2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第9页

Accelerating protein-protein complex validation by GPU based funnel generation 基于GPU的漏斗生成加速蛋白质复合物验证

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822510

Michael Zabejansky, H. Wolfson

引用次数: 0

Research on early risk predictive model and discriminative feature selection of cancer based on real-world routine physical examination data 基于真实体检数据的癌症早期风险预测模型及判别特征选择研究

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822746

Guixia Kang, Zhuang Ni

{"title":"Research on early risk predictive model and discriminative feature selection of cancer based on real-world routine physical examination data","authors":"Guixia Kang, Zhuang Ni","doi":"10.1109/BIBM.2016.7822746","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822746","url":null,"abstract":"most cancers at early stages show no obvious symptoms and curative treatment is not an option any more when cancer is diagnosed. Therefore, making accurate predictions for the risk of early cancer has become urgently necessary in the field of medicine. In this paper, our purpose is to fully utilize real-world routine physical examination data to analyze the most discriminative features of cancer based on ReliefF algorithm and generate early risk predictive model of cancer taking advantage of three machine learning (ML) algorithms. We use physical examination data with a return visit followed 1 month later derived from CiMing Health Checkup Center. The ReliefF algorithm selects the top 30 features written as Sub(30) based on weight value from our data collections consisting of 34 features and 2300 candidates. The 4-layer (2 hidden layers) deep neutral network (DNN) based on B-P algorithm, the support machine vector with the linear kernel and decision tree CART are proposed for predicting the risk of cancer by 5-fold cross validation. We implement these criteria such as predictive accuracy, AUC-ROC, sensitivity and specificity to identify the discriminative ability of three proposed method for cancer. The results show that compared with the other two methods, SVM obtains higher AUC and specificity of 0.926 and 95.27%, respectively. The superior predictive accuracy (86%) is achieved by DNN. Moreover, the fuzzy interval of threshold in DNN is proposed and the sensitivity, specificity and accuracy of DNN is 90.20%, 94.22% and 93.22%, respectively, using the revised threshold interval. The research indicates that the application of ML methods together with risk feature selection based on real-world routine physical examination data is meaningful and promising in the area of cancer prediction.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133780512","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: 5

GIDAC: A prototype for bioimages annotation and clinical data integration GIDAC:生物图像注释和临床数据整合的原型

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822663

P. Vizza, P. Guzzi, P. Veltri, G. Cascini, R. Curia, Loredana Sisca

{"title":"GIDAC: A prototype for bioimages annotation and clinical data integration","authors":"P. Vizza, P. Guzzi, P. Veltri, G. Cascini, R. Curia, Loredana Sisca","doi":"10.1109/BIBM.2016.7822663","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822663","url":null,"abstract":"The analysis of bioimages and their correlated clinical patient information allows to investigate specific diseases and define the corresponding medical protocols. To perform a correct diagnosis and apply a precise therapy, bioimages must be collected and studied together with others relevant data as well as laboratory results, medical annotations and patient history. Today, the management of these data is performed by single systems inside hospital departments that often do not provide dedicated data integration platforms among different departments as well as different health structures to exchange of relevant clinical information. Also, images cannot be annotated or enriched by physicians to trace temporal studies for patients or even among patients with similar diseases. In this contribution, we report the results of a research project called GIDAC (standing for Gestione Integrata DAti Clinici) that aims to define a general purpose framework for the bioimages management and annotations as well as clinical data view and integration in a simple-to-use information system. The proposed framework does not substitute any existing clinical information system but is able in gathering and integrating data by using a XML-based module. The novelty also consists in allowing annotations on DICOM images by means of simple user-interface to take trace of changes intra images as well as comparisons among patients. This system supports oncologists in the management of DICOM images from different devices (e.g., ecograph or PACS) to extract relevant information necessary to query (annotate) images and study similar clinical cases.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232367","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: 12

Evaluation of CD-HIT for constructing non-redundant databases 构建非冗余数据库的CD-HIT评价

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822604

Qingyu Chen, Yu Wan, Yang Lei, J. Zobel, Karin M. Verspoor

引用次数: 12

A novel identified temporal protein complexes strategy inspired by density-distance and brainstorming process 由密度距离和头脑风暴过程启发的一种新的识别时间蛋白复合物策略

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822701

Xianjun Shen, Jin Zhou, Xingpeng Jiang, Xiaohua Hu, Tingting He, Jincai Yang, Dan Xie

{"title":"A novel identified temporal protein complexes strategy inspired by density-distance and brainstorming process","authors":"Xianjun Shen, Jin Zhou, Xingpeng Jiang, Xiaohua Hu, Tingting He, Jincai Yang, Dan Xie","doi":"10.1109/BIBM.2016.7822701","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822701","url":null,"abstract":"Detection of protein complexes and functional modules plays a crucial role for strengthening the comprehension of cellular organization and biological functions on the dynamic protein-protein interaction network. In this article, we put forward a new strategy to identify temporal protein complexes. Integrating time-course gene expression data into static protein interaction data, a series of time-sequenced subnetworks were constructed. Then we combined the network topology and gene ontology information for defining the distance between proteins in PPI network. A novel method to find the cluster centers and then form initial clusters was based on the idea that cluster centers are usually recognized as nodes with higher densities than their neighbors and with a relatively larger distance from other cluster centers. Finally, inspired by the brainstorming discussion process, two ways are introduced to update the initial clusters for achieving the optimal results. After the filtering and merging procedure, experimental results demonstrated that the proposed strategy had a good performance comparing with the other four advanced algorithms - MCODE, FAG-EC, HC-PIN, and CNC.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132928378","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-norm constrained optimization methods for calling copy number variants in single cell sequencing data 单细胞测序数据中拷贝数变量调用的多规范约束优化方法

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822511

Changsheng Zhang, Hongmin Cai, Jingying Huang, Bo Xu

{"title":"Multi-norm constrained optimization methods for calling copy number variants in single cell sequencing data","authors":"Changsheng Zhang, Hongmin Cai, Jingying Huang, Bo Xu","doi":"10.1109/BIBM.2016.7822511","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822511","url":null,"abstract":"The revolutionary invention of single-cell sequencing technology carves out a new way to delineate intra tumor heterogeneity and the evolution of single cells at the molecular level. Since single-cell sequencing requires a special genome amplification step to accumulate enough samples, a large number of bias were introduced, making the calling of copy number variants rather challenging. Accurately modeling this process and effectively detecting copy number variations (CNVs) are the major roadblock for single-cell sequencing data analysis. Recent advances manifested that the underlying copy numbers are corrupted by noise, which could be approximated by negative binomial distribution. In this paper, we formulated a general mathematical model for copy number reconstruction from read depth signal, and presented its two specific variants, namely Poisson-CNV and NB-CNV to catering for various reads distribution. Efficient numerical solution based on the classical alternating direction minimization method was designed to solve the proposed models. Extensive experiments on both synthetic datasets and empirical single-cell sequencing datasets were conducted to compare the performance of the two models. The results show that the proposed model of NB-CNV achieved superior performance in calling the CNV for single-cell sequencing data.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116640622","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

Unconstrained optimization in projection method for indefinite SVMs 不定支持向量机投影法的无约束优化

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822585

Hao Jiang, W. Ching, Yushan Qiu, Xiaoqing Cheng

引用次数: 0

A representational analysis of a temporal indeterminancy display in clinical events 临床事件中时间不确定性表现的代表性分析

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822673

M. Madkour, Hsing-yi Song, Jingcheng Du, Cui Tao

引用次数: 1

Visual orchestration and autonomous execution of distributed and heterogeneous computational biology pipelines 分布式和异构计算生物学管道的可视化编排和自主执行

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822615

Xin Mou, H. Jamil, R. Rinker

引用次数: 4

Wide line detection with water flow 宽线检测水流

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2016-12-01 DOI: 10.1109/BIBM.2016.7822715

Yangyang Hu, Wenqiang Zhang, Hong Lu, Fufeng Li, Weifei Zhang

引用次数: 1