2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第6页

A domain ontology for the Non-Coding RNA field 非编码RNA领域的领域本体

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359755

Jingshan Huang, K. Eilbeck, J. Blake, D. Dou, D. Natale, A. Ruttenberg, Barry Smith, Michael T. Zimmermann, Guoqian Jiang, Yu Lin, Bin Wu, Y. He, Shaojie Zhang, Xiaowei Wang, He Zhang, Zixing Liu, M. Tan

引用次数: 2

A seeding-searching-ensemble method for gland segmentation and detection 一种用于腺体分割和检测的种子搜索集成方法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359707

Yizhe Zhang, L. Yang, J. MacKenzie, R. Ramachandran, D. Chen

引用次数: 0

A path based approach to quantifying the progression of Alzheimer's disease 基于路径的方法量化阿尔茨海默病的进展

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359826

Prabesh Kanel, Xiuwen Liu

{"title":"A path based approach to quantifying the progression of Alzheimer's disease","authors":"Prabesh Kanel, Xiuwen Liu","doi":"10.1109/BIBM.2015.7359826","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359826","url":null,"abstract":"Histological studies suggest that different pathological changes occur in the subfields of hippocampi in aging and in Alzheimer0s disease. The pathological changes due to Alzheimer's disease follows a neural system and are consistent with subjects. The aim of this study is to study the path in vivo the changes in normal control, mild cognitive impairment and Alzheimers disease patients using high resolution MRI scanned at 3 Tesla. T1 weighted images were obtained from the ADNI database. The dataset consists of 11 control patients, 13 MCI patients, and 9 AD patients. The hippocampal subfields were segmented using the Freesurfer image-analysis suite (Version 6.0). The volume of the subfields and the path along the medial axis of the subfields were studied. The results suggest that the intensity along the medial axis shows more variation on the CA1 region than other subfields in cases of Alzheimer disease, MCI and normal control patients. The changes are more prominent in the early section of the CA1 suggesting the progressive nature of the disease. The mean intensity value along the medial axis of CA1 subfield shows change in AD, MCI and NC but similar change are not seen in other subfields.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"67 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":"125560697","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

The syndromes of lung cancer and compatibility of medicine in Traditional Chinese Medicine science treatment based on Clustering Algorithm 基于聚类算法的中医治疗肺癌证候与药物配伍

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359830

Miao Wang, Mengying Wang, Dongyi Wang, S. Duan, Yisheng Wang, Yanjun Huang, Huiliang Shang

引用次数: 2

Structuring unstructured clinical narratives in OpenMRS with medical concept extraction 利用医学概念提取在OpenMRS中构建非结构化临床叙述

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359782

R. Eshleman, Hui Yang, Barry Levine

{"title":"Structuring unstructured clinical narratives in OpenMRS with medical concept extraction","authors":"R. Eshleman, Hui Yang, Barry Levine","doi":"10.1109/BIBM.2015.7359782","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359782","url":null,"abstract":"We have developed a new software module for the open source Electronic Medical Record System OpenMRS to analyze unstructured clinical narratives. This module leverages Named Entity Recognition (NER) to deliver concise, semantic-type driven, interactive summaries of clinical notes. To this end, we performed an extensive empirical evaluation of four Named Entity Recognition (NER) systems using textual clinical narratives and full biomedical journal articles. The four NER systems under evaluation are MetaMap, cTAKES, BANNER. We studied several ensemble approaches built upon the above four NER systems to exploit their collaborative strengths. Evaluations are performed on the manually annotated patient discharge summaries from the Informatics for Integrating Biology and the Bedside group (I2B2) and the CRAFT dataset. The main results include (1) BANNER significantly outperforms the other three systems on the I2B2 dataset with F1 values in the range of .73-.89, in contrast to .28 - .60 of other systems; and (2) Surprisingly, an ensemble approach of BANNER with any combinations of the other three approaches tends to degrade the performance by .08 - .11 in F1 when evaluated on the I2B2 dataset. Based on the evaluation results, we have developed a BANNER-based NER module for OpenMRS to recognize semantic concepts including problems, tests, and treatments. This module works with OpenMRS versions 2.×. The user interface presents concise clinical notes summaries and allows the user to filter, search and view the context of the concepts. We have also developed a companion web application to retrain the BANNER model using data from OpenMRS. The module and source code are available at wiki.openmrs.org/display/docs/Visit+Notes+Analysis+Module.","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":"125834664","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: 4

ENISI multiscale modeling of mucosal immune responses driven by high performance computing 高性能计算驱动的ENISI粘膜免疫反应多尺度建模

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359768

V. Abedi, R. Hontecillas, S. Hoops, Nathan Liles, Adria Carbo, Pinyi Lu, C. Philipson, J. Bassaganya-Riera

引用次数: 6

Comparative study of Middle East respiratory syndrome coronavirus using bioinformatics techniques 利用生物信息学技术对中东呼吸综合征冠状病毒的比较研究

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359937

Insung Ahn, Jinhwa Jang

{"title":"Comparative study of Middle East respiratory syndrome coronavirus using bioinformatics techniques","authors":"Insung Ahn, Jinhwa Jang","doi":"10.1109/BIBM.2015.7359937","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359937","url":null,"abstract":"In this study, we collected 141 coding sequences of MERS-CoV from the National Center for Biotechnology and Information (NCBI), including the sequences isolated in Korea in 2015. We conducted the phylogenetic analysis using the Maximum Likelihood method to examine the overall variation patterns among the target sequences, and then, we divided the target sequences into 4 different groups according to occurred countries and host species. Using the codon analyzer named SimFluVar program, we analyzed the codon variation patterns in the wobble position among 4 groups. In order to investigate the effect of codon variations that can change the phenotype of target genes, we compared the transversional substitutions between the Korean-origin sequences and other groups. As a result, we found that the Korea-origin sequences showed very minor differences with those collected from the Saudi Arabia in 2015, whereas other groups which were collected from USA and UK in 2013 and 2014 showed more complicated differences. We also compared the Korea-origin sequences with those of camel-origin sequences, and we found that the substitution pattern was somewhat different with that of human-origin viruses.","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":"130166346","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: 4

Parallel group ICA for multimodal biomedical data analyses 用于多模态生物医学数据分析的平行组ICA

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359832

Jingyu Liu, Jiayu Chen, V. Calhoun

引用次数: 0

Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis 基于改进SFLA的高维生物医学数据特征选择用于疾病诊断

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359728

Yongqiang Dai, Bin Hu, Yun Su, Chengsheng Mao, Jing Chen, Xiaowei Zhang, P. Moore, Lixin Xu, Hanshu Cai

{"title":"Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis","authors":"Yongqiang Dai, Bin Hu, Yun Su, Chengsheng Mao, Jing Chen, Xiaowei Zhang, P. Moore, Lixin Xu, Hanshu Cai","doi":"10.1109/BIBM.2015.7359728","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359728","url":null,"abstract":"High-dimensional biomedical datasets contain thousands of features used in molecular disease diagnosis, however many irrelevant or weak correlation features influence the predictive accuracy. Feature selection algorithms enable classification techniques to accurately identify patterns in the features and find a feature subset from an original set of features without reducing the predictive classification accuracy while reducing the computational overhead in data mining. In this paper we present an improved shuffled frog leaping algorithm (ISFLA) which explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. Evaluation employs the K-nearest neighbour approach and a comparative analysis with a genetic algorithm, particle swarm optimization and the shuffled frog leaping algorithm shows that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"33 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":"121851369","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: 16

SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions SRP:用于预测药物-靶标相互作用的简洁非参数相似性排序模型

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359921

Jianyu Shi, S. Yiu

{"title":"SRP: A concise non-parametric similarity-rank-based model for predicting drug-target interactions","authors":"Jianyu Shi, S. Yiu","doi":"10.1109/BIBM.2015.7359921","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359921","url":null,"abstract":"The identification of drug-target interactions in web lab is costly and time-consuming. Computational approaches become important to help identifying potential candidates for laboratory experiments. However, they usually involve solving optimization problems or assuming statistical distribution based on prior knowledge, and may require estimating tunable parameters. This paper is motivated by the concepts behind “follow-on” drugs. They are the drugs developed by drug companies to substitute the pioneering drug which was firstly discovered and patented for a specific target and determined a new therapeutic class. There are three observations from “follow-on” drugs. The first observation has been used by many existing methods: drugs interacting with a common target usually have higher similar scores (e.g. the similarity score in terms of chemical structure). The second one is that a drug candidate for a specific target gains more attention if it is more similar to those drugs interacting with the target than other known drugs, even though the similarity score is low. Lastly, people intuitively tend to design a “follow-on” drug for the targets already having more drugs because of less cost and less risk. In our approach, the above observations are translated into more evidences for predicted drug-target interaction. Designing an interaction tendency index to characterize these observations, we propose the similarity-rank-based predictor (SRP). Unlike other models, SRP is a non-parametric model and requires neither solving an optimization problem nor prior statistical knowledge. Based on real benchmark datasets, we show that our model is able to achieve higher accuracy than the two most recent models and our approach is able to cope with two real predicting scenario of missing interactions.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"17 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":"121137291","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: 14