2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献

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SVM Classification Model of Similar Bacteria Species using Negative Marker: Based on Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry 基于基质辅助激光解吸/电离飞行时间质谱的相似菌种阴性标记SVM分类模型

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-07-01 DOI: 10.1109/BIBE.2017.00-64

Jongseo Lee, Yoonsu Shin, Songkuk Kim, Kyoohyoung Rho, Kyu H. Park

{"title":"SVM Classification Model of Similar Bacteria Species using Negative Marker: Based on Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry","authors":"Jongseo Lee, Yoonsu Shin, Songkuk Kim, Kyoohyoung Rho, Kyu H. Park","doi":"10.1109/BIBE.2017.00-64","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-64","url":null,"abstract":"MALDI-TOF mass spectrometry has high social and economic value in rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Numerous studies have been conducted to identify microorganisms using MALDI-TOF MS. Markers are characteristics that can be used to uniquely distinguish microorganisms. Microorganisms can be identified by applying markers selected based on the extracted mass information. Previous studies demonstrated that combining mass information extracted by MALDI-TOF MS with machine-learning techniques can improve microorganism classification. Classification of microorganisms is particularly difficult and critical for mycobacteria because various pathogens should be treated with different prescriptions, although they exhibit similar compositions. It is quite challenging to accurately identify mycobacteria using conventional methods because their MALDI-TOF MS patterns are similar to each other. In this study, we propose a support vector machine model for improving the distinction of similar species by learning positive and negative markers separately extracted in each group. We classified species in the Mycobacterium abscessus group and Mycobacterium fortuitum group. Our novel approach applies negative markers to classify similar species and improves the identification of similar species using a combination of positive and negative markers.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075822","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

Identification of Cerebellar Dysarthria with SISO Characterisation 小脑构音障碍的SISO特征鉴别

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-07-01 DOI: 10.1109/BIBE.2017.000-8

Bipasha Kashyap, D. Szmulewicz, P. Pathirana, M. Horne, L. Power

引用次数: 4

A Neuroimaging Feature Extraction Model for Imaging Genetics with Application to Alzheimer's Disease 一种用于成像遗传学的神经影像学特征提取模型及其在阿尔茨海默病中的应用

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-07-01 DOI: 10.1109/BIBE.2017.00-85

Chunfei Li, Chen Fang, M. Adjouadi, M. Cabrerizo, A. Barreto, J. Andrian, R. Duara, D. Loewenstein

{"title":"A Neuroimaging Feature Extraction Model for Imaging Genetics with Application to Alzheimer's Disease","authors":"Chunfei Li, Chen Fang, M. Adjouadi, M. Cabrerizo, A. Barreto, J. Andrian, R. Duara, D. Loewenstein","doi":"10.1109/BIBE.2017.00-85","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-85","url":null,"abstract":"Neuroimaging is an important research platform that can be very useful for eliciting new understanding on the complicated pathogenesis between genetics and disease phenotypes. Due to the extremely high dimensionality of image and genetic data, and considering the potential joint effect of genetic variants, multivariate techniques have been examined to detect Alzheimers disease (AD) related genetic variants expressed through single-nucleotide polymorphisms (SNPs). However, the image features used in support of those methods are not immediately related to the disease, and the detected genetic markers may not be related to AD. In this study, we propose an ensemble model based framework for firstly extracting 50 region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. This task is followed by performing sparse Partial Least Squares regression (sPLS) method on the extracted 50 AD related image features and pre-selected 1508 SNPs to detect the significant SNPs associated with the extracted image features. Instead of modeling a direct link between genetic variants and disease label, we captured disease information indirectly.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128150897","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

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