2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第5页

Determining Dose-Response Characteristics of Molecular Perturbations in Whole-Organism Assays Using Biological Imaging and Machine Learning 利用生物成像和机器学习确定整个生物体检测中分子扰动的剂量-反应特征

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621083

D. Asarnow, Rahul Singh

引用次数: 3

Early experiences in 4D quantitative analysis of insulin granules in living beta-cells 活β细胞胰岛素颗粒4D定量分析的早期经验

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621293

E. Cordelli, M. Merone, F. D. Giacinto, B. Daniel, G. Maulucci, S. Sasson, P. Soda

{"title":"Early experiences in 4D quantitative analysis of insulin granules in living beta-cells","authors":"E. Cordelli, M. Merone, F. D. Giacinto, B. Daniel, G. Maulucci, S. Sasson, P. Soda","doi":"10.1109/BIBM.2018.8621293","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621293","url":null,"abstract":"Pancreatic beta cells biosynthesize and package insulin in insulin granules, whose secretion is regulated to maintain blood glucose homeostasis. The detailed knowledge of the dynamics of insulin granules could reveal defects in the intracellular handling and secretion of these granules, leading to impaired insulin secretion and consequently to the development of several metabolic diseases, including type-2 diabetes and the metabolic syndrome. The use of spinning disk confocal and light sheet microscopy with fast sequential scanning that enable rapid volumetric imaging, allows to monitor at high spatial and temporal resolution the dynamics of insulin granules. However, obtaining all the information for accurate 3D imaging and particle tracking within a single cell is complex and challenging, and extracting information from the particle tracking data requires to analyse the segments of motion trajectories. To this aim, we present in this study a quantitative analysis of the 4D motion of insulin granules in glucose-stimulated INS- 1E beta cells. First, we tracked each granule inside the cells via a computer-based automatic approach relying on a two-step iterative process. Next, we removed the artifacts and introduced a set of quantitative cinematic features describing granule dynamics. Finally, we implemented an unsupervised machine learning based exploratory data analysis, which allows to distinguish two sets of granules marked by distinct dynamics: a first pool is characterized by a diffusive dynamic behavior, and a second pool that is characterized by a more directed and targeted movement. These pools may have distinct functional roles and/or interactions with other structures and organelles in beta cells that could be selectively impaired in pathological settings.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134569782","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: 3

ProbPFP: A Multiple Sequence Alignment Algorithm Combining Partition Function and Hidden Markov Model with Particle Swarm Optimization ProbPFP:一种结合配分函数、隐马尔可夫模型和粒子群优化的多序列比对算法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621220

Qing Zhan, Nan Wang, Shuilin Jin, Renjie Tan, Qinghua Jiang, Yadong Wang

{"title":"ProbPFP: A Multiple Sequence Alignment Algorithm Combining Partition Function and Hidden Markov Model with Particle Swarm Optimization","authors":"Qing Zhan, Nan Wang, Shuilin Jin, Renjie Tan, Qinghua Jiang, Yadong Wang","doi":"10.1109/BIBM.2018.8621220","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621220","url":null,"abstract":"The substitution score for pairwise sequence alignment is essential in conducting multiple sequence alignment (MSA). The Hidden Markov Model (HMM) and partition function are two methods that are widely chosen for this purpose. Recent studies showed that the accuracy of alignment could be improved by combining the partition function and HMM algorithms or optimizing the parameters of HMM. However, the combination of optimized HMM and partition function, which could greatly improve the accuracy of alignment, was ignored in these studies. This study presents a new MSA algorithm known as ProbPFP that combines the partition function and the HMM optimized by particle swarm optimization (PSO). In this work, the parameters of HMM were first optimized by the PSO algorithm, and the posterior probabilities derived from the HMM were subsequently combined with the results derived from the partition function to compute a comprehensive substitution score for alignment. To assess the effectiveness, ProbPFP was compared with 13 leading aligners, namely, Probalign, CONTRAlign, ProbCons, MUSCLE, MAFFT, COBALT, T-Coffee, ClustalΩ, ClustalW, DIALIGN, PicXAA, Align-m and KALIGN2. The results showed that ProbPFP achieved the highest average sum-of-pairs (SP) scores (0.9015, 0.5984) and average total column (TC) scores (0.8170, 0.3956) on two benchmark sets OXBench and SABmark, as well as the second highest average SP score (0.8250) and average TC score (0.6703) on the benchmark set BAliBASE. We also used the alignments generated by ProbPFP and 4 other leading aligners to rebuild the phylogenetic trees of 6 families from the TreeFam database. The result suggests that the trees from the alignments generated by ProbPFP are closer to the reference trees.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134297942","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: 6

Comparing adverse effects of Hepatitis C drugs using FAERS data 使用FAERS数据比较丙型肝炎药物的不良反应

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621427

Jing Huang, Xinyuan Zhang, Jiayi Tong, Jingcheng Du, R. Duan, Liu Yang, J. Moore, Y. Chen, Cui Tao

引用次数: 0

The lookAfterRisk Project: Dynamic Cardiovascular Risk Assessment based on Remote Monitoring Solutions lookAfterRisk项目:基于远程监测解决方案的动态心血管风险评估

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621327

Simão Paredes, J. Henriques, T. Rocha, P. Carvalho, J. Morais, Luis Santos, R. Carvalho

{"title":"The lookAfterRisk Project: Dynamic Cardiovascular Risk Assessment based on Remote Monitoring Solutions","authors":"Simão Paredes, J. Henriques, T. Rocha, P. Carvalho, J. Morais, Luis Santos, R. Carvalho","doi":"10.1109/BIBM.2018.8621327","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621327","url":null,"abstract":"The LookAfterRisk project addresses the management of myocardial infarction (MI) patients. The main goal is the development of models for cardiovascular (CV) risk assessment of acute events integrating data from home-mobile technologies, in order to stratify patients according to their care needs. The models are applied at hospital admission, in patients with a first episode of acute MI and are continuously updated when the patient returns home.The scientific goal is the use of computational intelligence methodologies for the development of personalized, interpretable and dynamic models in the risk assessment of acute events, i.e., death/re-hospitalization. Three major scientific challenges are addressed: i) extraction of knowledge from recent datasets using data mining approaches; ii) integration of this knowledge with clinical evidence in a meaningful and interpretable way; iii) update of risk level based on data periodically collected at home during the follow up period.The result is an integrated tele-monitoring platform, merging the advantages of the continuous monitoring provided by low cost mobile technologies with prediction models.The validation is performed in the hospital admission using the largest MI Portuguese dataset (N=16000), provided by the Portuguese Society of Cardiology (PSC). The second phase is based on a home telemonitoring observational study (9 months), involving 50 patients (admitted with a first episode of acute MI). The data collected at patient’s home will be: blood pressure, heart rate, cholesterol and glycaemia. The project lasts 36 months; the team is composed by University of Coimbra, Leiria Hospital Centre and has the commitment of SPC.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131801200","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

Automatic 3D Neuron Tracing Based on Terminations Detection 基于终止检测的自动三维神经元跟踪

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621212

Chao Wang, Weixun Chen, Min Liu, Zhi Zhou

引用次数: 1

Using Deep Learning to Classify X-ray Images of Potential Tuberculosis Patients 利用深度学习对潜在结核病患者的x射线图像进行分类

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621525

Ojasvi Yadav, K. Passi, Chakresh Kumar Jain

引用次数: 34

Computerized Wrist pulse signal Diagnosis using Gradient Boosting Decision Tree 基于梯度增强决策树的腕部脉搏信号诊断

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621391

Xue Li, Zhiyue Fu, P. Qian, Lijuan Wang, Hongkai Zhang, Xiaofang Zhou, Wenqiang Zhang, Fufeng Li

{"title":"Computerized Wrist pulse signal Diagnosis using Gradient Boosting Decision Tree","authors":"Xue Li, Zhiyue Fu, P. Qian, Lijuan Wang, Hongkai Zhang, Xiaofang Zhou, Wenqiang Zhang, Fufeng Li","doi":"10.1109/BIBM.2018.8621391","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621391","url":null,"abstract":"In traditional Chinese medicine (TCM), pulse diagnosis is an important diagnostic method that has a long history and has been widely applied. Wrist pulse signals can be used to analyze a person’s health status, reflecting the pathologic changes of the person’s body condition. With regard to TCM pulse diagnosis, the However, the traditional diagnostic approach has been mainly based on the feel of the doctor, which is non-quantitative and subjective. This paper aims to present a new classification method is proposed for analyzing wrist pulse signals, to provide an automatic and quantitative approach for the diagnosis of TCM based on the pulse. Methods: First, the time domain analysis and hemodynamics method were used to extract and analyze pulse parameters. Then the filtering method was used to select all features. Furthermore, GBDT was used to classify and identify the pulse, and establish a model. Results: The wave peaks, wave valleys and time periods, pulse wave velocity and reflection factors are extracted by time domain analysis and hemodynamic analysis. Then, four important features, including h3/h1, h4/h1, w/t and Rf, were selected using the filter feature selection method. Then, the GBDT classification method was used to classify the pulse image of TCM. The middle GBDT classification method exhibited the best effect. The recognition accuracy of the sliding vein, chord vein and chord pulse was 90.33%, 83.52%, 97.74% and 78.60%, respectively, and the overall recognition accuracy was 90.51%. Conclusion: The parameters of the pulse map were optimized and the classification and recognition model of the pulse image was established to realize the automatic recognition of characteristics of pulse diagnosis in TCM. Based on the GBDT classification recognition method, a more accurate classification and recognition model of TCM was established.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059152","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: 14

An embedded method for gene identification in heterogenous data involving unwanted heterogeneity 在涉及不需要的异质性的异构数据中进行基因鉴定的嵌入方法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621445

Meng Lu

引用次数: 1

Image pre-processing in computer vision systems for melanoma detection 黑素瘤检测计算机视觉系统中的图像预处理

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621507

E. Vocaturo, E. Zumpano, P. Veltri

引用次数: 31