Proceedings. IEEE International Conference on Bioinformatics and Biomedicine最新文献_第10页

Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. 用于从冷冻电镜蛋白质密度图中检测二级结构的深度卷积神经网络

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2016-12-01 Epub Date: 2017-01-19 DOI: 10.1109/BIBM.2016.7822490

Rongjian Li, Dong Si, Tao Zeng, Shuiwang Ji, Jing He

{"title":"Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy.","authors":"Rongjian Li, Dong Si, Tao Zeng, Shuiwang Ji, Jing He","doi":"10.1109/BIBM.2016.7822490","DOIUrl":"10.1109/BIBM.2016.7822490","url":null,"abstract":"The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å ). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"41-46"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952046/pdf/nihms874389.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36106213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival. 基于多模态图的半监督管道预测癌症生存。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2016-12-01 Epub Date: 2017-01-19 DOI: 10.1109/bibm.2016.7822516

Hamid Reza Hassanzadeh, John H Phan, May D Wang

{"title":"A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival.","authors":"Hamid Reza Hassanzadeh, John H Phan, May D Wang","doi":"10.1109/bibm.2016.7822516","DOIUrl":"https://doi.org/10.1109/bibm.2016.7822516","url":null,"abstract":"Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"184-189"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bibm.2016.7822516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38151657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Analysis of Temporal Constraints in Qualitative Eligibility Criteria of Cancer Clinical Studies. 肿瘤临床研究定性资格标准的时间约束分析。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2016-12-01 Epub Date: 2017-01-19 DOI: 10.1109/BIBM.2016.7822607

Zhe He, Zhiwei Chen, Jiang Bian

引用次数: 2

CHALLENGES IN MATCHING SECONDARY STRUCTURES IN CRYO-EM: AN EXPLORATION. 低温电镜中二级结构匹配的挑战:探索。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2016-12-01 Epub Date: 2017-01-19 DOI: 10.1109/BIBM.2016.7822776

Devin Haslam, Mohammad Zubair, Desh Ranjan, Abhishek Biswas, Jing He

引用次数: 2

Sparse Canonical Correlation Analysis via Truncated ℓ₁-norm with Application to Brain Imaging Genetics. 截断1-范数稀疏典型相关分析及其在脑成像遗传学中的应用。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2016-01-01 Epub Date: 2017-01-19 DOI: 10.1109/BIBM.2016.7822605

Lei Du, Tuo Zhang, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L Risacher, Lei Guo, Andrew J Saykin, Li Shen

{"title":"Sparse Canonical Correlation Analysis via Truncated ℓ1-norm with Application to Brain Imaging Genetics.","authors":"Lei Du, Tuo Zhang, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L Risacher, Lei Guo, Andrew J Saykin, Li Shen","doi":"10.1109/BIBM.2016.7822605","DOIUrl":"https://doi.org/10.1109/BIBM.2016.7822605","url":null,"abstract":"Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants. The ℓ0-norm is more desirable, which however remains unexplored since the ℓ0-norm minimization is NP-hard. In this paper, we impose the truncated ℓ1-norm to improve the performance of the ℓ1-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2016 ","pages":"707-711"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2016.7822605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35426546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model. 基于集成流形排序模型的人可吸收MicroRNA预测。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359697

Jiang Shu, Kevin Chiang, Dongyu Zhao, Juan Cui

{"title":"Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model.","authors":"Jiang Shu, Kevin Chiang, Dongyu Zhao, Juan Cui","doi":"10.1109/BIBM.2015.7359697","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359697","url":null,"abstract":"MicroRNAs, a class of short non-coding RNAs, are able to regulate more than half of human genes and affect many fundamental biological processes. It has been long considered synthesized endogenously until very recent discoveries showing that human can absorb exogenous microRNAs from dietary resources. This finding has raised a challenge scientific question: which exogenous microRNAs can be integrated into human circulation and possibly exert functions in human? Here we present a well-designed ensemble manifold ranking model for identifying human absorbable exogenous miRNAs from 14 common dietary species. Specifically, we have analyzed 4,910 dietary microRNAs with 1,120 features derived based on the microRNA sequence and structure. In total, 70 discriminative features were selected to characterize the circulating microRNAs in human and have been used to infer the possibility of a certain exogenous microRNA getting integrated into human circulation. Finally, 461 dietary microRNAs have been identified as transportable exogenous microRNAs. To assess the performance of our ensemble model, we have validated the top predictions through a milk-feeding study. In addition, 26 microRNAs from two virus species were predicted as transportable and have been validated in two external experiments. The results demonstrate the data-driven computational model is highly promising to study transportable microRNAs while bypassing the complex mechanistic details.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"295-300"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36655301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Deep Learning of Tissue Fate Features in Acute Ischemic Stroke. 急性缺血性脑卒中组织命运特征的深度学习。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359869

Noah Stier, Nicholas Vincent, David Liebeskind, Fabien Scalzo

引用次数: 47

Integration of multimodal RNA-seq data for prediction of kidney cancer survival. 整合多模态RNA-seq数据预测肾癌生存。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 DOI: 10.1109/BIBM.2015.7359913

Matt Schwartzi, Martin Parkl, John H Phanl, May D Wang

引用次数: 10

Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research. Insight:多中心神经保健研究的语义来源和分析平台。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 DOI: 10.1109/BIBM.2015.7359776

Priya Ramesh, Annan Wei, Elisabeth Welter, Yvan Bamps, Shelley Stoll, Ashley Bukach, Martha Sajatovic, Satya S Sahoo

{"title":"Insight: Semantic Provenance and Analysis Platform for Multi-center Neurology Healthcare Research.","authors":"Priya Ramesh, Annan Wei, Elisabeth Welter, Yvan Bamps, Shelley Stoll, Ashley Bukach, Martha Sajatovic, Satya S Sahoo","doi":"10.1109/BIBM.2015.7359776","DOIUrl":"https://doi.org/10.1109/BIBM.2015.7359776","url":null,"abstract":"Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with \"expansion of query expression\" using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2015 ","pages":"731-736"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2015.7359776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34393837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence. 基于对称信息散度的非负矩阵分解混合算法。

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine Pub Date : 2015-11-01 Epub Date: 2015-12-17 DOI: 10.1109/BIBM.2015.7359924

Karthik Devarajan, Nader Ebrahimi, Ehsan Soofi

引用次数: 4