IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology最新文献

Comparison tools for lncRNA identification: analysis among plants and humans lncRNA鉴定的比较工具:植物和人类的分析

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2020-10-27 DOI: 10.1109/CIBCB48159.2020.9277716

T. C. Negri, A. R. Paschoal, W. A. Alves

{"title":"Comparison tools for lncRNA identification: analysis among plants and humans","authors":"T. C. Negri, A. R. Paschoal, W. A. Alves","doi":"10.1109/CIBCB48159.2020.9277716","DOIUrl":"https://doi.org/10.1109/CIBCB48159.2020.9277716","url":null,"abstract":"This article has as its main objective the evaluation of the differences between long non-coding RNAs of plants and humans. Long non-coding RNAs are also known as lncRNAs. The lncRNAS belong to the class of RNAs that do not encode proteins and are related to several biological functions, such as chromatin modifications, post-transcriptional regulation and mainly in the different development processes of diseases such as cancer. In this work, we want to verify the existence of differences in lncRNAs in plants and humans using state-of-the-art approaches to identify lncRNAs. The main reason for the study is that there are differences between the miNAs (small ncRNAs) of plants and humans, whether in biological or computational characteristics, for lncRNAs it is still an open question. To answer this question, this paper proposes to show the results of two ncRNAS prediction tools, trained with humans, and which are widely used for lncRNA prediction: CPC2 and CPAT. We will also show results from tools used to predict lncRNAS in plants, which are trained with plant data: the RNAplonc, the PlncPRO tool that contains two versions, one for monocot and one for dicot and the LGC tool that was trained with plants and humans. The results of tools trained with human data will also be displayed: PLEK, CPPRED and PredLnc-GFStack. These eight tools were applied in two sets of tests, one composed of eight species of plants (Amborella trichopoda, Brachypodium distachyon, Citrus sinensis, Manihot esculenta, Ricinus communis, Solanum tuberosum, Sorghum bicolor, Zea mays) and the other composed of human lncRNAS.","PeriodicalId":89148,"journal":{"name":"IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"16 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80972329","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: 0

Message from the conference chair 来自会议主席的留言

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2016-10-01 DOI: 10.1109/CIBCB.2016.7758175

S. Auephanwiriyakul

引用次数: 0

Messages from the technical program chairs 来自技术项目主席的信息

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2016-10-01 DOI: 10.1109/CIBCB.2016.7758176

N. Theera-Umpon, M. Popescu, Patiwet Wuttisarnwattana

引用次数: 0

Side-chain flexibility in protein docking 蛋白质对接中的侧链灵活性

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2015-01-01 DOI: 10.1109/CIBCB.2015.7300315

Hui Liu, Feng Lin, Jianli Yang, Xiu-Ling Liu

引用次数: 0

Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach. 使用云计算大数据方法的电子健康记录数据预测运动神经元疾病进展的严重程度。

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2014-05-01 DOI: 10.1109/CIBCB.2014.6845506

Kyung Dae Ko, Tarek El-Ghazawi, Dongkyu Kim, Hiroki Morizono

{"title":"Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach.","authors":"Kyung Dae Ko, Tarek El-Ghazawi, Dongkyu Kim, Hiroki Morizono","doi":"10.1109/CIBCB.2014.6845506","DOIUrl":"https://doi.org/10.1109/CIBCB.2014.6845506","url":null,"abstract":"Motor neuron diseases (MNDs) are a class of progressive neurological diseases that damage the motor neurons. An accurate diagnosis is important for the treatment of patients with MNDs because there is no standard cure for the MNDs. However, the rates of false positive and false negative diagnoses are still very high in this class of diseases. In the case of Amyotrophic Lateral Sclerosis (ALS), current estimates indicate 10% of diagnoses are false-positives, while 44% appear to be false negatives. In this study, we developed a new methodology to profile specific medical information from patient medical records for predicting the progression of motor neuron diseases. We implemented a system using Hbase and the Random forest classifier of Apache Mahout to profile medical records provided by the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) site, and we achieved 66% accuracy in the prediction of ALS progress.","PeriodicalId":89148,"journal":{"name":"IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2014 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CIBCB.2014.6845506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32968947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach 肿瘤预测的基因表达和全基因组DNA甲基化综合分析:一种基于关联规则挖掘的方法

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2013-04-16 DOI: 10.1109/CIBCB.2013.6595397

Saurav Mallik, A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay

{"title":"Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach","authors":"Saurav Mallik, A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay","doi":"10.1109/CIBCB.2013.6595397","DOIUrl":"https://doi.org/10.1109/CIBCB.2013.6595397","url":null,"abstract":"Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.","PeriodicalId":89148,"journal":{"name":"IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"3 4 1","pages":"120-127"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79713417","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: 34

An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions. 通过寻找高度保守的核心区域改进DNA基序推断的探索。

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2013-04-01 Epub Date: 2013-09-12 DOI: 10.1109/CIBCB.2013.6595389

Jeffrey A Thompson, Clare Bates Congdon

引用次数: 1

Matrix Factorization for Transcriptional Regulatory Network Inference. 转录调控网络推理的矩阵分解。

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2012-05-01 DOI: 10.1109/CIBCB.2012.6217256

Michael F Ochs, Elana J Fertig

引用次数: 38

Data Synthesis and Tool Development for Exploring Imaging Genomic Patterns. 探索成像基因组模式的数据综合和工具开发。

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2009-03-30 DOI: 10.1109/CIBCB.2009.4925742

Sungeun Kim, Li Shen, Andrew J Saykin, John D West

{"title":"Data Synthesis and Tool Development for Exploring Imaging Genomic Patterns.","authors":"Sungeun Kim, Li Shen, Andrew J Saykin, John D West","doi":"10.1109/CIBCB.2009.4925742","DOIUrl":"https://doi.org/10.1109/CIBCB.2009.4925742","url":null,"abstract":"Recent advances in brain imaging and high throughput genotyping techniques enable new approaches to study the influence of genetic variation on brain structure and function. However, major computational challenges are bottlenecks for comprehensive joint analysis of these high-dimensional image and genomic data. We report our initial progress in developing an imaging genomic browsing system for integrated exploration of neuroimaging and genomic data. We describe a method for synthesizing a set of realistic neuroimaging and genomic data, where the relationships between imaging phenotypes and genotypes are known. This data set is used to demonstrate the functionality of our system, which is designed for effectively exploring the neuroanatomical distribution of statistical results that measure the associations between brain imaging phenotypes and genotypes on a genome-wide scale. The proposed system has substantial potential for enabling discovery of important imaging genomic associations through visual evaluation and can be extended towards several directions.","PeriodicalId":89148,"journal":{"name":"IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology","volume":"2009 ","pages":"298-305"},"PeriodicalIF":0.0,"publicationDate":"2009-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CIBCB.2009.4925742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29789984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Computational Intelligence in Bioinformatics 生物信息学中的计算智能

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Pub Date : 2008-02-13 DOI: 10.1007/978-3-540-76803-6

A. Kelemen, A. Abraham, Yuehui Chen

引用次数: 44