IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences最新文献_第3页

Efficient Sequential and Parallel Algorithms for Incremental Record Linkage 增量记录链接的高效顺序和并行算法

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2019-11-15 DOI: 10.1007/978-3-030-46165-2_3

A. Baihan, R. Ammar, R. Aseltine, Mohammed S. Baihan, S. Rajasekaran

引用次数: 0

Optimized Multiple Fluorescence Based Detection in Single Molecule Synthesis Process Under High Noise Level Environment 高噪声环境下单分子合成过程中基于多重荧光的优化检测

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2019-11-15 DOI: 10.1007/978-3-030-46165-2_6

Hsin-Hao Chen, Chung-Chin Lu

引用次数: 0

Survey on deep convolutional neural networks in mammography 深度卷积神经网络在乳腺造影中的应用综述

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114310

Dina Abdelhafiz, S. Nabavi, Reda Ammar, Clifford Yang

引用次数: 7

Efficient exact algorithms for LDD motif search 高效精确的LDD基序搜索算法

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114294

Peng Xiao, S. Rajasekaran

{"title":"Efficient exact algorithms for LDD motif search","authors":"Peng Xiao, S. Rajasekaran","doi":"10.1109/ICCABS.2017.8114294","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114294","url":null,"abstract":"Motifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Several motif models have been proposed in the literature. The (1, d)-motif model is one of these that has been studied widely. In this model, there are n input sequences and each has a length of m. Input are also two integers I and d. The (I, d)-motif search (LDMS) problem is to identify all the strings (called (I, d)-motifs) of length 1 that occur in each of the sequences within a hamming distance of d. However, this requirement might be unnecessarily stringent. We interpret a motif as a biologically significant entity that is evolutionarily preserved (within some distance). It may be highly improbable that the motif undergoes the same number of changes in each of the species. If d is the maximum number of changes that have occurred in a motif, then it is very likely that the number of mutations in one or more of the species is (possibly much) less than d. To account for this possibility we introduce a new model of motif in this paper. This model is called the (l, d 1 , d 2 )-motif model and is defined as follows. Input are n sequences each of length m and three integers l, d 1 , and d 2 , where d2 1 . The (l, d 1 , d 2 )-motif search (LDDMS) problem is to identify all the strings M (called (l, d 1 , d 2 ) motifs) of length l each such that M occurs in all the input sequences within a hamming distance of d1 and it occurs in at least one of the input sequences within a hamming distance of d 2 . This model is more general than the (l, d)-motif model and hence is NP-hard as well.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"14 5 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81684799","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

Out of one, many: Exploiting intrinsic motions to explore protein structure spaces 其中之一就是:利用内在运动来探索蛋白质结构空间

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114290

David Morris, T. Maximova, E. Plaku, Amarda Shehu

{"title":"Out of one, many: Exploiting intrinsic motions to explore protein structure spaces","authors":"David Morris, T. Maximova, E. Plaku, Amarda Shehu","doi":"10.1109/ICCABS.2017.8114290","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114290","url":null,"abstract":"Reconstructing the energy landscape of a protein holds the key to characterizing its structural dynamics and function [1]. While the disparate spatio-temporal scales spanned by the slow dynamics challenge reconstruction in wet and dry laboratories, computational efforts have had recent success on proteins where a wealth of experimentally-known structures can be exploited to extract modes of motion. In [2], the authors propose the SoPriM method that extracts principle components (PCs) and utilizes them as variables of the structure space of interest. Stochastic optimization is employed to sample the structure space and its associated energy landscape in the defined varible space. We refer to this algorithm as SoPriM-PCA and compare it here to SoPriM-NMA, which investigates whether the landscape can be reconstructed with knowledge of modes of motion (normal modes) extracted from one single known structure. Some representative results are shown in Figure 1, where structures obtained by SoPriM-PCA and those obtained by SoPriM-NMA for the H-Ras enzyme are compared via color-coded projections onto the top two variables utilized by each algorithm. The results show that precious information can be obtained on the energy landscape even when one structural model is available. The presented work opens up interesting venues of research on structure-based inference of dynamics. Acknowledgment: This work is supported in part by NSF Grant No. 1421001 to AS and NSF Grant No. 1440581 to AS and EP. Computations were run on ARGO, a research computing cluster provided by the Office of Research Computing at George Mason University, VA (URL: http://orc.gmu.edu).","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"11 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87453272","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

New automatic and effective tools for genome annotation 新的自动和有效的基因组注释工具

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114287

M. Borodovsky

{"title":"New automatic and effective tools for genome annotation","authors":"M. Borodovsky","doi":"10.1109/ICCABS.2017.8114287","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114287","url":null,"abstract":"Gene prediction and annotation plays central role in genomics. However, in spite of much attention, open problems still exist and stimulate searches for new algorithmic solutions in all categories of gene finding. Prokaryotic genes can be identified with higher average accuracy than eukaryotic ones. Nevertheless, the error rate is not negligible and largely species-specific. Our prokaryotic gene finder GeneMarkS, a self-training tool working in iterations, was used in many genome projects [1]. In the new version, GeneMarkS-2, we introduced a series of heuristic models for training initialization, classification of genomes with respect to gene start organization, as well as an adaptive process of model structure modification. We used multiple tests to assess accuracy of the new tool as well as several other current gene finders. A self-training tool for gene annotation in eukaryotic genomes GeneMark-ES, has been constantly updated and has been used in a number of genome projects conducted by the DOE Joint Genome Institute and the Broad Institute since 2007. This tool was recently extended to fully automated GeneMark-ET [2] integrating information on RNA-Seq reads mapped to the genome. Another extension, GeneMark-EP uses genomic footprints of homologous proteins. Both algorithms carry similar approaches for filtering out errors in algorithms of processing external evidence. The metagenomic gene finder, MetaGeneMark [3] has been employed in IMG/M at DOE Joint Genome Institute for metagenome annotation. This tool was further developed to call genes in fungal metagenomes. Finally, BRAKER1, a pipeline for unsupervised RNA-Seq based genome annotation combines advantages of GeneMark-ET and AUGUSTUS [4]. All the tools described above can be applied for analysis of newly assembled NGS genomes without any additional preparation steps.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"35 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83776429","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

BAMSE: Bayesian model selection for tumor phylogeny inference among multiple tumor samples BAMSE:用于多个肿瘤样本间肿瘤系统发育推断的贝叶斯模型选择

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114293

Hosein Toosi, A. Moeini, I. Hajirasouliha

{"title":"BAMSE: Bayesian model selection for tumor phylogeny inference among multiple tumor samples","authors":"Hosein Toosi, A. Moeini, I. Hajirasouliha","doi":"10.1109/ICCABS.2017.8114293","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114293","url":null,"abstract":"Intra-tumor heterogeneity is believed to be a major source of confounding analysis and treatment resistance. In this research we introduce BAMSE, a Bayesian model based tool for intra-tumor heterogeneity analysis of bulk tumor sequencing results across multiple samples. BAMSE takes as input a list of somatic mutations and their corresponding reference and variant read counts, clusters these mutations into sub-clones and outputs a list of high probability evolutionary trees, each representing a scenario for clonal evolution of the tumor. We use a Hierarchical Uniform Prior for clustering of mutations into subclones and a uniform prior over tree topologies describing the evolutionary relations between them. This way, all configurations that have equal number of subclones are assigned equal prior, leading to an unbiased model selection. We show that for this model, to calculate the posterior for a model with K subclones, we need to calculate an integral over a K-1 simplex. These integrals are calculated numerically using a series of convolutions, allowing fast and accurate calculation of the posterior probability. Finally, for the selected high-probable models, we use convex optimization to determine the maximum likelihood cell fraction for each subclone. Both synthetic and experimental data are used to benchmark BAMSE against existing tools for analysis of intra-tumor heterogeneity of bulk samples. Unbiased model selection, accurate calculation of subclonal cell fractions and short runtimes are the main advantages of BAMSE. We will extend BAMSE to account for copy number variations in a future work. BAMSE is available at https://github.com/HoseinT/BAMSE.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"4 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88631462","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

A computational method to aid in the design and analysis of single cell RNA-seq experiments 帮助设计和分析单细胞RNA-seq实验的计算方法

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114311

Douglas Abrams, Parveen Kumar, K. R. K. Murthy, J. George

{"title":"A computational method to aid in the design and analysis of single cell RNA-seq experiments","authors":"Douglas Abrams, Parveen Kumar, K. R. K. Murthy, J. George","doi":"10.1109/ICCABS.2017.8114311","DOIUrl":"https://doi.org/10.1109/ICCABS.2017.8114311","url":null,"abstract":"The advent of single-cell RNA sequencing (scRNA-seq) has given researchers the ability to study transcriptomic activity within individual cells, rather than across hundreds or thousands of cells as with bulk RNA-seq techniques. The greater precision afforded by scRNA-seq identifies mutations and gene expression landscapes private to individual cells or subpopulations, enabling us to determine novel cell types and understand biological systems at greater resolution. Usually biological insights are obtained through the use of unsupervised learning methods on high dimensional single-cell datasets. These methods have to take into account the technical noise structure and distributional properties of scRNA-seq datasets for optimal results. Because the optimal set of analysis methods is different between datasets and there is a wide selection of methods available, it can be both daunting and challenging to design an effective scRNA-seq experiment. In this study, we propose an empirical approach to design a better scRNAseq experiment and answer unresolved biological questions. The tool helps to determine the number of single cells to be profiled and the optimal computational pipeline based on the characteristics of the tissue system under study. Using simulated datasets, we demonstrate that the number of single cells required and the appropriate analysis strategy depend on the characteristics of the cell types under investigation1.","PeriodicalId":89933,"journal":{"name":"IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences","volume":"40 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79660940","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

Efficient filtering algorithm for detection of genetic similarity between large genomic datasets 大型基因组数据集间遗传相似性检测的高效过滤算法

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114318

Viachaslau Tsyvina, David S. Campo, Seth Sims, A. Zelikovsky, Y. Khudyakov, P. Skums

引用次数: 0

Assessment of HCV infection stage as recent or chronic using multi-parameter analysis and machine learning 使用多参数分析和机器学习评估HCV感染分期为近期或慢性

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences Pub Date : 2017-10-01 DOI: 10.1109/ICCABS.2017.8114316

P. Baykal, Alexander Artyomenko, S. Ramachandran, Y. Khudyakov, A. Zelikovsky, P. Skums

引用次数: 4