Pub Date : 2016-12-01DOI: 10.1109/BIBM.2016.7822595
Shaoliang Peng, Xiaoyu Zhang, Yutong Lu, Xiangke Liao, Kai Lu, Canqun Yang, Jie Liu, Weiliang Zhu, Dongqing Wei
Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single MIC, single-node CPU/MIC collaborated parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration strategy above, we achieved the highest speedup of 25–33 times compared with the original program. Source Code: https://github.com/tianhe2/mAMBER
{"title":"mAMBER: A CPU/MIC collaborated parallel framework for AMBER on Tianhe-2 supercomputer","authors":"Shaoliang Peng, Xiaoyu Zhang, Yutong Lu, Xiangke Liao, Kai Lu, Canqun Yang, Jie Liu, Weiliang Zhu, Dongqing Wei","doi":"10.1109/BIBM.2016.7822595","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822595","url":null,"abstract":"Molecular dynamics (MD) is a computer simulation method of studying physical movements of atoms and molecules that provide detailed microscopic sampling on molecular scale. With the continuous efforts and improvements, MD simulation gained popularity in materials science, biochemistry and biophysics with various application areas and expanding data scale. Assisted Model Building with Energy Refinement (AMBER) is one of the most widely used software packages for conducting MD simulations. However, the speed of AMBER MD simulations for system with millions of atoms in microsecond scale still need to be improved. In this paper, we propose a parallel acceleration strategy for AMBER on Tianhe-2 supercomputer. The parallel optimization of AMBER is carried out on three different levels: fine grained OpenMP parallel on a single MIC, single-node CPU/MIC collaborated parallel optimization and multi-node multi-MIC collaborated parallel acceleration. By the three levels of parallel acceleration strategy above, we achieved the highest speedup of 25–33 times compared with the original program. Source Code: https://github.com/tianhe2/mAMBER","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134490291","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.2016.7822558
M. Zhou, Daisy Li, X. Huan, Joseph Manthey, E. Lioutikova, Hong Zhou
The true power of genome editing mechanism known as RNA-programmable CRISPR Cas9 endonuclease system, lies in the fact that Cas9 can be guided to any loci complementary to a 20-nt RNA, single guide RNA (sgRNA), to cleave double stranded DNA, and therefore allows the introduction of wanted mutations. Unfortunately, sgRNA is prone to off-target homologous attachment, thus guiding Cas9 to cleave DNA sequences at unwanted sites. Using human genome and Streptococcus pyogenes Cas9 (SpCas9) as the example, this article analyzed the probabilities of off-target sites of sgRNAs and discovered that for large-size genomes such as human genome, off-target sites are nearly inevitable for sgRNA selection. Based on the mathematical analysis, it seems that the double nicking approach is currently the only feasible solution to promise genome editing specificity. An effective computational algorithm for off-target homology searching is also implemented to confirm the mathematical analysis.
被称为RNA可编程CRISPR Cas9内切酶系统的基因组编辑机制的真正力量在于，Cas9可以被引导到与20 nt RNA(单导RNA (sgRNA))互补的任何位点上，以切割双链DNA，从而允许引入所需的突变。不幸的是，sgRNA容易脱靶同源附着，从而引导Cas9在不需要的位点切割DNA序列。本文以人类基因组和化脓性链球菌Cas9 (SpCas9)为例，分析了sgRNA脱靶位点的概率，发现对于人类基因组这样的大尺度基因组，sgRNA选择的脱靶位点几乎是不可避免的。基于数学分析，双切口方法似乎是目前唯一可行的解决方案，以保证基因组编辑的特异性。实现了一种有效的脱靶同源搜索计算算法来验证数学分析。
{"title":"Mathematical and computational analysis of CRISPR Cas9 sgRNA off-target homologies","authors":"M. Zhou, Daisy Li, X. Huan, Joseph Manthey, E. Lioutikova, Hong Zhou","doi":"10.1109/BIBM.2016.7822558","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822558","url":null,"abstract":"The true power of genome editing mechanism known as RNA-programmable CRISPR Cas9 endonuclease system, lies in the fact that Cas9 can be guided to any loci complementary to a 20-nt RNA, single guide RNA (sgRNA), to cleave double stranded DNA, and therefore allows the introduction of wanted mutations. Unfortunately, sgRNA is prone to off-target homologous attachment, thus guiding Cas9 to cleave DNA sequences at unwanted sites. Using human genome and Streptococcus pyogenes Cas9 (SpCas9) as the example, this article analyzed the probabilities of off-target sites of sgRNAs and discovered that for large-size genomes such as human genome, off-target sites are nearly inevitable for sgRNA selection. Based on the mathematical analysis, it seems that the double nicking approach is currently the only feasible solution to promise genome editing specificity. An effective computational algorithm for off-target homology searching is also implemented to confirm the mathematical analysis.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520727","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.2016.7822572
Shuohong Wang, Xiang Liu, Jingwen Zhao, Ye Liu, Y. Chen
3D motion data of fish school is more valuable than 2D data for behavior and other researches. This paper proposes to use a master view tracking first strategy based on a novel master-slave camera setup. On this basis, fish are firstly tracked in master view in 2D after being extracted via an eye-focused Gaussian Mixture Model (E-GMM) detector. Then 3D trajectories are reconstructed by associating 2D tracking results in master view and detection results in slave views after fish in slave views are localized using an eye-focused Gabor (E-Gabor) detector. Experiments on data sets with different fish densities demonstrate that the proposed method outperforms two state-of-the-art methods in terms of 5 evaluation metrics.
{"title":"3D tracking swimming fish school using a master view tracking first strategy","authors":"Shuohong Wang, Xiang Liu, Jingwen Zhao, Ye Liu, Y. Chen","doi":"10.1109/BIBM.2016.7822572","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822572","url":null,"abstract":"3D motion data of fish school is more valuable than 2D data for behavior and other researches. This paper proposes to use a master view tracking first strategy based on a novel master-slave camera setup. On this basis, fish are firstly tracked in master view in 2D after being extracted via an eye-focused Gaussian Mixture Model (E-GMM) detector. Then 3D trajectories are reconstructed by associating 2D tracking results in master view and detection results in slave views after fish in slave views are localized using an eye-focused Gabor (E-Gabor) detector. Experiments on data sets with different fish densities demonstrate that the proposed method outperforms two state-of-the-art methods in terms of 5 evaluation metrics.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133171954","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.2016.7822644
Iasmini Lima, Matheus Oliveira, Diego S. Kieckbusch, M. Holanda, M. E. Walter, Aleteia P. F. Araujo, M. Victorino, Waldeyr M. C. Silva, Sérgio Lifschitz
Many research projects in bioinformatics may be viewed as scientific workflows. Biologists often run multiple times the same workflow with different parameters in order to refine their data analysis. These executions generate a large volume of files with different formats, which need to be stored for future evaluations. New database models, like NoSQL systems, could be considered to deal with large volumes of data, particularly in distributed systems. This work presents a data replication impact assessment from the execution of scientific workflows for two NoSQL database management systems: Cassandra and MongoDB.
{"title":"An evaluation of data replication for bioinformatics workflows on NoSQL systems","authors":"Iasmini Lima, Matheus Oliveira, Diego S. Kieckbusch, M. Holanda, M. E. Walter, Aleteia P. F. Araujo, M. Victorino, Waldeyr M. C. Silva, Sérgio Lifschitz","doi":"10.1109/BIBM.2016.7822644","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822644","url":null,"abstract":"Many research projects in bioinformatics may be viewed as scientific workflows. Biologists often run multiple times the same workflow with different parameters in order to refine their data analysis. These executions generate a large volume of files with different formats, which need to be stored for future evaluations. New database models, like NoSQL systems, could be considered to deal with large volumes of data, particularly in distributed systems. This work presents a data replication impact assessment from the execution of scientific workflows for two NoSQL database management systems: Cassandra and MongoDB.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133781263","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.2016.7822795
Jin-Xing Liu, Xiangzhen Kong, C. Zheng, J. Shang, Wei Zhang
Recently, feature extraction and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as genome data. In this paper, a new feature extraction method based on sparse singular value decomposition (SSVD) is developed. SSVD algorithm is applied to extract differentially expressed genes from two different genome datasets that are all from The Cancer Genome Atlas (TCGA), and then the extracted genes are evaluated by the tools based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis, the p-norm robust feature extraction and sparse principal component analysis. The experimental GO analysis results show that SSVD method outperforms the competitive algorithms. The KEGG analysis results demonstrate the genes which participate in the pathways in cancer. The elaborate experiments prove that SSVD is an effective feature selection method compared with the competitive methods. The KEGG analysis results may provide a meaningful reference to carry out further study for professionals in the field of biomedical science.
近年来，特征提取和降维已经成为许多数据挖掘任务的基本工具，特别是处理高维数据，如基因组数据。本文提出了一种基于稀疏奇异值分解(SSVD)的特征提取方法。采用SSVD算法从两个不同的基因组数据集(均来自The Cancer genome Atlas, TCGA)中提取差异表达基因，并基于基因本体(Gene Ontology, GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of genes and Genomes, KEGG)途径富集分析工具对提取的基因进行评估。作为一种基因提取方法，SSVD还与独立成分分析、p范数鲁棒特征提取和稀疏主成分分析等现有特征提取方法进行了比较。实验结果表明，SSVD方法优于竞争算法。KEGG分析结果显示了参与癌症通路的基因。实验证明，与竞争方法相比，SSVD是一种有效的特征选择方法。KEGG分析结果可为生物医学领域的专业人员进一步开展研究提供有意义的参考。
{"title":"Sparse singular value decomposition-based feature extraction for identifying differentially expressed genes","authors":"Jin-Xing Liu, Xiangzhen Kong, C. Zheng, J. Shang, Wei Zhang","doi":"10.1109/BIBM.2016.7822795","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822795","url":null,"abstract":"Recently, feature extraction and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as genome data. In this paper, a new feature extraction method based on sparse singular value decomposition (SSVD) is developed. SSVD algorithm is applied to extract differentially expressed genes from two different genome datasets that are all from The Cancer Genome Atlas (TCGA), and then the extracted genes are evaluated by the tools based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis, the p-norm robust feature extraction and sparse principal component analysis. The experimental GO analysis results show that SSVD method outperforms the competitive algorithms. The KEGG analysis results demonstrate the genes which participate in the pathways in cancer. The elaborate experiments prove that SSVD is an effective feature selection method compared with the competitive methods. The KEGG analysis results may provide a meaningful reference to carry out further study for professionals in the field of biomedical science.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004401","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.2016.7822665
Zhijun Yao, Bin Hu, Xuejiao Chen, Yuanwei Xie, Lei Fang
Recent studies suggested that cognitive impairments and memory difficulties in cancer survivors were associated with topology changes of brain network, particularly in terms of the functional and structural abnormalities. However, little is known about the modular reconfiguration of metabolic brain network among this population. In this study, we recruited 78 patients with pre-treatment cancer and 80 age- and gender-matched normal controls (NCs), and constructed the metabolic brain networks derived from resting-state 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to assess the alters of modularity pattern in cancer. The measurements of the participation index (PI) and mutual information (MI) were calculated for the cancer and NC groups. Compared with NC group, one module composed by the hippocampus, the amygdala and frontal and temporal regions was absented in cancer group. Moreover, cancer patients showed abnormal topology pattern in their metabolic networks (i.e., increased local efficiency and reduced global efficiency). Although node-wise PI shared positive correlated with normalized metabolism uptake in both groups, the more energy consumption were observed in metabolism network of cancer group that might be indicative of reduced capability of information processing. In addition, the between-group MIs were gradually increased over a range of thresholds. Our results suggested that modular pattern of the metabolic brain network seemed to re-shape its organization in cancer, which might uncover the neurobiological mechanisms underlying cancer-related cognitive dysfunction.
{"title":"Modular reconfiguration of metabolic brain networks in health and cancer: A resting-state PET study","authors":"Zhijun Yao, Bin Hu, Xuejiao Chen, Yuanwei Xie, Lei Fang","doi":"10.1109/BIBM.2016.7822665","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822665","url":null,"abstract":"Recent studies suggested that cognitive impairments and memory difficulties in cancer survivors were associated with topology changes of brain network, particularly in terms of the functional and structural abnormalities. However, little is known about the modular reconfiguration of metabolic brain network among this population. In this study, we recruited 78 patients with pre-treatment cancer and 80 age- and gender-matched normal controls (NCs), and constructed the metabolic brain networks derived from resting-state 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to assess the alters of modularity pattern in cancer. The measurements of the participation index (PI) and mutual information (MI) were calculated for the cancer and NC groups. Compared with NC group, one module composed by the hippocampus, the amygdala and frontal and temporal regions was absented in cancer group. Moreover, cancer patients showed abnormal topology pattern in their metabolic networks (i.e., increased local efficiency and reduced global efficiency). Although node-wise PI shared positive correlated with normalized metabolism uptake in both groups, the more energy consumption were observed in metabolism network of cancer group that might be indicative of reduced capability of information processing. In addition, the between-group MIs were gradually increased over a range of thresholds. Our results suggested that modular pattern of the metabolic brain network seemed to re-shape its organization in cancer, which might uncover the neurobiological mechanisms underlying cancer-related cognitive dysfunction.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132394965","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.2016.7822500
Keke Chen, Venkata Sai Abhishek Gogu, Di Wu, Jiang Ning
Lineage analysis has been an important method for understanding the mutation patterns and the diversity of genes, such as antibodies. A mutation lineage is typically represented as a tree structure, describing the possible mutation paths. Generating lineage trees from sequence data imposes two unique challenges: (1) Types of constraints might be defined on top of sequence data and tree structures, which have to be appropriately formulated and maintained by the algorithms. (2) Enumerating all possible trees that satisfy constraints is typically computationally intractable. In this paper, we present a COnstrained Lineage Tree generation framework (COLT) that builds lineage trees from sequences, based on local and global constraints specified by domain experts and heuristics derived from the mutation processes. Our formal analysis and experimental results show that this framework can efficiently generate valid lineage trees, while strictly satisfying the constraints specified by domain experts.
{"title":"COLT: COnstrained Lineage Tree Generation from sequence data","authors":"Keke Chen, Venkata Sai Abhishek Gogu, Di Wu, Jiang Ning","doi":"10.1109/BIBM.2016.7822500","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822500","url":null,"abstract":"Lineage analysis has been an important method for understanding the mutation patterns and the diversity of genes, such as antibodies. A mutation lineage is typically represented as a tree structure, describing the possible mutation paths. Generating lineage trees from sequence data imposes two unique challenges: (1) Types of constraints might be defined on top of sequence data and tree structures, which have to be appropriately formulated and maintained by the algorithms. (2) Enumerating all possible trees that satisfy constraints is typically computationally intractable. In this paper, we present a COnstrained Lineage Tree generation framework (COLT) that builds lineage trees from sequences, based on local and global constraints specified by domain experts and heuristics derived from the mutation processes. Our formal analysis and experimental results show that this framework can efficiently generate valid lineage trees, while strictly satisfying the constraints specified by domain experts.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132477300","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.2016.7822603
S. N. Chandrasekaran, Jun Huan
The paradigm of drug discovery has moved from finding new drugs that exhibit therapeutic properties for a disease to reusing existing approved drugs for a newer disease. The association between a drug and a disease involves a complex network of targets and pathways. In order to provide new insights, there has been a constant need for sophisticated tools that have the potential to discover new associations from the underlying drugs-disease interactions. In addition to computational tools, there has been an explosion of data available in terms of drugs, disease and their activity profiles. On one hand, researchers have been using existing machine learning tools that have shown great promise in predicting associations but on the other hand there has been a void in exploiting advance machine learning frameworks to handle this kind of data integration. In this paper, we propose a learning framework called weighted multi-view learning that is a variant of the Multi-view learning framework in which the views are assumed to contribute equally to the prediction whereas our method learns a weight for each view since we hypothesize that certain views might have better prediction capability than others.
{"title":"Weighted multiview learning for predicting drug-disease associations","authors":"S. N. Chandrasekaran, Jun Huan","doi":"10.1109/BIBM.2016.7822603","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822603","url":null,"abstract":"The paradigm of drug discovery has moved from finding new drugs that exhibit therapeutic properties for a disease to reusing existing approved drugs for a newer disease. The association between a drug and a disease involves a complex network of targets and pathways. In order to provide new insights, there has been a constant need for sophisticated tools that have the potential to discover new associations from the underlying drugs-disease interactions. In addition to computational tools, there has been an explosion of data available in terms of drugs, disease and their activity profiles. On one hand, researchers have been using existing machine learning tools that have shown great promise in predicting associations but on the other hand there has been a void in exploiting advance machine learning frameworks to handle this kind of data integration. In this paper, we propose a learning framework called weighted multi-view learning that is a variant of the Multi-view learning framework in which the views are assumed to contribute equally to the prediction whereas our method learns a weight for each view since we hypothesize that certain views might have better prediction capability than others.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"100 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190462","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.2016.7822657
Runzhi Li, Hongling Zhao, Yusong Lin, Andrew S. Maxwell, Chaoyang Zhang
A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.
{"title":"Multi-label classification for intelligent health risk prediction","authors":"Runzhi Li, Hongling Zhao, Yusong Lin, Andrew S. Maxwell, Chaoyang Zhang","doi":"10.1109/BIBM.2016.7822657","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822657","url":null,"abstract":"A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134600494","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.2016.7822744
Cunjin Luo, Kuanquan Wang, Henggui Zhang
The identified genetic short QT syndrome (SQTS) is associated with an increased risk of arrhythmia and sudden death. This study was to investigate the potential effects of propafenone on KCNH2-linked short QT syndrome (SQT1) using a multi-scale biophysically detailed model of the heart developed by ten Tusscher and Panfilov. The ion electrical conductivities were reduced by propafenone in order to simulate the pharmacological effects in healthy and SQT1 cells. Based on the experimental data of McPate et al., the pharmacological effect of propafenone was modelled by dose-dependent IKr blocking. Action potential (AP) profiles and 1D tissue level were analyzed to predict the effects of propafenone on SQT1. Both low- and high- dose of propafenone prolonged APD and QT interval in SQT1 cells. It suggests the superior efficacy of high dose of propafenone on SQT1. However, propafenone did not significantly alter the healthy APD or QT interval at low dose, whereas markedly shortened them at high dose. Our simulation data show that propafenone has a dose-dependently anti-arrhythmic effect on SQT1, and a pro-arrhythmic effect on healthy cells. These computer simulations help to better understand the underlying mechanisms responsible for the initiation or termination of arrhythmias in healthy or SQT1 patients using propafenone.
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