Pub Date : 2018-01-01DOI: 10.1109/BIBM.2018.8621259
S. Pernice, M. Beccuti, P. Do', M. Pennisi, F. Pappalardo
{"title":"Estimating Daclizumab effects in Multiple Sclerosis using Stochastic Symmetric Nets","authors":"S. Pernice, M. Beccuti, P. Do', M. Pennisi, F. Pappalardo","doi":"10.1109/BIBM.2018.8621259","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621259","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"1 1","pages":"1393-1400"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78628269","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 randomization and trial supply management system for adaptive clinical studies of TCM and its scientific research application in recurrent tuberculosis","authors":"Tiancai Wen, Baoyan Liu, Liyun He, Xiaoying Lv, Xin Wang, Yanning Zhang","doi":"10.1109/BIBM.2018.8621116","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621116","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"10 1","pages":"1917-1921"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78264795","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 : 2017-01-01DOI: 10.1109/BIBM.2017.8217864
Xiaofang Zhou, Xue Li, Yangyang Hu, Wenqiang Zhang, Fufeng Li
{"title":"Lip analysis in traditional Chinese medicine","authors":"Xiaofang Zhou, Xue Li, Yangyang Hu, Wenqiang Zhang, Fufeng Li","doi":"10.1109/BIBM.2017.8217864","DOIUrl":"https://doi.org/10.1109/BIBM.2017.8217864","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"17 1","pages":"1381-1387"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77656098","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 : 2017-01-01DOI: 10.1109/BIBM.2017.8217865
Xue Li, Weifei Zhang, Yangyang Hu, Wenqiang Zhang, Fufeng Li
{"title":"Tongue diagnosis management for mobile health in the wild","authors":"Xue Li, Weifei Zhang, Yangyang Hu, Wenqiang Zhang, Fufeng Li","doi":"10.1109/BIBM.2017.8217865","DOIUrl":"https://doi.org/10.1109/BIBM.2017.8217865","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"42 1","pages":"1388-1395"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77208026","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822484
Xiangdong Wang
Tomorrow's genome medicine in lung cancer should focus more on the homogeneity and heterogeneity of lung cancer which play an important role in the development of drug resistance, genetic complexity, as well as confusion and difficulty of early diagnosis and therapy. Chromosome positioning and repositioning may contribute to the sensitivity of lung cancer cells to therapy, the heterogeneity associated with drug resistance, and the mechanism of lung carcinogenesis. The CCCTC-binding factor plays critical roles in genome topology and function, increased risk of carcinogenicity, and potential of lung cancer-specific mediations. Chromosome reposition in lung cancer can be regulated by CCCTC binding factor. Single-cell gene sequencing, as part of genome medicine, was paid special attention in lung cancer to understand mechanical phenotypes, single-cell biology, heterogeneity, and chromosome positioning and function of single lung cancer cells. We at first propose to develop an intelligent single-cell robot of human cells to integrate together systems information of molecules, genes, proteins, organelles, membranes, architectures, signals, and functions. It can be a powerful automatic system to assist clinicians in the decision-making, molecular understanding, risk analyzing, and prognosis predicting.
{"title":"Clinical application of precision medicine: Zhongshan Hospital strategy","authors":"Xiangdong Wang","doi":"10.1109/BIBM.2016.7822484","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822484","url":null,"abstract":"Tomorrow's genome medicine in lung cancer should focus more on the homogeneity and heterogeneity of lung cancer which play an important role in the development of drug resistance, genetic complexity, as well as confusion and difficulty of early diagnosis and therapy. Chromosome positioning and repositioning may contribute to the sensitivity of lung cancer cells to therapy, the heterogeneity associated with drug resistance, and the mechanism of lung carcinogenesis. The CCCTC-binding factor plays critical roles in genome topology and function, increased risk of carcinogenicity, and potential of lung cancer-specific mediations. Chromosome reposition in lung cancer can be regulated by CCCTC binding factor. Single-cell gene sequencing, as part of genome medicine, was paid special attention in lung cancer to understand mechanical phenotypes, single-cell biology, heterogeneity, and chromosome positioning and function of single lung cancer cells. We at first propose to develop an intelligent single-cell robot of human cells to integrate together systems information of molecules, genes, proteins, organelles, membranes, architectures, signals, and functions. It can be a powerful automatic system to assist clinicians in the decision-making, molecular understanding, risk analyzing, and prognosis predicting.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"36 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86512562","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822477
A. Zhang
Biological networks are dynamic and modular. Identifying dynamic functional modules is key to elucidating biological insight and disease mechanism. In recent years, while most researchers have focused on detecting functional modules from static protein-protein interaction (PPI) networks where the networks are treated as static graphs derived from aggregated data across all available experiments or from a single snapshot at a particular time, temporal nature of context-specific transcriptomic and proteomic data has been recognized by researchers. Meanwhile, the analysis of dynamic networks has been a hot topic in data mining and social networks. Dynamic networks are structures with objects and links between the objects that vary in time. Temporary information in dynamic networks can be used to reveal many important phenomena such as bursts of activities in social networks and evolution of functional modules in protein interaction networks. In this talk, I will address several critical challenges to construct robust, dynamic gene interaction networks, and present our computational approaches to identify disease-relevant functional modules and to track the progression patterns of modules in dynamic biological networks. Significant modules which are correlated to phenotypes of interest can be identified, for example, those functional modules which form and progress across different stages of a cancer. Through identifying these functional modules in the progression process, we are able to detect the critical groups of proteins that are responsible for the transition of different cancer stages. Our approaches can also discover how the strength of each detected modules changes over the entire observation period. I will also demonstrate the application of our approach in a variety of biomedical applications.
{"title":"Trajectory analysis - Linking genomic and proteomic data with disease progression","authors":"A. Zhang","doi":"10.1109/BIBM.2016.7822477","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822477","url":null,"abstract":"Biological networks are dynamic and modular. Identifying dynamic functional modules is key to elucidating biological insight and disease mechanism. In recent years, while most researchers have focused on detecting functional modules from static protein-protein interaction (PPI) networks where the networks are treated as static graphs derived from aggregated data across all available experiments or from a single snapshot at a particular time, temporal nature of context-specific transcriptomic and proteomic data has been recognized by researchers. Meanwhile, the analysis of dynamic networks has been a hot topic in data mining and social networks. Dynamic networks are structures with objects and links between the objects that vary in time. Temporary information in dynamic networks can be used to reveal many important phenomena such as bursts of activities in social networks and evolution of functional modules in protein interaction networks. In this talk, I will address several critical challenges to construct robust, dynamic gene interaction networks, and present our computational approaches to identify disease-relevant functional modules and to track the progression patterns of modules in dynamic biological networks. Significant modules which are correlated to phenotypes of interest can be identified, for example, those functional modules which form and progress across different stages of a cancer. Through identifying these functional modules in the progression process, we are able to detect the critical groups of proteins that are responsible for the transition of different cancer stages. Our approaches can also discover how the strength of each detected modules changes over the entire observation period. I will also demonstrate the application of our approach in a variety of biomedical applications.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"90 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85949537","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822482
C. Shyu
Medical research and clinical trials are often based on hypotheses that were observed from clinical practice with noticeable evidence. Forming clinically significant hypotheses will greatly benefit the success of clinical research and ensure both external and internal validity of the trial. In this talk, I will introduce a knowledge discovery approach to automatically identify populations of subjects with commonly occurred comorbidities, genotypes, and phenotypes that present statistically high contract between populations. To focus on a confined set of medical problems as most of medical researchers would like to target (hypertension and diabetes versus all chronic diseases), this approach is able to take a set of selected attributes of interest and expand knowledge discoveries from the initial set. The computational approach consists of a forward floating search method for population selection, a hierarchical frequent pattern mining tree to efficiently handle dense associations, contrast mining for identifying actionable plans, and accumulated contrast (ac-)index for ranking mining results for biomedical researchers. I will present exploratory analysis process and results from the Simon's Simplex Collection (SSC) by the Simons Foundation Autism Research Initiative (SFARI) which comprises data representing 11,560 individuals from 2,591 families. Putative autism subtypes were explored by partitioning families based on demographics and autism phenotypes. An extended contrast mining procedure identified genetic combinations showing preferential association for one of the contrasted subgroups, emphasizing combinations novel to the autistic proband within each family tree. Potentials for other biomedical applications will also be discussed.
{"title":"Semi-hypothesis guided exploratory analysis for biomedical applications","authors":"C. Shyu","doi":"10.1109/BIBM.2016.7822482","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822482","url":null,"abstract":"Medical research and clinical trials are often based on hypotheses that were observed from clinical practice with noticeable evidence. Forming clinically significant hypotheses will greatly benefit the success of clinical research and ensure both external and internal validity of the trial. In this talk, I will introduce a knowledge discovery approach to automatically identify populations of subjects with commonly occurred comorbidities, genotypes, and phenotypes that present statistically high contract between populations. To focus on a confined set of medical problems as most of medical researchers would like to target (hypertension and diabetes versus all chronic diseases), this approach is able to take a set of selected attributes of interest and expand knowledge discoveries from the initial set. The computational approach consists of a forward floating search method for population selection, a hierarchical frequent pattern mining tree to efficiently handle dense associations, contrast mining for identifying actionable plans, and accumulated contrast (ac-)index for ranking mining results for biomedical researchers. I will present exploratory analysis process and results from the Simon's Simplex Collection (SSC) by the Simons Foundation Autism Research Initiative (SFARI) which comprises data representing 11,560 individuals from 2,591 families. Putative autism subtypes were explored by partitioning families based on demographics and autism phenotypes. An extended contrast mining procedure identified genetic combinations showing preferential association for one of the contrasted subgroups, emphasizing combinations novel to the autistic proband within each family tree. Potentials for other biomedical applications will also be discussed.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"72 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80992644","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822483
Kai Tan
Determining the 3-dimensional structure of the genome and its impact on gene expression has been a long-standing question in cell biology. Recent development in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data, revealing a hierarchical organization of the 3D genome, from large compartments spanning multiple chromosomes, to mega-base-sized topological associated chromatin domains, to individual long-range chromatin loops mediating enhancer-promoter interactions. With the explosion of chromatin interaction data, there is a pressing need for analytical tools. In this talk, I will describe two computational algorithms for analyzing chromatin interaction data at different scales. I will first present a fast algorithm for identifying large-scale, hierarchical chromatin domains. I will demonstrate how the algorithm enables studies of chromatin subdomains in gene regulation. Accurate knowledge of enhancer-promoter interactions is a pre-requisite to understanding regulatory output of enhancers. I will present an algorithm for predicting enhancer-promoter interactions by integrating genomic, transcriptomic, and epigenomic data. Using data from multiple human cell types, I will demonstrate how the algorithm can help decipher the mechanisms underlying enhancer-promoter communication.
{"title":"Computational tools for studying gene regulation in the 3-dimensional genome","authors":"Kai Tan","doi":"10.1109/BIBM.2016.7822483","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822483","url":null,"abstract":"Determining the 3-dimensional structure of the genome and its impact on gene expression has been a long-standing question in cell biology. Recent development in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data, revealing a hierarchical organization of the 3D genome, from large compartments spanning multiple chromosomes, to mega-base-sized topological associated chromatin domains, to individual long-range chromatin loops mediating enhancer-promoter interactions. With the explosion of chromatin interaction data, there is a pressing need for analytical tools. In this talk, I will describe two computational algorithms for analyzing chromatin interaction data at different scales. I will first present a fast algorithm for identifying large-scale, hierarchical chromatin domains. I will demonstrate how the algorithm enables studies of chromatin subdomains in gene regulation. Accurate knowledge of enhancer-promoter interactions is a pre-requisite to understanding regulatory output of enhancers. I will present an algorithm for predicting enhancer-promoter interactions by integrating genomic, transcriptomic, and epigenomic data. Using data from multiple human cell types, I will demonstrate how the algorithm can help decipher the mechanisms underlying enhancer-promoter communication.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"269 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75924758","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822475
Yixue Li
Whole genome sequencing of disease animal models together with population genetics methodology is a powerful technology for deciphering new variations which associated with disease phenotypes. Here we show our studies on camel, dog, and rabbit. Whole genome sequencing data were generated from those animals, and then population genetics methodology was used in dealing with these whole genome sequencing date. Some important genetic variations were discovered which shown a strong association with disease related phenotypes.
{"title":"Whole genome sequencing of disease animal models","authors":"Yixue Li","doi":"10.1109/BIBM.2016.7822475","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822475","url":null,"abstract":"Whole genome sequencing of disease animal models together with population genetics methodology is a powerful technology for deciphering new variations which associated with disease phenotypes. Here we show our studies on camel, dog, and rabbit. Whole genome sequencing data were generated from those animals, and then population genetics methodology was used in dealing with these whole genome sequencing date. Some important genetic variations were discovered which shown a strong association with disease related phenotypes.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"1 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78531181","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 : 2016-12-01DOI: 10.1109/BIBM.2016.7822476
D. Rand
I will discuss a new theoretical approach to information and decisions in signalling systems and relate this to new experimental results about the NF-kappaB signalling system. NF-kappaB is an exemplar system that controls inflammation and in different contexts has varying effects on cell death and cell division. It is activated by various stress stimuli, including inflammatory cytokines such as TNFalpha and IL-1beta and is regarded as one of the most important stress response pathways in the mammalian cell. In a variety of conditions it displays oscillatory dynamics when stimulated, with the transcription factor entering the nucleus in a pulsatile fashion with a period of roughly 100 minutes. It is commonly claimed that it is information processing hub, taking in signals about the infection and stress status of the tissue environment and as a consequence of the oscillations, transmitting higher amounts of information to the hundreds of genes it controls. My aim is to develop a conceptual and mathematical framework to enable a rigorous quantifiable discussion of information in this context in order to follow Francis Crick's counsel that it is better in biology to follow the flow of information than those of matter or energy. In my approach the value of the information in the signalling system is defined by how well it can be used to make the “correct decisions” when those “decisions” are made by molecular networks. As part of this I will introduce a new mathematical method for the analysis and simulation of large stochastic non-linear oscillating systems. This allows an analytic analysis of the stochastic relationship between input and response and shows that for tightly-coupled systems like those based on current models for signalling systems, clocks, and the cell cycle this relationship is highly constrained and non-generic.
{"title":"Information and decision-making in dynamic cell signaling","authors":"D. Rand","doi":"10.1109/BIBM.2016.7822476","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822476","url":null,"abstract":"I will discuss a new theoretical approach to information and decisions in signalling systems and relate this to new experimental results about the NF-kappaB signalling system. NF-kappaB is an exemplar system that controls inflammation and in different contexts has varying effects on cell death and cell division. It is activated by various stress stimuli, including inflammatory cytokines such as TNFalpha and IL-1beta and is regarded as one of the most important stress response pathways in the mammalian cell. In a variety of conditions it displays oscillatory dynamics when stimulated, with the transcription factor entering the nucleus in a pulsatile fashion with a period of roughly 100 minutes. It is commonly claimed that it is information processing hub, taking in signals about the infection and stress status of the tissue environment and as a consequence of the oscillations, transmitting higher amounts of information to the hundreds of genes it controls. My aim is to develop a conceptual and mathematical framework to enable a rigorous quantifiable discussion of information in this context in order to follow Francis Crick's counsel that it is better in biology to follow the flow of information than those of matter or energy. In my approach the value of the information in the signalling system is defined by how well it can be used to make the “correct decisions” when those “decisions” are made by molecular networks. As part of this I will introduce a new mathematical method for the analysis and simulation of large stochastic non-linear oscillating systems. This allows an analytic analysis of the stochastic relationship between input and response and shows that for tightly-coupled systems like those based on current models for signalling systems, clocks, and the cell cycle this relationship is highly constrained and non-generic.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"86 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80907196","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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