2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)最新文献

DGCF: A Distributed Greedy Clustering Framework for Large-scale Genomic Sequences 大规模基因组序列的分布式贪婪聚类框架

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983385

Zekun Yin, Xiaoming Xu, Kaichao Fan, Ruilin Li, Weizhong Li, Weiguo Liu, B. Niu

{"title":"DGCF: A Distributed Greedy Clustering Framework for Large-scale Genomic Sequences","authors":"Zekun Yin, Xiaoming Xu, Kaichao Fan, Ruilin Li, Weizhong Li, Weiguo Liu, B. Niu","doi":"10.1109/BIBM47256.2019.8983385","DOIUrl":"https://doi.org/10.1109/BIBM47256.2019.8983385","url":null,"abstract":"Clustering is a very fundamental while time-consuming compute operation in biological sequence analysis. New sequencing technologies such as NGS and 3GS have dramatically increased both the dataset size and the length of a single read sequence. However, existing tools lack scalability for handling large-scale datasets as well as long sequences. A feasible solution to this problem is to use parallel and distributed systems. The efficient deployment of such systems, however, requires high parallelism in both software implementations as well as algorithmic optimizations. In this paper, we propose DGCF, a Distributed Greedy Clustering Framework which is capable to handle large-scale datasets and long sequences. Our framework adopts a greedy clustering strategy which overlaps communication with computation among many distributed computing nodes. We also design and implement a sparse suffix array (SSA)-based alignment algorithm that can support long sequences. Experiments show that our framework achieves near-linear speedups on a distributed memory cluster.","PeriodicalId":412074,"journal":{"name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721137","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

From clonal diversity to transposon derived long non-coding RNA in trematode Himasthla elongata parthenogenetic generations 从克隆多样性到转座子衍生的长链非编码RNA

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983314

A. Solovyeva, I. Levakin, O. Podgornaya

引用次数: 0

SCENARIO: Discovery of Similar Aspects for Gene Similarity Explanation from Gene Information Network 情景:从基因信息网络发现基因相似性解释的相似方面

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983188

Yidan Zhang, Lei Duan, Huiru Zheng, Jesse Li-Ling, Bin Hu, Ruiqi Qin, Chengxin He

{"title":"SCENARIO: Discovery of Similar Aspects for Gene Similarity Explanation from Gene Information Network","authors":"Yidan Zhang, Lei Duan, Huiru Zheng, Jesse Li-Ling, Bin Hu, Ruiqi Qin, Chengxin He","doi":"10.1109/BIBM47256.2019.8983188","DOIUrl":"https://doi.org/10.1109/BIBM47256.2019.8983188","url":null,"abstract":"Gene similarity analysis not only provides information on understanding the biological roles and functions of a gene, but also reveals the relationships among different genes. In this paper, we identify the novel idea of mining similar aspects from gene information network, i.e., given a pair of genes, we want to know, in which aspects (meta paths) the two genes are mostly similar from the perspective of gene information network? We define a similarity metric based on the set of meta paths connecting the query genes in the gene information network, and use the rank of the similarity of a gene pair in a meta path set to measure the similarity significance in the aspect. A minimal set of meta paths where the query gene pair is ranked the best is a similar aspect. Computing the similar aspects of a query gene pair is far from trivial. In this paper, we propose a novel heuristic based-mining method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from seven public gene-related databases, verified that our proposed method is effective, efficient, and useful.","PeriodicalId":412074,"journal":{"name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995956","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

Predictive Modeling for Geometric Rule-Based Methods 基于几何规则方法的预测建模

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983285

Brittany R. Hoard

引用次数: 0

Brain MRI Super-resolution Reconstruction using a Multi-level and Parallel Conv-Deconv Network 基于多级并行卷积-反卷积网络的脑MRI超分辨率重建

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983233

Lulu Wang, Jinglong Du, A. Gholipour, Zhongshi He, Yuanyuan Jia

{"title":"Brain MRI Super-resolution Reconstruction using a Multi-level and Parallel Conv-Deconv Network","authors":"Lulu Wang, Jinglong Du, A. Gholipour, Zhongshi He, Yuanyuan Jia","doi":"10.1109/BIBM47256.2019.8983233","DOIUrl":"https://doi.org/10.1109/BIBM47256.2019.8983233","url":null,"abstract":"High resolution (HR) magnetic resonance images (MRI) provide rich tissue anatomical information that enables accurate diagnostics and pathological analysis. However, the acquisition of HR MRI is limited by hardware restrictions, scanning time, and signal-to-noise ratio (SNR) in clinical applications. Recently, deep learning has shown promising power for improving the spatial resolution of MRI. In this study, we propose a multilevel and parallel Conv-Deconv super-resolution (CDSR) network to reconstruct high-quality HR MRI from low resolution (LR) inputs. Different from current SR methods based on convolutional neural networks (CNNs), we connect parallel 3D convolution and deconvolution filters to capture context information and extract multi-level features. Hierarchical features are adaptively upsampled using each of their following deconvolution layers and then fused together to recover the HR details. In order to alleviate the optimization difficulty, we introduce the interpolated input to the fused output, which performs like a cross-scale residual learning strategy, hence accelerates the convergence. Extensive experimental results on three benchmark datasets show that our proposed method outperforms current reported MRI SR methods and sets a new state-of-the-art performance.","PeriodicalId":412074,"journal":{"name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125941653","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

Using BioDepot-workflow-builder to Access Public Databases in a Containerized Environment 使用biodepot工作流构建器访问容器化环境中的公共数据库

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983263

Christin Scott, Ling-Hong Hung, W. Lloyd, K. Y. Yeung

引用次数: 0

A Big Data Approach for Health Data Information Retrieval 健康数据信息检索的大数据方法

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983302

Mario Ciampi, E. Masciari, G. Pietro, Stefano Silvestri

引用次数: 0

Microbial Interaction Extraction from Biomedical Literature using Max-Bi-LSTM 利用Max-Bi-LSTM提取生物医学文献中的微生物相互作用

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983259

Ran Zhong, Xusheng Li, Xia Sun, Chengcheng Fu, Tingting He, Xingpeng Jiang

引用次数: 2

A MapReduce tool for in-depth analysis of KEGG pathways: identification and visualization of therapeutic target candidates MapReduce工具，用于深入分析KEGG通路:识别和可视化治疗候选靶点

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8982978

G. Palumbo, Pietro Biondi, G. Sgroi, M. Pennisi, G. Russo, F. Pappalardo

{"title":"A MapReduce tool for in-depth analysis of KEGG pathways: identification and visualization of therapeutic target candidates","authors":"G. Palumbo, Pietro Biondi, G. Sgroi, M. Pennisi, G. Russo, F. Pappalardo","doi":"10.1109/BIBM47256.2019.8982978","DOIUrl":"https://doi.org/10.1109/BIBM47256.2019.8982978","url":null,"abstract":"Intracellular biochemical reactions emerge from the interaction among multiple extracellular signaling components. Considering the number, type and connections of the signaling components represents a needed step to characterize, identify and describe potential targets for a clinical purpose. However, it is increasingly documented that the presence of sub-types of signaling proteins, branching and crosstalk may lead to very variable outcomes in the same path, which is not always well defined experimentally. For this reason, we present an improved version of the algorithm based on the MapReduce paradigm to facilitate the discovery of new therapeutic targets. Our algorithm allows you to scan and perform a search in depth of biological pathway in order to analyze less recurrent and therefore non-trivial paths. These routes represent a chain of biochemical interactions among different biological actors that can be represented by quite distant nodes along the pathway. This type of analysis can also be performed manually, but with high execution times due to the large amount of pathways and genes present. Thus, our tool performs exhaustive analysis in an automated way, drastically reducing the time required. Our proposal allows us to discover the genes far from the initial target genes, also showing the number of occurrences of a given path found within the set of biological pathways analyzed during the simulation.","PeriodicalId":412074,"journal":{"name":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114593098","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

Identifying Common Driver Pathways based on Pan-cancer Data 基于泛癌症数据识别常见驱动通路

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2019-11-01 DOI: 10.1109/BIBM47256.2019.8983414

Jingli Wu, Ke Pan, K. Zhu, Qirong Cai

引用次数: 3