2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第9页

Generation of novel encrypted code using cryptography for multiple level data security for Electronic Patient Record 利用密码学生成新型加密代码，实现电子病历多层次数据安全

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359806

M. Bhattacharya, K. Pal, G. Ghosh, Som Shuvra Mandal

引用次数: 4

Semantic rules for extracting proteins functions information from biomedical abstracts 生物医学文摘中蛋白质功能信息提取的语义规则

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359749

K. Taha

{"title":"Semantic rules for extracting proteins functions information from biomedical abstracts","authors":"K. Taha","doi":"10.1109/BIBM.2015.7359749","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359749","url":null,"abstract":"We present a classifier system called SRPFP that predicts the functions of un-annotated proteins. SRPFP aims at enhancing the state of the art of biological text mining. It analyzes biomedical texts in order to discover protein function information that is difficult to retrieve. It employs semantic rules for extracting proteins functions information from biomedical abstracts. It applies a novel model and linguistic computational techniques for extracting the functional relationship from different structural forms of terms in the sentences of biological abstracts. Specifically, SRPFP extracts phrases that represent functional relationships between proteins and molecules. These molecules usually bind to the proteins and are highly predictive of the functions of these proteins. The proposed semantic rules can identify the semantic relationship between each co-occurrence of a protein-molecule pair using the syntactic structures of sentences and linguistics theories. SRPFP represents each protein by the molecules that have high co-occurrences with the protein in biomedical abstracts. This is because such molecules are good characteristics and indicators of the functions of proteins. SRPFP measures the semantic similarity between the molecules representing an un-annotated protein p and the molecules representing annotated proteins and assigns p the functions of annotated proteins that are similar to p.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126130101","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

On frequency dependencies of sliding window correlation 滑动窗相关的频率依赖关系

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359708

S. Shakil, S. Keilholz, Chin-Hui Lee

引用次数: 17

Chronic lymphocytic leukemia patient classification methodology through flow cytometry analysis 通过流式细胞术分析慢性淋巴细胞白血病患者的分类方法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359778

E. Papadopoulou, K. Kotta, P. Moschonas, V. Douka, A. Anagnostopoulos, K. Stamatopoulos, D. Tzovaras

{"title":"Chronic lymphocytic leukemia patient classification methodology through flow cytometry analysis","authors":"E. Papadopoulou, K. Kotta, P. Moschonas, V. Douka, A. Anagnostopoulos, K. Stamatopoulos, D. Tzovaras","doi":"10.1109/BIBM.2015.7359778","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359778","url":null,"abstract":"Flow cytometry (FC) is widely used for diagnostic purposes in clinical practice. This analysis typically aims at clustering cellular events according to their biological characteristics, known as gating, and then use the selected clusters in order to conclude about clinical outcomes. As each step of this process is highly subjective, various proposed methods have attempted to automate each step of the procedure separately, but any method has been proposed in order to automate the whole diagnostic process. We constructed a tool that simulates the experts decisions during the whole process in order to conclude if a sample is pathologic or not (`healthy'). We used flow cytometric data from 10 individuals with a diagnosis of chronic lymphocytic leukemia (CLL) from a panel that produces 7 files for each sample. With the help of the present tool we were able to identify whether the analysis of the tested sample confirms the diagnosis of CLL, thus successfully reproducing the experts' decisions at each step of the diagnostic workflow. The validation was conducted by experts against the traditional manual procedure. The proposed methodology is the first attempt to automate the entire process, which is a prerequisite for a fully automated diagnostic system that would ensure objectivity to the clinical diagnostic procedure. The experimental results presented herein show that our proposed new technique has satisfying performance at each level of evaluation.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127482306","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

Leveraging the k-Nearest Neighbors classification algorithm for Microbial Source Tracking using a bacterial DNA fingerprint library 利用细菌DNA指纹库进行微生物源跟踪的k近邻分类算法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359930

Jeffrey D. McGovern, Alex Dekhtyar, C. Kitts, Michael Black, Jennifer Vanderkelen, Anya L. Goodman

{"title":"Leveraging the k-Nearest Neighbors classification algorithm for Microbial Source Tracking using a bacterial DNA fingerprint library","authors":"Jeffrey D. McGovern, Alex Dekhtyar, C. Kitts, Michael Black, Jennifer Vanderkelen, Anya L. Goodman","doi":"10.1109/BIBM.2015.7359930","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359930","url":null,"abstract":"Fecal contamination in bodies of water is an issue that cities must combat regularly. Often, city governments must restrict access to water sources until the contaminants dissipate. Sourcing the species of the fecal matter helps curb the issue in the future, giving city governments the ability to mitigate the effects before they occur again. Microbial Source Tracking (MST) aims to determine source host species of strains of microbiological lifeforms and library-based MST is one method that can assist in sourcing fecal matter. Recently, the Biology Department in conjunction with the Computer Science Department at California Polytechnic State University San Luis Obispo (Cal Poly) teamed up to build a database called the Cal Poly Library of Pyroprints (CPLOP). Students collect fecal samples, culture and pyrosequence the E. coli in the samples, and insert this data, called pyroprints, into CPLOP. Using two intergenic transcribed spacer regions of DNA, Cal Poly biologists perform studies on strain differentiation. We propose using k-Nearest Neighbors, a straightforward machine learning technique, to classify the host species of a given pyroprint, construct four algorithms to resolve the regions, and investigate classification accuracy.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124294363","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

Integrating prior biological knowledge and graphical LASSO for network inference 结合先验生物知识和图形LASSO进行网络推理

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359905

Yiming Zuo, Guoqiang Yu, H. Ressom

{"title":"Integrating prior biological knowledge and graphical LASSO for network inference","authors":"Yiming Zuo, Guoqiang Yu, H. Ressom","doi":"10.1109/BIBM.2015.7359905","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359905","url":null,"abstract":"Systems biology aims at unravelling the mechanisms of complex diseases by investigating how individual elements of the cell (e.g., genes, proteins, metabolites, etc.) interact with each other. Network-based methods provide an intuitive framework to model, characterize, and understand these interactions. To reconstruct a biological network, one can either query public databases for known interactions (knowledge-driven approach) or build a mathematical model to measure the associations from data (data-driven approach). In this paper, we propose a new network inference method, integrating knowledge and data-driven approaches. The method integrates prior biological knowledge (i.e., protein-protein interactions from BioGRID database) and a Gaussian graphical model (i.e., graphical LASSO algorithm) to construct robust and biologically relevant network. The network is then utilized to extract differential sub-networks between case and control groups using the result from a statistical analysis (e.g., logistic regression). We applied the proposed method on a proteomic dataset acquired by analysis of sera from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The differential sub-networks led to the identification of hub proteins and key pathways, whose relevance to HCC study has been confirmed by literature survey.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561238","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

ZSeq 2.0: A fully automatic preprocessing method for next generation sequencing data ZSeq 2.0:下一代测序数据的全自动预处理方法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359954

A. Alkhateeb, Iman Rezaeian, L. Rueda

引用次数: 0

HapColor: A graph coloring framework for polyploidy phasing 一个多倍体相位的图形着色框架

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359663

Sepideh Mazrouee, Wei Wang

引用次数: 2

The upregulation of Myb and Peg3 may mediate EGCG inhibition effect on mouse lung adenocarcinoma 上调Myb和Peg3可能介导EGCG对小鼠肺腺癌的抑制作用

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359903

Hong Zhou, Joseph Manthey, E. Lioutikova, Mary Yang, William Yang, K. Yoshigoe, Hong Wang

引用次数: 0

Novel Isoniazid- and Ethionamide-resistance loci in mycobacterium tuberculosis identified by phenome-wide association scans 新型异烟肼和乙硫酰胺耐药位点结核分枝杆菌全现象关联扫描鉴定

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359840

Zhi-Hua Pei, Ting Xie, Qing-Yong Yang, Qing-Ye Zhang, Hong-yu Zhang

{"title":"Novel Isoniazid- and Ethionamide-resistance loci in mycobacterium tuberculosis identified by phenome-wide association scans","authors":"Zhi-Hua Pei, Ting Xie, Qing-Yong Yang, Qing-Ye Zhang, Hong-yu Zhang","doi":"10.1109/BIBM.2015.7359840","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359840","url":null,"abstract":"Due to the wide spread of multidrug-resistant tuberculosis, Mycobacterium tuberculosis (MTB) has once again become a serious public health threat. However, drug resistance mechanisms and the many loci related to drug resistance in MTB currently remain unclear. Similar to genome-wide association studies (GWASs), phenome-wide association scans (PheWAS) is a method for evaluating the association between whole phenotypes and a significant SNP. In this study, we identified S315N, S315T and R463L SNP sites within the katG gene that are related to INH-resistance as well as W191R and G169S SNPs within the katG gene related to ETH-resistance in MTB using the PheWAS method with the available data of Zhang et al. Compared with the GWAS results reported by Zhang et al., R463L and S315N were new sites of the INH prodrug identified by the PheWAS method. Moreover, S266R on gene ethA and A187V variation on mshA are also new sites that were found to be significantly associated with the resistance of ETH by our approach. The loci found by the PheWAS analysis method in this study had been experimentally estimated to be associated with drug resistance in MTB. These findings lend further credence to our PheWAS analysis method in SNP analysis and demonstrate the power of PheWAS in genetic disease loci identification.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761257","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