Pub Date : 2015-11-09DOI: 10.1109/BIBM.2015.7359647
E. Xing
Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and Director of the CMU/UPMC Center for Machine Learning and Health. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. He servers (or served) as an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He was a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Award. He was the Program Chair of ICML 2014.
Eric Xing博士是卡内基梅隆大学计算机科学学院机器学习教授，也是CMU/UPMC机器学习与健康中心主任。他的主要研究兴趣是机器学习和统计方法的发展，以及大规模计算系统和架构;特别是在人工、生物和社会系统的高维、多模态和动态可能世界中解决涉及自动学习、推理和决策的问题。邢教授获得罗格斯大学分子生物学博士学位，以及加州大学伯克利分校计算机科学博士学位。他是《应用统计年鉴》(AOAS)、《美国统计协会杂志》(JASA)、《IEEE模式分析与机器智能学报》(PAMI)、《公共科学图书馆计算生物学杂志》(PLoS Journal of Computational Biology)的副主编，也是《机器学习杂志》(MLJ)、《机器学习研究杂志》(JMLR)的行动编辑。他是DARPA信息科学与技术(ISAT)咨询小组的成员，是NSF职业奖、斯隆奖学金、美国空军青年研究员奖和IBM开放合作研究奖的获得者。他是ICML 2014的项目主席。
{"title":"Toward personalized pan-omic association analysis under complex structures and big data","authors":"E. Xing","doi":"10.1109/BIBM.2015.7359647","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359647","url":null,"abstract":"Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and Director of the CMU/UPMC Center for Machine Learning and Health. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. He servers (or served) as an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He was a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Award. He was the Program Chair of ICML 2014.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"24 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80504055","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}
Pub Date : 2015-11-09DOI: 10.1109/BIBM.2015.7359646
S. Aluru
Reverse engineering whole-genome networks from large-scale gene expression measurements and analyzing them to extract biologically valid hypotheses are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. In this talk, I will present our research on the development of parallel mutual information and Bayesian network based structure learning methods to eliminate such bottlenecks and facilitate genome-scale network inference. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer. Such networks can be used as a guide to predicting gene function and extracting context-specific subnetworks.
{"title":"Parallel machine learning approaches for reverse engineering genome-scale networks","authors":"S. Aluru","doi":"10.1109/BIBM.2015.7359646","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359646","url":null,"abstract":"Reverse engineering whole-genome networks from large-scale gene expression measurements and analyzing them to extract biologically valid hypotheses are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. In this talk, I will present our research on the development of parallel mutual information and Bayesian network based structure learning methods to eliminate such bottlenecks and facilitate genome-scale network inference. As a demonstration, we reconstructed genome-scale networks of the model plant Arabidopsis thaliana from 11,700 microarray experiments using 1.57 million cores of the Tianhe-2 Supercomputer. Such networks can be used as a guide to predicting gene function and extracting context-specific subnetworks.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"47 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83255840","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}
Pub Date : 2015-01-01DOI: 10.1109/BIBM.2015.7359715
Hongxia Gao, Yinghao Luo, Kewei Chen, Ge Ma, Lixuan Wu
{"title":"An image reconstruction model and hybrid algorithm for limited-angle projection data","authors":"Hongxia Gao, Yinghao Luo, Kewei Chen, Ge Ma, Lixuan Wu","doi":"10.1109/BIBM.2015.7359715","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359715","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"9 1","pages":"405-408"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75515184","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}
Pub Date : 2014-01-01DOI: 10.1109/BIBM.2014.6999357
H. Ye, Shuai Mao, Juan Chen, Qin-juan Wu, Yunfei Wang
{"title":"Analysis of the correlation between the ambulatory arterial stiffness index, circadian rhythm and TCM syndrome differentiation in patients with essential hypertension","authors":"H. Ye, Shuai Mao, Juan Chen, Qin-juan Wu, Yunfei Wang","doi":"10.1109/BIBM.2014.6999357","DOIUrl":"https://doi.org/10.1109/BIBM.2014.6999357","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"54 1","pages":"193-196"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80918772","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}
Pub Date : 2013-12-01DOI: 10.1109/BIBM.2013.6732449
Ju Han Kim
Summary form only given. Biological networks often show a scale-free power-law distribution. Furthermore, leathal genes tend to form functional hubs whereas non-leathal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. Here we report two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation-sensitivity (PSN) network. Hubs of both networks demonstrate a low molecular evolutionary rate and a high codon adaptation index, indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes whereas PSN hubs with disease genes and drug targets. PPI network hubs are enriched with essential cellular processes whereas PSN hubs with environmental interactions like TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are reciprocal to each other but work in concert between death and disease.
{"title":"Yin and Yang of reciprocally scale-free biological networks between disease genes and death genes","authors":"Ju Han Kim","doi":"10.1109/BIBM.2013.6732449","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732449","url":null,"abstract":"Summary form only given. Biological networks often show a scale-free power-law distribution. Furthermore, leathal genes tend to form functional hubs whereas non-leathal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. Here we report two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation-sensitivity (PSN) network. Hubs of both networks demonstrate a low molecular evolutionary rate and a high codon adaptation index, indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes whereas PSN hubs with disease genes and drug targets. PPI network hubs are enriched with essential cellular processes whereas PSN hubs with environmental interactions like TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are reciprocal to each other but work in concert between death and disease.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"134 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86521893","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}
Pub Date : 2013-12-01DOI: 10.1109/BIBM.2013.6732450
Ying Xu
In this talk, I will present our recent work on how inflammation, change in the availability of oxygen, and hyaluronan play essential roles in key transitions in cancer development, including initiation and metastasis.
{"title":"Elucidation of drivers for cancer initiation and metastasis: A data-mining approach","authors":"Ying Xu","doi":"10.1109/BIBM.2013.6732450","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732450","url":null,"abstract":"In this talk, I will present our recent work on how inflammation, change in the availability of oxygen, and hyaluronan play essential roles in key transitions in cancer development, including initiation and metastasis.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"17 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88546192","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}
For the present hospital management information system which lacked of data availability situation, this study was designed and implemented comprehensive query system of patients. The system can provide various forms of statistical analysis of patients for hospital administrators. After the system implementing in the hospital, it can effectively improved the utilization of data, help managers make decisions and made hospital's information technology and management to higher level.
{"title":"The design and implementation of inpatient medical expenses analysis system","authors":"Wangbin, Xieqi, Shihuaxin, Caoxinyu, Wangwenjing, Chendi","doi":"10.1109/BIBM.2013.6732687","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732687","url":null,"abstract":"For the present hospital management information system which lacked of data availability situation, this study was designed and implemented comprehensive query system of patients. The system can provide various forms of statistical analysis of patients for hospital administrators. After the system implementing in the hospital, it can effectively improved the utilization of data, help managers make decisions and made hospital's information technology and management to higher level.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"29 1","pages":"166-169"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73201632","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}
Pub Date : 2013-12-01DOI: 10.1109/BIBM.2013.6732448
Keith C. C. Chan
Big Data refers to data sets that are so large and complex that traditional data processing tools and technologies cannot cope with. The process of examining such data to uncover hidden patterns in them is referred to as Big Data Analytics. Drug discovery is related to big data analytics as the process may require the collection, processing and analysis of extremely large volume of structured and unstructured biomedical data stemming from a wide range of experiments and surveys collected by hospitals, laboratories, pharmaceutical companies or even social media. These data may include sequencing and gene expression data, drug data including molecular data, protein and drug interaction data, clinical trial and electronic patient record data, patient behavior and self-reporting data in social media, regulatory monitoring data, and literatures where trends and drug repurposing and protein-protein interaction data may be found. To analyze such diversity of data types in large volumes for the purpose of drug discovery, we need algorithms that are simple, effective, efficient and scalable. In this talk, we discuss how we can take advantage of the recent development in big data analytics to improve the drug discovery process. We describe what have recently been done and what remain to be done to develop big data algorithms for drug discovery. We present the effort we have recently made to develop such algorithms to uncover hidden patterns in such data as unreported drug side-effect discussions in social media communications, patient record and sequencing data, regulatory monitoring and drug-protein interaction data, protein-chemical interactions data, etc., for drug side-effect prediction and how such predictions may be used to determine possible drug structures with different desirable properties. We discuss how big data analytics may contribute to better drug efficacy and safety for pharmaceutical companies and regulators.
{"title":"Big data analytics for drug discovery","authors":"Keith C. C. Chan","doi":"10.1109/BIBM.2013.6732448","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732448","url":null,"abstract":"Big Data refers to data sets that are so large and complex that traditional data processing tools and technologies cannot cope with. The process of examining such data to uncover hidden patterns in them is referred to as Big Data Analytics. Drug discovery is related to big data analytics as the process may require the collection, processing and analysis of extremely large volume of structured and unstructured biomedical data stemming from a wide range of experiments and surveys collected by hospitals, laboratories, pharmaceutical companies or even social media. These data may include sequencing and gene expression data, drug data including molecular data, protein and drug interaction data, clinical trial and electronic patient record data, patient behavior and self-reporting data in social media, regulatory monitoring data, and literatures where trends and drug repurposing and protein-protein interaction data may be found. To analyze such diversity of data types in large volumes for the purpose of drug discovery, we need algorithms that are simple, effective, efficient and scalable. In this talk, we discuss how we can take advantage of the recent development in big data analytics to improve the drug discovery process. We describe what have recently been done and what remain to be done to develop big data algorithms for drug discovery. We present the effort we have recently made to develop such algorithms to uncover hidden patterns in such data as unreported drug side-effect discussions in social media communications, patient record and sequencing data, regulatory monitoring and drug-protein interaction data, protein-chemical interactions data, etc., for drug side-effect prediction and how such predictions may be used to determine possible drug structures with different desirable properties. We discuss how big data analytics may contribute to better drug efficacy and safety for pharmaceutical companies and regulators.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"40 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74053917","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}
Pub Date : 2013-01-01DOI: 10.1109/BIBM.2013.6732671
K. Mi, Yinan Tang, Sili Tan
{"title":"Discussion on TCM pulse diagnosis technology evaluation standard","authors":"K. Mi, Yinan Tang, Sili Tan","doi":"10.1109/BIBM.2013.6732671","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732671","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"38 1","pages":"186-191"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74261952","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}
Pub Date : 2013-01-01DOI: 10.1109/BIBM.2013.6732685
Q. Tian, Rusong Guo, Kiulam Chung, Shan Wu
{"title":"Effect of Tuina therapy and Baduanjin exercise for primary fibromyalgia syndrome: A prospective, randomized study","authors":"Q. Tian, Rusong Guo, Kiulam Chung, Shan Wu","doi":"10.1109/BIBM.2013.6732685","DOIUrl":"https://doi.org/10.1109/BIBM.2013.6732685","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"23 1","pages":"251-253"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75175557","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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