2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第2页

Protein Structure Recognition by Means of Sequential Pattern Mining 基于序列模式挖掘的蛋白质结构识别

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-32

Anna N. Ntagiou, M. Tsipouras, N. Giannakeas, A. Tzallas

引用次数: 2

Automated Microaneurysm Detection in Fundus Images through Region Growing 基于区域生长的眼底图像微动脉瘤自动检测

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-67

Lin Li, J. Shan

{"title":"Automated Microaneurysm Detection in Fundus Images through Region Growing","authors":"Lin Li, J. Shan","doi":"10.1109/BIBE.2017.00-67","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-67","url":null,"abstract":"Diabetic retinopathy (DR) is the leading cause of blindness if not detected and treated in time and is a serious complication of diabetes. Since DR is a progressive eye disease, the early detection and diagnosis of DR is important to prevent patients from blindness. One of the most characteristic symptoms of DR is the presence of microaneurysm (MA) – the early sign of DR, which is hard to detect manually due to its small size. In this paper, we propose an automatic MA detection method based on region growing and region classification. We solve two problems: 1) given a fundus image, how to automatically partition the image into regions that may or may not contain MAs through a region growing approach, and 2) given a region in a fundus image, how to automatically evaluate whether this region contains MA by feeding the features of the region into an artificial neural network (ANN) for classification. The proposed approach involves image preprocessing, region growing, feature selection and classification steps. In the experiment, the public dataset DIAbetic RETinopathy DataBase 1 (DIARETDB1) is used to provide training/testing data and ground truth. The proposed method can achieve the performance with sensitivity 86.6%, specificity 96.3%, and accuracy 93.9%, for automatic MA detection.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125844870","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: 1

Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients using MRI Images 使用MRI图像预测脑肿瘤患者总体生存的机器学习和深度学习技术

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-86

Lina Chato, S. Latifi

引用次数: 55

Predictive Genome Analysis Using Partial DNA Sequencing Data 使用部分DNA测序数据的预测性基因组分析

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-68

Nauman Ahmed, K. Bertels, Z. Al-Ars

{"title":"Predictive Genome Analysis Using Partial DNA Sequencing Data","authors":"Nauman Ahmed, K. Bertels, Z. Al-Ars","doi":"10.1109/BIBE.2017.00-68","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-68","url":null,"abstract":"Much research has been dedicated to reducing the computational time associated with the analysis of genome data, which resulted in shifting the bottleneck from the time needed for the computational analysis part to the actual time needed for sequencing of DNA information. DNA sequencing is a time consuming process, and all existing DNA analysis methods have to wait for the DNA sequencing to completely finish before starting the analysis. In this paper, we propose a new DNA analysis approach where we start the genome analysis before the DNA sequencing is completely finished. The genome analysis is started when the DNA reads are still in the process of being sequenced. We use algorithms to predict the unknown bases and their corresponding base quality scores of the incomplete read. Results show that our method of predicting the unknown bases and quality scores achieves more than 90% similarity with the full dataset for 50 unknown bases (slashing more than a day of sequencing time). We also show that our base quality value prediction scheme is highly accurate, only reducing the similarity of the detected variants by 0.45%. However, there is still room to introduce more accurate prediction schemes for the unknown bases to increase the effectiveness of the analysis by up to 5.8%.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708264","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

Pixel-ECC Based Laparoscopic Image Alignment In Presence of Large Untextured Regions 基于像素- ecc的大范围无纹理区域腹腔镜图像对齐

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-50

Nefeli Lamprinou, E. Psarakis

引用次数: 0

Performance Comparison of HSV and L*a*b* Spaces in Thought Form Image Analysis HSV与L*a*b*空间在思维形态图像分析中的性能比较

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-36

R. S. Prasad

{"title":"Performance Comparison of HSV and L*a*b* Spaces in Thought Form Image Analysis","authors":"R. S. Prasad","doi":"10.1109/BIBE.2017.00-36","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-36","url":null,"abstract":"Nature and behavior of human beings are reflected in their Thought-forms which are inseparable part of human biofield. Emission of Biophotons from human body has been known to be ultraweak. Recent research on biophotons capturing techniques have yielded a two dimensional imaging system–a marked improvement over only detection and measurement possibility till recent past. With continuing progress in biophotonic research, prospects of developing an imaging system worthy of capturing thoughts in image form, are no longer outside the realm of possibility. The physiological structure of human beings, from the view point of Biophysicists and Theosophists, has a great deal in common since both talk of a cloud of electromagnetic field in a spectrum of colors over a physical body. A literature survey resulted in locating a large number of hand-drawn, hand-painted true color images of thought forms in Theosophical Society literature. These images were published nearly hundred years ago bearing comments of ‘Good or Bad, or a mix of both Good and Bad’. The basis on which the comments were attributed was stated to have followed the three principles of â€˜(i) Quality of thought determines color, (ii) Nature of thought determines form, and (iii) Definiteness of thought determines clearness of outline. Motivated by this background, this paper is devoted not only to verify the truth of comments, but also to explore the possibility of applications of the developed procedure in other areas of social life. We use L*a*b* color space to identify the patterns of Good or Bad in thought form images, and compare its performance with the HSV space which authors recently reported. It is shown that L*a*b* space outperforms HSV space.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131327982","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: 1

A Medical Diagnosis Method Based on Interval-valued Fuzzy Cognitive Map 基于区间值模糊认知图的医学诊断方法

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-20

Li Li, Runtong Zhang, Jun Wang

引用次数: 4

Coupled Computer Modeling of Atherosclerosis Development in the Coronary Arteries 冠状动脉粥样硬化发展的耦合计算机模拟

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-19

V. Isailović, Z. Milosevic, D. Nikolić, I. Šaveljić, Milica G. Nikolić, M. Gacic, Bojana R. Cirkovic-Andjelkovic, T. Exarchos, D. Fotiadis, G. Pelosi, O. Parodi, N. Filipovic

引用次数: 1

SynAPhy and SynAVal: Mining a Synteny-Similarity Graph to Resolve Orthology of Proteins in Fungal Genomes synsyny和SynAVal:挖掘一个Synteny-Similarity图来解析真菌基因组中蛋白质的同源性

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-30

Christine Kehyayan, G. Butler

{"title":"SynAPhy and SynAVal: Mining a Synteny-Similarity Graph to Resolve Orthology of Proteins in Fungal Genomes","authors":"Christine Kehyayan, G. Butler","doi":"10.1109/BIBE.2017.00-30","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-30","url":null,"abstract":"Phylogenomics is the study of evolution of proteins and the genomic events of speciation, duplication, horizontal gene transfer, and gene loss. It is critical to distinguish between orthologs created by speciation, and paralogs created by duplication, in order to accurately predict the function of a protein using annotation transfer by homology. In an age where complete genomes are available, we leverage synteny, the genomic context of a gene, for resolving orthology. We introduce the synteny-similarity graph. We present SynAPhy, a novel graph-based approach for clustering proteins. SynAPhy computes the “syntenic reciprocal best hits” of proteins across genomes. The synteny-similarity graphs are input to the MCL algorithm to determine orthologous clusters across genomes. There is no gold standard genome scale dataset to evaluate the capability of SynAPhy in generating orthologous clusters. We therefore present SynAVal, an evaluation framework that can be applied to an orthology prediction technique. The results of applying SynAVal on eight fungal genomes show that SynAVal with synteny resolution can successfully resolve potential confusions raised by 8.98% of all proteins, and resolve 23.33% of the subset of the proteins likely to cause confusions.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122293693","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

Studies of a Hybrid and Multiscale Tumor Growth Model via Isogeometric Analysis using PetIGA 基于等几何分析的混合多尺度肿瘤生长模型的研究

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-76

Paulo Wander Barbosa, Adriano M. de A. Cirtes, L. Catabriga

引用次数: 1