2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第6页

Graph Cuts-based Segmentation of Alveolar Bone in Ultrasound Imaging 超声成像中基于图形切面的牙槽骨分割

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621309

K. Nguyen, Danni Shi, N. Kaipatur, E. Lou, P. Major, K. Punithakumar, L. Le

{"title":"Graph Cuts-based Segmentation of Alveolar Bone in Ultrasound Imaging","authors":"K. Nguyen, Danni Shi, N. Kaipatur, E. Lou, P. Major, K. Punithakumar, L. Le","doi":"10.1109/BIBM.2018.8621309","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621309","url":null,"abstract":"Alveolar bone is a part of the periodontium complex supporting the teeth. Conventional radiography and cone-beam computed tomography are currently used to image the alveolar bones. Recently, ionizing radiation-free ultrasound has shown promising potential to image dento-periodontium. However, the ability to visualize alveolar bones in ultrasound images is a challenge for the dentists who are novice to ultrasonography. This study proposes a semi-automated technique to segment alveolar bone by using a multi-label graph cuts optimization approach, where the K-means clustering of intensity values was used in constructing the initial graph. A homomorphic filter was employed as a preprocessing step to de-noise the ultrasound data. The approach was evaluated by over 15 ultrasound images acquired from fresh porcine specimens. Four quantitative evaluators, namely Dice coefficient, sensitivity, specificity, and Hausdorff distance were measured from the proposed method and the manual ground truth by an expert orthodontist. The inter-rater and intra-rater variabilities were also calculated using the delineations by three raters with different levels of experience. The study has demonstrated that the proposed segmentation method provides consistent, reliable, and accurate results among raters and thus has potential to be used as a tool to help dentists to delineate alveolar bones for further analysis.","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":"131184661","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: 8

Prediction of Drug-Disease Associations and Their Effects by Signed Network-Based Nonnegative Matrix Factorization 基于签名网络的非负矩阵分解预测药物-疾病关联及其影响

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621191

Wen Zhang, Feng Huang, Xiang Yue, Xiaoting Lu, Weitai Yang, Zhishuai Li, Feng Liu

{"title":"Prediction of Drug-Disease Associations and Their Effects by Signed Network-Based Nonnegative Matrix Factorization","authors":"Wen Zhang, Feng Huang, Xiang Yue, Xiaoting Lu, Weitai Yang, Zhishuai Li, Feng Liu","doi":"10.1109/BIBM.2018.8621191","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621191","url":null,"abstract":"Predicting drug-disease associations using computational methods benefits drug repositioning. Drug-disease associations are events that drugs exert effects on diseases, there are different effects about drug-disease associations. For example, drug-disease associations are annotated as therapeutic or marker/mechanism (non-therapeutic) in Comparative Toxicogenomics database (CTD). However, existing association prediction methods ignore effects that drugs exert on diseases. In this paper, we propose a signed network-based nonnegative matrix factorization method (SNNMF) to predict drug-disease associations and their effects. First, drug-disease associations are represented as a signed bipartite network with two types of links for therapeutic effects and non-therapeutic effects. After decomposing the network into two subnetworks, SNNMF aims to approximate the association matrix of each subnetwork by two nonnegative matrices, which are low-dimensional latent representations for drugs and diseases respectively, and diseases in two subnetworks share the same latent representations. In the computational experiments, SNNMF performs well in predicting effects of drug-disease associations. Moreover, SNNMF accurately predicts drug-disease associations and outperforms existing association prediction methods. Case studies show that SNNMF helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their therapeutic effects.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"671 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":"133268284","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

Analysis of integrated inflammatory bowel disease mouse models to assess their disease driving pathways and relevance for Crohn’s disease and Ulcerative colitis 综合炎症性肠病小鼠模型分析以评估其疾病驱动途径及其与克罗恩病和溃疡性结肠炎的相关性

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621158

W. Schultz, C. Monast, Mathias Hesse, L. Chang, Michael Scully, Yanqing Chen, Xuejun Liu, Zachary Hutchins, S. Pavlidis, F. Baribaud

引用次数: 0

Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content 基于全注意力的药物相互作用提取，利用用户生成的内容

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621281

Bo Xu, Xiufeng Shi, Zhehuan Zhao, Wei Zheng, Hongfei Lin, Zhihao Yang, Jian Wang, Feng Xia

{"title":"Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content","authors":"Bo Xu, Xiufeng Shi, Zhehuan Zhao, Wei Zheng, Hongfei Lin, Zhihao Yang, Jian Wang, Feng Xia","doi":"10.1109/BIBM.2018.8621281","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621281","url":null,"abstract":"When a patient takes multiple medications simultaneously under treatment, it is vital for the doctor to comprehend all interactions between drugs in the prescription entirely. Drug drug interaction (DDI) extraction aims to obtain interactions between drugs from biomedical literature automatically. Nowadays, researchers apply artificial intelligence and natural language processing techniques to perform DDI extraction task. Existing DDI extraction methods have utilized some kinds of external resources such as biomedical databases or ontologies to offer more knowledge and improve the performance. However, these kinds of external resources are delayed because of the hardship of updating. User-generated content (UGC) is another sort of external biomedical resource which is up-to-date and can be updated rapidly. We attempt to utilize UGC resource in our deep learning DDI extraction method to provide more fresh information. We propose a DDI extraction method that merges UGC information and contextual information together by a new attention mechanism called full-attention. We conduct a series of experiments on the DDI 2013 Evaluation dataset to evaluate our method. UGC-DDI outperforms the other state-of-the-art methods and achieves a competitive F-score of 0.712.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"166 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720157","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: 8

Forecasting depressive relapse in Bipolar Disorder from clinical data 从临床资料预测双相情感障碍抑郁复发

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621255

Renato Borges-Junior, R. Salvini, A. Nierenberg, G. Sachs, B. Lafer, R. Dias

{"title":"Forecasting depressive relapse in Bipolar Disorder from clinical data","authors":"Renato Borges-Junior, R. Salvini, A. Nierenberg, G. Sachs, B. Lafer, R. Dias","doi":"10.1109/BIBM.2018.8621255","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621255","url":null,"abstract":"Bipolar disorder (BD) is a mood disorder characterized by recurrent episodes of depression and mania/hypomania. Depressive relapse in BD reach rates close to 50% in 1 year and 70% in up to 4 years of treatment. Several studies have been developed to discover more efficient treatments for BD and prevent relapses. However, most of relapse studies used only statistical methods. We aim to analyze the performance of machine learning algorithms in predicting depressive relapse using only clinical data from patients. Five well-used machine learning algorithms (Support Vector Machines, Random Forests, Naïve Bayes and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEPBD) dataset of a cohort of 800 patients who became euthymic during the study and were followed up for 1 year: 507 presented a depressive relapse and 293 did not. The algorithms showed reasonable performance in the prediction task, ranging from 61% to 80% in the F-measure. Random Forest algorithm had a higher average of performance (Relapse Group 68%; No Relapse Group 74%), although, the performance between classifiers showed no significant difference. Random Forest analysis demonstrated that the three most important mood symptoms observed were: interest, depression mood and energy. Results show that the machine learning algorithms could be seen as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 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":"115579823","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

PASCL: Pathway-based Sparse Deep Clustering for Identifying Unknown Cancer Subtypes PASCL:基于路径的稀疏深度聚类识别未知癌症亚型

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621387

Tejaswini Mallavarapu, Jie Hao, Youngsoon Kim, J. Oh, Mingon Kang

{"title":"PASCL: Pathway-based Sparse Deep Clustering for Identifying Unknown Cancer Subtypes","authors":"Tejaswini Mallavarapu, Jie Hao, Youngsoon Kim, J. Oh, Mingon Kang","doi":"10.1109/BIBM.2018.8621387","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621387","url":null,"abstract":"Cancer is a heterogeneous disease which has several subtypes that can be distinguished by molecular, histopathological, and clinical stages. Accurate diagnosis of cancer subtypes is vital to identify distinct disease states and develop effective personalized therapies. A number of unsupervised machine learning techniques have been applied to genomic data of the tumor samples, where clusters of patients were formed to be associated with a clinical outcome such as the survival of patients. However, clustering methods based on distance (or similarity) between data often fail to cluster biological data, due to their nonlinearity. In this paper, we develop a PAthway-based Sparse deep CLustering (PASCL) method for the identification of cancer subtypes. PASCL incorporates prior biological knowledge from pathway databases to build a robust and biological interpretable model. We evaluated the performance of PASCL by comparing with several state-of-the-art clustering methods. PASCL outperformed the benchmarking methods with lowest p-value in logrank test, and its outstanding performance is statistically assessed. PASCL provides a solution not only to effectively identify subtypes using high-dimensional nonlinear genomic data, but also to biologically interpret the model at a pathway level.","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":"115795906","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

Mining clinical and laboratory data of neurodegenerative diseases by Machine Learning: transcriptomic biomarkers 通过机器学习挖掘神经退行性疾病的临床和实验室数据:转录组生物标志物

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621072

I. Arisi, M. D'Onofrio, R. Brandi, M. Sonnessa, A. Campanelli, Rita Florio, V. Sposato, F. Malerba, A. Cattaneo, P. Mecocci, G. Bruno, M. Canevelli, M. Tsolaki, N. Pelteki, F. Stocchi, L. Vacca, G. Fiscon, P. Bertolazzi

{"title":"Mining clinical and laboratory data of neurodegenerative diseases by Machine Learning: transcriptomic biomarkers","authors":"I. Arisi, M. D'Onofrio, R. Brandi, M. Sonnessa, A. Campanelli, Rita Florio, V. Sposato, F. Malerba, A. Cattaneo, P. Mecocci, G. Bruno, M. Canevelli, M. Tsolaki, N. Pelteki, F. Stocchi, L. Vacca, G. Fiscon, P. Bertolazzi","doi":"10.1109/BIBM.2018.8621072","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621072","url":null,"abstract":"Low sensitivity and specificity of current diagnostic methodologies lead to frequent misdiagnosis of Alzheimer’s and other dementia, causing an extra economic and social burden. We aim to compare real word data with the largest public databases, to extract new diagnostic models for an earlier and more accurate diagnosis of cognitive impairment. We analyzed both neuropsychological, neurological, physical assessments and transcriptomic data from biosamples. We used Machine Learning approaches and biostatistical methods to analyze the transcriptome from the large-scale ADNI and AddNeuroMed international projects: we selected some genes as potential transcriptomic biomarkers and highlighted affected cellular processes. Furthermore the analysis, by machine learning, of real-world data provided by European clinical dementia centres, resulted in a small subset of comorbidities able to discriminate diagnostic classes with a good classifier performance.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"15 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":"124254856","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

Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification 组织病理学图像分类的半监督深度线性判别分析

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621451

Lei Cui, Jun Feng, L. Yang

{"title":"Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification","authors":"Lei Cui, Jun Feng, L. Yang","doi":"10.1109/BIBM.2018.8621451","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621451","url":null,"abstract":"Recently, deep learning techniques achieve remarkable classification performance on histopathology images. How-ever, they usually require a large amount of labeled training images to obtain satisfactory accuracy, and manual labeling is labor expensive and time consuming. To address this issue, in this paper, we propose a novel semi-supervised deep learning framework, namely semi-supervised deep linear discriminant analysis, by taking advantage of the deep neural network (DNN) and the graph to simultaneously exploit the semantic information of labeled and unlabeled images for classification. Specifically, we first replace the loss function of the DNN with the objective function of linear discriminant analysis to produce features minimizing the intra-class distance yet maximizing the inter-class distance, in order to construct a robust and effective graph Laplacian. Afterwards, we design a new objective function via employing the graph constructed by features of labeled and un-labeled images, and then adopt this objective as the loss function of the DNN to produce features for classification. Experiments on skeletal muscle and lung cancer images demonstrate the proposed framework outperforms several recent state of the arts.","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":"124342371","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

3D Neuron Branch Points Detection in Microscopy Images 显微镜图像中的三维神经元分支点检测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621482

Min Liu, Chao Wang, Weixun Chen

{"title":"3D Neuron Branch Points Detection in Microscopy Images","authors":"Min Liu, Chao Wang, Weixun Chen","doi":"10.1109/BIBM.2018.8621482","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621482","url":null,"abstract":"Neuron tracing (reconstruction) is an important step toward understanding the functionality of neuronal networks. Neuron termination points and branch points, collectively called critical points, play an important role in neuron tracing applications. There are some existing methods for 3D neuron termination points detection. However, 3D branch points detection method has barely been explored. In this paper, we propose a 3D branch points detection method in microscopy images by reverse-mapping the 2D branch points back into the 3D space, according to the pixel intensity distribution along the projection direction. The 2D branch points are detected by an adaptive ray-shooting model in 2D maximum intensity projections (MIPs), where the center is the 3D branch point candidates, of a specified number of adjacent slices along the Z direction. The adaptive ray-shooting model analyzes the intensity distribution of the neighborhood around the branch point candidates and is robust to neurite diameter variations. The experimental results on multiple neuron image datasets show that our proposed method can achieve an average false negative rate and false positive rate of 15.67% and 10.67% for neuron branch point, respectively.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"22 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":"124398101","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

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types 单细胞转录组学揭示了pbmc和其他细胞类型特有的总结模式

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621396

Jingjie Xu, R. A. Shaikh, V. Brusic

引用次数: 2