. Song","doi":"10.1109/IJCBS.2009.28","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.28","url":null,"abstract":"Vehicle trajectories are widely used in varies of applications. As to car navigation applications, typical usages of trajectories include correcting the digital maps, finding out the traffic jams, looking for the locations of cars. In this paper, it is assumed that trajectory data of expert drivers implies “best practice” routes, which will be helpful to naïve drivers during driving. Therefore we propose a new idea called “collaborative navigation” in which the best practice routes are mined from trajectories and are send to naïve drivers when needed. The major difficulties of this approach are that the trajectory data may be too large and the data processing time may be too long. To address the problem, we design a framework which covers the whole life cycle of trajectories processing, including original data collection, “point to route” conversion, “best route” mining, and route query. Corresponding algorithms, data structures and data indexes are devised for each step in the life cycle. Experiments show “collaborative navigation” can be used to enhance routing selection with small footprint and quick response time using our framework.","PeriodicalId":133764,"journal":{"name":"International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing","volume":"5 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":"122180133","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}