2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献_第7页

On accurate, automated and insightful deviation analysis of clinical protocols 对临床方案进行准确、自动化和深刻的偏差分析

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621133

H. Yan, Xudong Lu, P. V. Gorp, S. Heines, Shan Nan, W. V. Mook, D. Bergmans, U. Kaymak, H. Duan

引用次数: 0

Cell Tracking Across Noisy Image Sequences Via Faster R-CNN and Dynamic Local Graph Matching 基于更快R-CNN和动态局部图匹配的噪声图像序列细胞跟踪

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621192

Min Liu, Lehui Wu, Weili Qian, Yalan Liu

{"title":"Cell Tracking Across Noisy Image Sequences Via Faster R-CNN and Dynamic Local Graph Matching","authors":"Min Liu, Lehui Wu, Weili Qian, Yalan Liu","doi":"10.1109/BIBM.2018.8621192","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621192","url":null,"abstract":"Automated tracking of cells in time-lapse live-imaging datasets of developing multicellular tissues is gaining popularity in developmental biology for understanding the cell growth dynamics. The tracking of plant cells across noisy microscopy image sequences is very challenging, because plant cells in noisy region cannot be correctly segmented and cause serious errors in subsequent cell tracking procedure. In this paper, we present to track plant cells across noisy images using a tracking method which is based on Faster R-CNN and dynamic local graph matching. Faster R-CNN is employed to detect cells in noisy images, and it is improved by cell characteristic prior bounding box design and soft non-maximum suppression strategies. Then a dynamic local graph matching model is proposed to track the detected plant cells, by exploiting the cells’ tight spatial and temporal contextual information. It tends to prevent the cell matching error accumulation by selecting the most similar cell pair in the dynamically growing neighbor set of matched cells. Compared with the existing tracking methods for plant cells, the experimental results show that the proposed method can greatly improve the tracking accuracy.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"78 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":"114871029","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

Drug2Vec: Knowledge-aware Feature-driven Method for Drug Representation Learning Drug2Vec:知识感知特征驱动的药物表示学习方法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621390

Ying Shen, Kaiqi Yuan, Yaliang Li, Buzhou Tang, Min Yang, Nan Du, Kai Lei

引用次数: 11

Biomedical Event Trigger Detection Based on BiLSTM Integrating Attention Mechanism and Sentence Vector 基于注意机制和句子向量集成的BiLSTM生物医学事件触发检测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621217

Xinyu He, Lishuang Li, Jia Wan, Dingxin Song, Jun Meng, Zhanjie Wang

引用次数: 7

Identification of lncRNA-disease association using bi-random walks 利用双随机漫步鉴定lncrna与疾病的关联

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621132

Yiqun Gao, Jialu Hu, Xuequn Shang

引用次数: 3

Overlapping Community Detection in Multi-view Brain Network 多视点脑网络中的重叠社区检测

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621075

Ling Huang, Changdong Wang, Hongyang Chao

引用次数: 14

An agent based modeling approach for the analysis of tuberculosis – immune system dynamics 基于agent的结核免疫系统动力学分析建模方法

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621355

F. Pappalardo, G. Russo, M. Pennisi, G. Sgroi, G. Palumbo, S. Motta, Epifanio Fichera

{"title":"An agent based modeling approach for the analysis of tuberculosis – immune system dynamics","authors":"F. Pappalardo, G. Russo, M. Pennisi, G. Sgroi, G. Palumbo, S. Motta, Epifanio Fichera","doi":"10.1109/BIBM.2018.8621355","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621355","url":null,"abstract":"Tuberculosis is one of the world’s deadliest diseases that infects one third of the world’s population, mostly in developing countries. However, tuberculosis is becoming again very dangerous also for developed countries, due to the increased mobility of the world population, and the appearance of several new bacterial strains that are multi-drug resistant. With the aim to help in finding new therapeutic interventions against tuberculosis, we present the application of a computational modeling infrastructure named UISS (Universal Immune System Simulator) able to simulate the main features and dynamics of the immune system activities. We show a further development of UISS to consider the underlying tuberculosis pathogenesis and its interaction with the host immune system. Even though the model can be further personalized employing immunological parameters and genetic information, based on the available data, we obtained simulation scenarios able to reproduce persistent latent infection or the development of active disease. In particular, UISS is able to simulate those mechanisms in which M. tuberculosis is involved in the early influx of alveolar macrophages and recruited neutrophils until the formation of the tuberculous granuloma, at both cellular and molecular levels.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"95 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":"116316215","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

Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis Cox-PASNet:基于路径的稀疏深度神经网络生存分析

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621345

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

{"title":"Cox-PASNet: Pathway-based Sparse Deep Neural Network for Survival Analysis","authors":"Jie Hao, Youngsoon Kim, Tejaswini Mallavarapu, J. Oh, Mingon Kang","doi":"10.1109/BIBM.2018.8621345","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621345","url":null,"abstract":"An in-depth understanding of complex biological processes associated to patients’ survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel pathway-based, sparse deep neural network, called Cox-PASNet, for survival analysis by integrating highdimensional gene expression data and clinical data. Cox-PASNet is a biologically interpretable neural network model where nodes in the network correspond to specific genes and pathways, while capturing nonlinear and hierarchical effects of biological pathways to a patient’s survival. We also provide a solution to train the deep neural network model with HDLSS data. Cox-PASNet was evaluated by comparing the performance of different cutting-edge survival methods such as Cox-nnet, SurvivalNet, and Cox elastic net (Cox-EN). Cox-PASNet significantly outperformed the benchmarking methods, and the outstanding performance was statistically assessed. We provide an open-source software implemented in PyTorch (https://github.com/DataX-JieHao/Cox-PASNet) that enables automatic training, evaluation, and interpretation of Cox-PASNet.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"148 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":"116333433","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: 19

Modeling biological complexity using Biology System Description Language (BiSDL) 利用生物系统描述语言(BiSDL)建模生物复杂性

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621533

F. Muggianu, A. Benso, R. Bardini, E. Hu, G. Politano, S. Carlo

引用次数: 7

An Efficient Implementation of a Subgraph Isomorphism Algorithm for GPUs. gpu上子图同构算法的高效实现。

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2018-12-01 DOI: 10.1109/BIBM.2018.8621444

Vincenzo Bonnici, R. Giugno, N. Bombieri

{"title":"An Efficient Implementation of a Subgraph Isomorphism Algorithm for GPUs.","authors":"Vincenzo Bonnici, R. Giugno, N. Bombieri","doi":"10.1109/BIBM.2018.8621444","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621444","url":null,"abstract":"The subgraph isomorphism problem is a computational task that applies to a wide range of today’s applications, ranging from the understanding of biological networks to the analysis of social networks. Even though different implementations for CPUs have been proposed to improve the efficiency of such a graph search algorithm, they have shown to be bounded by the intrinsic sequential nature of the algorithm. More recently, graphics processing units (GPUs) have become widespread platforms that provide massive parallelism at low cost. Nevertheless, parallelizing any efficient and optimized sequential algorithm for subgraph isomorphism on many-core architectures is a very challenging task. This article presents GRASS, a parallel implementation of the subgraph isomorphism algorithm for GPUs. Different strategies are implemented in GRASS to deal with the space complexity of the graph searching algorithm, the potential workload imbalance, and the thread divergence involved by the non-homogeneity of actual graphs. The paper presents the results obtained on several graphs of different sizes and characteristics to understand the efficiency of the proposed approach.","PeriodicalId":108667,"journal":{"name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"37 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":"124035556","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