2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第2页

TMDFM: A data fusion model for combined detection of tumor markers TMDFM:用于肿瘤标志物联合检测的数据融合模型

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359763

Chi Yuan, Yongli Wang, Yanchao Li

引用次数: 0

Predicting microbial interactions by using network-constrained regularization incorporating covariate coefficients and connection signs 利用包含协变量系数和连接符号的网络约束正则化预测微生物相互作用

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359758

Yan Wang, Xiaohua Hu, Xingpeng Jiang, Tingting He, Jie Yuan

{"title":"Predicting microbial interactions by using network-constrained regularization incorporating covariate coefficients and connection signs","authors":"Yan Wang, Xiaohua Hu, Xingpeng Jiang, Tingting He, Jie Yuan","doi":"10.1109/BIBM.2015.7359758","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359758","url":null,"abstract":"Network is an exceptional way of depicting biological information. In biology, many different biological processes are represented by network, such as regulatory network, metabolic network and food web. In biology, network is a powerful supplement to the standard numerical data such as profile or count data. By absorbing network information, Vector autoregressive (VAR) model was proved to be an efficient approach to infer dynamic interactions in biological systems. Variants of network-regularized VAR with different penalties or regularization can avoid the problem of over-fitting and provide great potential in high-dimensional time series analysis. In this paper, we develop a novel regularization method for multivariate VAR which incorporates not only network topology but the signs of the network connections. By virtue of coordinate descent, we present a fast implementation for estimating model parameters. We then apply the proposed approach on several time series data sets especially a time series dataset of human gut microbiomes. The experimental results indicate that the new approach has better performance than other VAR-based models.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"19 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":"129885244","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 question-answering system over Traditional Chinese Medicine 中医问答系统

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359945

Xiangzhou Huang, Yin Zhang, Baogang Wei, Liang Yao

{"title":"A question-answering system over Traditional Chinese Medicine","authors":"Xiangzhou Huang, Yin Zhang, Baogang Wei, Liang Yao","doi":"10.1109/BIBM.2015.7359945","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359945","url":null,"abstract":"Traditional Chinese Medicine (TCM) has been around for thousands of years and it's a significant part of Chinese cultural heritage. The theoretical framework of TCM is unique and with rich of content, which contains the complex relationships between disease and medicine. Research on question-answering (QA) over TCM is significant for Chinese NLP and representative, because the resources of TCM are mostly Chinese-based. In this paper we present a QA system over TCM, which transforms user supplied questions into conjunctive query sentences (i.e. SQL) and retrieves the answer from both the built-up dataset and online encyclopedia. The contribution of this paper is threefold: Firstly, we introduce a novel approach for word segmentation over Chinese questions. We employ a TF-IDF model on the dataset to generate domain-specific dictionary with weight factor and tags, which are computed to select the best result of segmentation. Secondly, we present a novel method for constructing queries to retrieve answers. We compute the entity-attribute distance over a set of tagged words to construct incomplete ontology instances, which are used as the intermediary to generate queries. Lastly, we propose a method to integrate web data extraction with question answering, which allows us to extract answers from online encyclopedia website (i.e. Wikipedia). The results of our evaluation with 50 benchmark queries demonstrate the value of our approach.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"84 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":"123621364","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

The Medical Knowledge Cockpit: Real-time analysis of big medical data enabling precision medicine 医疗知识驾驶舱:实时分析大医疗数据，实现精准医疗

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359783

M. Schapranow, Milena Kraus, Cindy Perscheid, Cornelius Bock, Franz Liedke, H. Plattner

引用次数: 5

Patho-finder — A fast and accurate program for pathogen identification through RNA-seq Patho-finder -通过RNA-seq快速准确的病原体鉴定程序

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359843

Chin-Ting Wu, T. Hsiao, Yu-Chiao Chiu, Yu-Ching Hsu, E. Chuang, Yidong Chen

引用次数: 0

Resting state functional connectivity explains individual scores of multiple clinical measures for major depression 静息状态功能连通性解释了重性抑郁症多项临床指标的个体得分

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359831

Kosuke Yoshida, Yu Shimizu, J. Yoshimoto, Shigeru Toki, G. Okada, M. Takamura, Y. Okamoto, S. Yamawaki, K. Doya

{"title":"Resting state functional connectivity explains individual scores of multiple clinical measures for major depression","authors":"Kosuke Yoshida, Yu Shimizu, J. Yoshimoto, Shigeru Toki, G. Okada, M. Takamura, Y. Okamoto, S. Yamawaki, K. Doya","doi":"10.1109/BIBM.2015.7359831","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359831","url":null,"abstract":"Recent studies have revealed that resting state functional connectivity is associated with major depressive disorder (MDD). However, the relationship between functional connectivity and clinical measures for the detailed assessment of depression remains unclear. The objective of our study is thus to associate functional connectivity of depressed patients and healthy controls with their individual clinical measures, using a statistical method called partial least squares analysis (PLS). We demonstrated that this method could predict certain clinical measures based on a limited number of functional connections and provided benefits to the prediction performance through incorporation of the subject's age and the estimation of multiple measures simultaneously. Generalizability of the prediction model was assured through leave one out cross validation. The results showed that for BDI-II and SHAPS the most contributing connections concerned cuneus, precuneus and middle frontal cortex and areas of the cerebellum. While the relationship was similar for PANAS(n), it showed its strongest relation with functional connection between calcarine and insula.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"5 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":"121802326","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

Predicting microRNA-disease associations by integrating multiple biological information 通过整合多种生物信息预测microrna与疾病的关联

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359678

Wei Lan, Jianxin Wang, Min Li, Jin Liu, Yi Pan

引用次数: 20

A novel signature for identification of upstream alternative translation initiation sites 一种识别上游替代翻译起始位点的新特征

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359850

Kritika Karri, Dhundy Bastola

{"title":"A novel signature for identification of upstream alternative translation initiation sites","authors":"Kritika Karri, Dhundy Bastola","doi":"10.1109/BIBM.2015.7359850","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359850","url":null,"abstract":"Alternative translation initiation sites (aTIS) and the factors that determine an ideal mode of translation of mRNA in the eukaryotic system has been widely studied over the past several decades. Such studies demonstrate that single mRNA can yield multiple protein isoforms using the AUG and non-AUG start codons. While the conspicuous scanning model explains how the process of translation initiation begins when the pre-initiation complex encounters a start codon with optimal sequence context, referred to as “Kozak” consensus and the leaky scanning model explains the process of alternative translation downstream. Additionally, there are alternative translation initiation sites upstream and in-frame to the canonical AUG start site, which are present in the 5' UTR of the mRNA. This choice of aTIS results in a longer N-terminally expanded protein isoform. It is unclear as to what are the factors that determine the selection of a start codon to be a potential upstream aTIS. In this study we investigate if there exists a third element, besides the sequence context at a certain distance from canonical AUG start codon, in the 5' UTR of human mRNAs called the “complementary matching palindrome” that differentiates an upstream aTIS from any other codon. Our results show the presence of longest complementary matching palindrome around CUG codon, which may serve as an aTIS to translate proteins that are longer than their canonical isoforms.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"8 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":"131521098","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

Assessment of gait patterns of chronic low back pain patients: A smart mobile phone based approach 慢性腰痛患者的步态模式评估:基于智能手机的方法

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359823

Herman Chan, Huiru Zheng, Haiying Wang, D. Newell

引用次数: 3

Prediction of radioprotectors targeting p53 for suppression of acute effect of cancer radiotherapy using machine learning 利用机器学习预测靶向p53的放射保护剂对癌症放疗急性效应的抑制

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2015-11-09 DOI: 10.1109/BIBM.2015.7359941

Atsushi Matsumoto, T. Ito, Yurie Nishi, Tatsuro Teraoka, S. Aoki, H. Ohwada

{"title":"Prediction of radioprotectors targeting p53 for suppression of acute effect of cancer radiotherapy using machine learning","authors":"Atsushi Matsumoto, T. Ito, Yurie Nishi, Tatsuro Teraoka, S. Aoki, H. Ohwada","doi":"10.1109/BIBM.2015.7359941","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359941","url":null,"abstract":"Radiation therapy and some chemotherapeutic agents mainly target the DNA of growing cancer cells, whereas these therapies have adverse side effects, including p53-induced apoptosis of normal tissues and cells. It is considered that p53 would be a target for therapeutic and mitigative radioprotection to escape from the apoptotic fate. So far, only three radioprotective p53 inhibitors have been reported, namely, pifithrin-α (PFTα), pifithrin-μ (PFTμ), and sodium orthovanadate (vanadate), which protect mice from acute lethality due to hematopoietic syndrome, indicating that pharmacologically temporary suppression of p53 effectively minimize the radiation damage. In this study, we examined the inhibitory activity of some zinc(II) chelators against radiation-induced apoptosis of MOLT-4 cells, based on the assumption that the binding of these compounds to zinc(II) in p53 proteins or removal of zinc(II) from the protein would temporally inhibit the function of p53. However, we have had some problems. The development of drug has been slow, due to the time required and the high cost of screening candidate compounds. It is possible to efficiently search for drugs by using machine learning. So we predict compounds that radioprotectors using Random Forest to study compound futures and using other machine learning methods for comparison with Random Forest. Procedure of learning is as follows: First, compounds were divided into several groups based on the toxicity and protection capability. Next, it was performed classification using machine learning. These results may contribute to discover of new radioprotectors.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"16 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":"132435013","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