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

Global Laparoscopy Positioning System with a Smart Trocar 带智能套管针的全球腹腔镜定位系统

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-23 DOI: 10.1109/BIBE.2017.00-28

G. Joerger, Albert Y Huang, B. Bass, B. Dunkin, M. Garbey

{"title":"Global Laparoscopy Positioning System with a Smart Trocar","authors":"G. Joerger, Albert Y Huang, B. Bass, B. Dunkin, M. Garbey","doi":"10.1109/BIBE.2017.00-28","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-28","url":null,"abstract":"Purpose: in the context of minimally invasive surgery, navigation in the patients abdomen is crucial, but is also more complicated than during open procedures. While the advantages for the patient are numerous, surgeons may not always know where they are in this complex environment, and getting used to this technique takes a lot of practice. Method: we present a novel smart trocar device that provides a global positioning system for the laparoscopic instruments that are inserted through the trocars. We augment current trocars with a camera pointing towards the ceiling. At any point in time, and by using computer vision as well as projective geometry, our smart trocar can provide the position of rigid instruments. Results: with our system, we reach one-millimeter accuracy on translation and half a degree on rotation with the only limitation being the velocity of surgeons movements. But this limitation has not been an obstacle in real practice. Conclusion: the trocar can track specific landmarks found on the ceiling of the OR, and, from these, accurately determine its global position in the OR in real time. Applications of this invention are first, assessment and/or training of surgical skills second, once our system is coupled to a real-time three-dimensional simulator of patient anatomy, we envision that our system can automatically provide surgical guidance during the procedure.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131908193","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

Augmenting a Wireless Portable Ultrasound Imaging with a real-time Hemodynamics Solver 增强无线便携式超声成像与实时血流动力学求解器

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-23 DOI: 10.1109/BIBE.2017.00-51

A. Lesage, M. Garbey

引用次数: 0

One-class Differential Expression Analysis using Tensor Decomposition-based Unsupervised Feature Extraction Applied to Integrated Analysis of Multiple Omics Data from 26 Lung Adenocarcinoma Cell Lines 基于张量分解的无监督特征提取的一类差异表达分析应用于26个肺腺癌细胞系多组学数据的综合分析

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-66

Y-h. Taguchi

{"title":"One-class Differential Expression Analysis using Tensor Decomposition-based Unsupervised Feature Extraction Applied to Integrated Analysis of Multiple Omics Data from 26 Lung Adenocarcinoma Cell Lines","authors":"Y-h. Taguchi","doi":"10.1109/BIBE.2017.00-66","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-66","url":null,"abstract":"Because usually there are no normal control cell lines, cancer cell lines can be examined only in a comparison between treatment and no-treatment conditions. Thus, characterization of cancer cell lines by themselves is impossible. To address this problem, one-class differential expression (DE) analysis, which can evaluate samples without a reference, is proposed here using tensor decomposition (TD)-based unsupervised feature extraction (FE) extended from recently proposed principal component analysis-based unsupervised FE. This one-class DE analysis was applied to multi-omics datasets of 26 lung adenocarcinoma cell lines. Enrichment analysis of selected genes identified multiple biological terms or concepts including signal recognition particles and nonsense-mediated decay (Reactome, Gene Ontology [GO] biological process), cadherin, poly(A) RNA binding (GO molecular function), eukaryotic translation initiation factors (Reactome), aberrant histone protein expression (Reactome and Human Protein Atlas [HPA]), and 163 transcription factors including E2F, PAX5, ARNT, AHR, and CREB, all of which are known to be related to non-small cell lung cancer and are expected to function cooperatively in lung adenocarcinoma oncogenesis. ,,,, These data not only indicate usefulness of one-class DE analysis using TD-based unsupervised FE but also point to new therapeutic targets in lung adenocarcinoma.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"51 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":"124940029","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: 13

Automated Differentiation between Normal Sinus Rhythm, Atrial Tachycardia, Atrial Flutter and Atrial Fibrillation during Electrophysiology 电生理过程中正常窦性心律、房性心动过速、心房扑动和心房颤动的自动鉴别

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-43

N. Razzaq, Shafa-At Ali Sheikh, T. Zaidi, I. Akhtar, Syed Hassaan Ahmed

{"title":"Automated Differentiation between Normal Sinus Rhythm, Atrial Tachycardia, Atrial Flutter and Atrial Fibrillation during Electrophysiology","authors":"N. Razzaq, Shafa-At Ali Sheikh, T. Zaidi, I. Akhtar, Syed Hassaan Ahmed","doi":"10.1109/BIBE.2017.00-43","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-43","url":null,"abstract":"Intracardiac Electrogram (IEGM) are examined during Cardiac Electrophysiology (EP) for detection, differentiation, analysis and treatment of different arrhythmias. The arrhythmia detection involves EP stimulation, observing IEGM response on monitor screens, and manual evaluation of IEGM key features. The process is time consuming and requires high level of expertise of Electro physiologists. During an EP stimulation process, a patient may develop Atrial Fibrillation (AF) and it is important for patient to be taken out of the AF before further proceeding with the procedure. It is required to automate the arrhythmia detection process during an EP study for real time monitoring of the patient condition and safety. In our previous work, successful detection of Atrio-Ventricular Reentrant Tachycardia and Atrio-Ventricular Nodal Re-entry Tachycardia was achieved in time domain. This work has been undertaken to automatically detect the AF as well as differentiate it from Atrial Flutter (AFL), Atrial Tachycardia (AT) and Normal Sinus Rhythm (NSR). In proposed work, non parametric technique has been applied on atrial IEGM signal for estimation of Dominant frequency (DF) to find out atrial activation rate during NSR, AT, AFL and AF. A new spectral parameter, Average Power Spectral Ratio (APSR), has been defined for ensuring reliability of DF for AF detection as well as differentiation of AF from other atrial arrhythmias. The proposed system successfully detects and differentiates between NSR, AT, AFL and AF with an accuracy of 99.52%. The proposed system can also be effectively used for additional therapeutic application by implantable cardioverter defibrillators.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"2 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":"125168439","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

Optimizing Protein Search Engines using Particle Swarm Optimization 利用粒子群优化技术优化蛋白质搜索引擎

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-31

Majdi Maabreh, Basheer Qolomany, Ajay K. Gupta, James R. Springstead

{"title":"Optimizing Protein Search Engines using Particle Swarm Optimization","authors":"Majdi Maabreh, Basheer Qolomany, Ajay K. Gupta, James R. Springstead","doi":"10.1109/BIBE.2017.00-31","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-31","url":null,"abstract":"The results of protein search engines depend mainly upon a set of parameters that adjust the searching space. One of the most effective parameters is the peptide mass window tolerance (w). Most of the current search engines use a constant user-defined value for this parameter. As an alternative option, Comet search engine designers proposed a statistical technique to estimate the best tolerance window for an input spectra file. However, this technique sometimes fails in picking a value, may set the parameter to a value that results in a loss of many correct matches, and is available only for one type of mass; namely ppm. In this paper, we propose to use particle swarm optimization (PSO) to improve the coverage of search engines by picking the optimal value for this influential parameter to maximize PSMs. Our results show that this biologically-inspired algorithm can be utilized to find peptide mass window tolerance values that facilitate Comet to increase peptide spectra matches, resulting in improved peptide identification. We also show experimental evidence that an open search (i.e., wide tolerance window) does not always optimize spectra matching using the current search engines and that narrow tolerance windows improve the coverage of protein search engines.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"42 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":"123592306","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

Rule Editor for ARDEN Syntax Generation towards a more Effective Self-Management of Asthma Disease Patients ARDEN语法生成规则编辑器，实现哮喘患者更有效的自我管理

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.000-1

M. Tsipouras, N. Giannakeas, Stefanos Doumpoulakis, A. Voulgaridis, D. Kikidis, K. Votis, D. Tzovaras

引用次数: 0

Computational Tools for Analysis of Codon Usage in Viral Genomes and its Hosts 病毒基因组及其宿主密码子使用分析的计算工具

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-15

Jonathan Felipe Xavier, C. Putonti, C. Nobre

引用次数: 0

Desynchronization and Energy Efficiency of Gaussian Neurostimulation on Different Sites of the Basal Ganglia 高斯神经刺激在基底神经节不同部位的非同步化和能量效率

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-78

M. Daneshzand, S. Ibrahim, M. Faezipour, B. Barkana

引用次数: 11

Genome Polymorphism Detection Through Relaxed de Bruijn Graph Construction 基于松弛de Bruijn图构建的基因组多态性检测

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-53

M. Fujimoto, Cole A. Lyman, Anton Suvorov, P. Bodily, Q. Snell, K. Crandall, S. Bybee, M. Clement

引用次数: 2

Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD) 机器学习分类技术在慢性肾病(CKD)疾病分类与数据分析预测中的性能评价

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2017-10-01 DOI: 10.1109/BIBE.2017.00-39

W. H. S. D. Gunarathne, K. Perera, K. Kahandawaarachchi

{"title":"Performance Evaluation on Machine Learning Classification Techniques for Disease Classification and Forecasting through Data Analytics for Chronic Kidney Disease (CKD)","authors":"W. H. S. D. Gunarathne, K. Perera, K. Kahandawaarachchi","doi":"10.1109/BIBE.2017.00-39","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-39","url":null,"abstract":"Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient’s status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 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":"129735441","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: 62