2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)最新文献

Microbial abundance analysis and phylogenetic adoption in functional metagenomics 功能宏基因组学中微生物丰度分析与系统发育采用

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-10-05 DOI: 10.1109/CIBCB.2017.8058557

Jyotsna Talreja Wassan, Haiying Wang, Fiona Browne, Huiru Zheng

{"title":"Microbial abundance analysis and phylogenetic adoption in functional metagenomics","authors":"Jyotsna Talreja Wassan, Haiying Wang, Fiona Browne, Huiru Zheng","doi":"10.1109/CIBCB.2017.8058557","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058557","url":null,"abstract":"Metagenomics is an unobtrusive science of studying uncultivated microbes sampled directly from an environment, e.g. soil, ocean, air, human body, or animals, etc. Functional metagenomics particularly deals with linking microbes to environmental derivations, such as classifying the role of human gut microbiome into a diseased or non-diseased state. Ongoing research in this area includes analyzing the structure of microbial communities, and relate it to functional analysis. We present an integrative experimental framework for functional metagenomics, including data driven (abundance count of microbial species) and knowledge driven (phylogenetic tree structure) contexts. Our related experiments, indicate that i) feature selection improves the performance of classifying human microbiome samples, ii) the classification of human microbiome remains a challenging problem while incorporating phylogenetic structures. For example, our best accuracy attained on the Costello body site (CBH) dataset with forehead and external ear as body sites, is 89.13 % with a non-phylogenetic model, and 78.26 % with a phylogenetic model. This forms a potential research direction of further exploration of space for incorporating phylogeny in microbial analysis and hence developing integrative computational models for deriving functional phenotypes, based on metagenomic sequencing data.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409813","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}

引用次数: 1

Using Benford's law to detect anomalies in electroencephalogram: An application to detecting alzheimer's disease 利用本福德定律检测脑电图异常:在阿尔茨海默病检测中的应用

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-10-05 DOI: 10.1109/CIBCB.2017.8058547

Santosh Tirunagari, D. Abásolo, A. Iorliam, A. Ho, N. Poh

{"title":"Using Benford's law to detect anomalies in electroencephalogram: An application to detecting alzheimer's disease","authors":"Santosh Tirunagari, D. Abásolo, A. Iorliam, A. Ho, N. Poh","doi":"10.1109/CIBCB.2017.8058547","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058547","url":null,"abstract":"Alzheimer's disease (AD) is a neurodegenerative disease caused by the progressive death of brain cells over time. It represents the most frequent cause of dementia in the western world, and affects an individual's cognitive ability and psychological capacity. While clinical diagnoses of AD are made primarily on the basis of clinical evaluation and mental health tests, diagnostic certainty is only possible through necropsy. One non-invasive approach to investigating AD is to use electroencephalograms (eEGs), which reflect brain electrical activity and so can be used to detect electrical abnormalities in brain signals with non-invasive cranial surface electrodes. Generally EEGs in AD patients show a shift to lower frequencies in spectral analysis and display less complexity and contain more regular patterns compared to those of control subjects. Here we present a method for differentiating AD patients from healthy ones based on their EEG signals using Benford's law and support vector machines (SVMs) with a radial basis function (RBF) kernel. EEG signals from eleven AD and eleven age-matched controls were divided into artefact-free 5-sec epochs and used to train an SVM. 10 fold cross validation was performed at both the epoch- and subject-level to evaluate the importance of each electrode in discriminating between AD and healthy subjects. Substantive variability was seen across the different electrodes, with electrodes O1, O2 and C4 particularly being important. Performance across the electrodes was reduced when subject-level cross validation was performed, but relative performance across the electrodes was consistent with that found using epoch-level cross validation.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893859","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}

引用次数: 2

A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions 一个新的混合差分进化策略应用于分类器设计的死亡率预测在成人重症监护入院

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-10-04 DOI: 10.1109/CIBCB.2017.8058544

A. Shenfield, M. Rodrigues, Hossam Nooreldeen, J. Moreno-Cuesta

{"title":"A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions","authors":"A. Shenfield, M. Rodrigues, Hossam Nooreldeen, J. Moreno-Cuesta","doi":"10.1109/CIBCB.2017.8058544","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058544","url":null,"abstract":"The optimisation of classifier performance in pattern recognition and medical prognosis tasks is a complex and poorly miderstood problem. Classifier performance is greatly affected by the choice of artificial neural network architecture and starting weights and biases — yet there exists very little guidance in the literature as to how to choose these parameters. Recently evolutionary artificial neural networks have been proposed to mitigate some of these problems; however, whilst evolutionary methods are extremely effective in finding global optima, they are notoriously computationally expensive (often requiring tens of thousands of function evaluations to arrive at a solution). This paper proposes a novel hybrid adaptive approach to the optimisation of artificial neural network parameters where the global search capabilities of differential evolution and the efficiency of local search heuristics (such as resilient back-propagation for artificial neural network training) are combined. A state-of-the-art adaptive differential evolution algorithm, JADE, has been chosen as the basis for this hybrid algorithm due to its proven effectiveness in optimising high dimensional problems. The performance of this hybrid adaptive differential evolution algorithm is then demonstrated in the design of a classifier for mortaUty risk prediction in a critical care environment, where the optimised classifier is shown to outperform the current state-of-the-art in risk prediction.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660517","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}

引用次数: 2

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance 类不平衡多类医疗诊断问题中人工神经网络的多目标演化

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-10-04 DOI: 10.1109/CIBCB.2017.8058553

A. Shenfield, Shahin Rostami

引用次数: 15

Data-driven longitudinal modeling and prediction of symptom dynamics in major depressive disorder: Integrating factor graphs and learning methods 重度抑郁症症状动态的数据驱动纵向建模与预测:整合因子图与学习方法

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-10-04 DOI: 10.1109/CIBCB.2017.8058559

A. Athreya, Subho Sankar Banerjee, Drew R Neavin, R. Kaddurah-Daouk, A. Rush, M. Frye, Liewei Wang, R. Weinshilboum, W. Bobo, R. Iyer

{"title":"Data-driven longitudinal modeling and prediction of symptom dynamics in major depressive disorder: Integrating factor graphs and learning methods","authors":"A. Athreya, Subho Sankar Banerjee, Drew R Neavin, R. Kaddurah-Daouk, A. Rush, M. Frye, Liewei Wang, R. Weinshilboum, W. Bobo, R. Iyer","doi":"10.1109/CIBCB.2017.8058559","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058559","url":null,"abstract":"This paper proposes a data-driven longitudinal model that brings together factor graphs and learning methods to demonstrate a significant improvement in predictability in clinical outcomes of patients with major depressive disorder treated with antidepressants. Using data from the Mayo Clinic PGRN-AMPS trial and the STAR∗D trial for validation, this work makes two significant contributions in the context of predictability in psychiatric therapeutic outcomes. First, we establish symptom dynamics in response to antidepressants by using the forward algorithm on a factor graph. Symptom dynamics are the changes in the symptom severity that are most likely to occur because of the antidepressants taken during the trial, and the associated clinical outcomes at 4 weeks and 8 weeks into the trial. The structure of the factor graph is inferred by using unsupervised learning to stratify patients by the similarity of their overall symptom severity. Second, by using metabolomics data as an accurate biological measure in addition to symptom survey data and other patient history information, the prediction of clinical outcomes such as response and remission significantly improved from 30% to 68% in men, and from 35% to 72% in women. This work demonstrates a significant difference in how men and women respond to antidepressants in terms of their symptom dynamics, and also shows that top predictors of clinical outcomes for men and women are significantly different and known to play a role in behavioral sciences.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123117206","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}

引用次数: 9

The identification of replication origin in bacterial genomes by cumulated phase signal 利用累积相位信号识别细菌基因组复制起源

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058561

D. Maderankova, K. Sedlář, Martin Vítek, Helena Skutková

{"title":"The identification of replication origin in bacterial genomes by cumulated phase signal","authors":"D. Maderankova, K. Sedlář, Martin Vítek, Helena Skutková","doi":"10.1109/CIBCB.2017.8058561","DOIUrl":"https://doi.org/10.1109/CIBCB.2017.8058561","url":null,"abstract":"The origin of replication (oriC) plays an important role in the cell cycle as the place where DNA replication is initiated. In bacterial cells, a single replication origin can be found and its correct identification is necessary in the annotation process of newly sequenced genomes. Although the rearrangement of a whole genome sequence according to oriC should be a standard procedure, public databases still contain lots of genomes starting at a random place. This situation complicates the comparative analysis of whole bacterial genomes as only two genomes rearranged according to oriC can be reliably aligned. In this paper, we present a novel technique for oriC prediction based exclusively on utilization of cumulated phase signal which distinguishes our approach from current techniques combining application of genomic signal processing techniques with a standard character based comparison. Proposed technique is therefore fast and suitably complements the current pipeline for comparison of whole bacterial genomes by aligned downsampled signals.","PeriodicalId":283115,"journal":{"name":"2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970812","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}

引用次数: 3

Applying brain emotional learning based fuzzy inference system for EEG signal classication between schizophrenics and control participant 基于脑情绪学习的模糊推理系统在精神分裂症与对照组脑电信号分类中的应用

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058555

Bahareh Javadi, S. Setayeshi, G. Price

引用次数: 2

Finding sub-graphs from contact map overlap problem 从接触图重叠问题求子图

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058537

K. Vani, K. P. Kumar, Movva Veda Sri

引用次数: 2

A novel representation for boolean networks designed to enhance heritability and scalability 一种新的布尔网络表示，旨在提高遗传性和可扩展性

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058530

D. Ashlock, G. A. Ruz

引用次数: 3

Disease outbreak prediction by data integration and multi-task learning 通过数据整合和多任务学习预测疾病爆发

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2017-08-01 DOI: 10.1109/CIBCB.2017.8058551

Batuhan Bardak, Mehmet Tan

引用次数: 0