检索结果-内蒙古大学图书馆

Classification of Patients with Coronary Microvascular Dysfunction

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第2期17卷 704-711页

作者： Fodeh, Samah J. Li, Taihua Jarad, Haya Safdar, Basmah Yale Univ Yale Ctr Med Informat New Haven CT 06510 USA Depaul Univ Sch Comp Chicago IL 60604 USA Univ Connecticut Sch Engn Storrs CT 06269 USA Yale Univ Dept Emergency Med New Haven CT 06519 USA

While coronary microvascular dysfunction (CMD) is a major cause of ischemia, it is very challenging to diagnose due to lack of CMD-specific screening measures. CMD has been identified as one of the five priority areas of investigation in a 2014 National Research Consensus conference on Gender-Specific Research in Emergency Care. In this study, we utilized methods from machine learning that leverage structured and unstructured narratives in clinical notes to detect patients with CMD. We have shown that structured data are not sufficient to detect CMD and integrating unstructured data in the computational model boosts the performance significantly.

关键词： Solid modeling Data models Pain Tools computational modeling Natural language processing Machine learning Health machine learning medicine natural language processing record classification text mining

来源：评论

学校读者我要写书评

暂无评论

3D Genome Reconstruction with ShRec3D+and Hi-C Data

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 460-468页

作者： Li, Jiangeng Zhang, Wei Li, Xiaodan Beijing Univ Technol Coll Elect & Control Engn Beijing 100022 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100022 Peoples R China

Hi-C technology, a chromosome conformation capture (3C) based method, has been developed to capture genome-wide interactions at a given resolution. The next challenge is to reconstruct 3D structure of genome from the 3C-derived data computationally. Several existing methods have been proposed to obtain a consensus structure or ensemble structures. These methods can be categorized as probabilistic models or restraint-based models. In this paper, we propose a method, named ShRec3D+, to infer a consensus 3D structure of a genome from Hi-C data. The method is a two-step algorithm which is based on ChromSDE and ShRec3D methods. First, correct the conversion factor by golden section search for converting interaction frequency data to a distance weighted graph. Second, apply shortest-path algorithm and multi-dimensional scaling (MDS) algorithm to compute the 3D coordinates of a set of genomic loci from the distance graph. We validate ShRec3DI accuracy on both simulation data and publicly Hi-C data. Our test results indicate that our method successfully corrects the parameter with a given resolution, is more accurate than ShRec3D, and is more efficient and robust than ChromSDE.

关键词： 3D genome reconstruction interaction frequencies multi-dimensional scaling (MDS) a conversion factor a distance weighted graph

来源：评论

学校读者我要写书评

暂无评论

computational Study of Particle Deposition in Patient Specific Geometries

Computational Study of Particle Deposition in Patient Specif...

引用

ieee 13th International conference on bioinformatics and Bioengineering (BIBE)

作者： Pilou, Marika Skiadopoulos, Anastasios Makris, Evangelos Neofytou, Panagiotis Housiadas, Christos NCSR Demokritos INRASTES Thermal Hydraul & Multiphase Flow Lab Aghia Paraskevi 15310 Greece

ISBN: (纸本)9781479931637

The present work focuses on the study of particle deposition in segments of the cardiovascular system. In particular, the geometry of an iliac bifurcation is reconstructed from medical imaging data and the flow fields of both blood and particles are obtained using computational Fluid Particle Dynamics techniques. Particle convection, diffusion and inertia are taken into account in the simulations. The numerical experiments indicate that diffusion dominates deposition and only 10% of the injected particles deposit in the bifurcation. Both blood flow field and the characteristics of the patient specific geometry influence the particle deposition sites. Overall, the proposed methodology could become a useful tool for the design and optimization of biomedical applications.

关键词： bifurcation biological fluid dynamics cardiovascular system computational fluid dynamics image reconstruction medical image processing

来源：评论

学校读者我要写书评

暂无评论

Multiple worlds model for motif discovery

Multiple worlds model for motif discovery

引用

2012 ieee Symposium on computational intelligence and computational biology, CIBCB 2012

作者： Brown, Joseph Alexander School of Computer Science University of Guelph Guelph ON Canada

ISBN: (纸本)9781467311892

In this study we look at a novel evolutionary technique known as the multiple worlds model for unsupervised classification of sequences via evolved motifs. This evolutionary algorithm uses the biological inspirations of species and species extinction as features modeled in the evolution in order to provide classifiers where the number of classes is not known a priori. In the multiple worlds model a number of populations which do not interbreed compete in fitness evaluation. Sequence motifs are small, biologically significant DNA/RNA or amino acid segments. They are represented as strings of symbols and wild cards. The model works well to locate classification motifs for sequences whose classes have statistical deviations such as those with a GC content difference or those created from differing Self-Driving Markov models. The creation of such classifiers will allow biologists to examine large sets of sequences in order to discover significant features. © 2012 ieee.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Using Machine Learning to Predict SP1 factor binding and non-binding sites on Chromosome1 1

Using Machine Learning to Predict SP1 factor binding and non...

引用

International conference on Big Data Analytics and computational intelligence (ICBDAC)

作者： Sultania, Dewang Birla Inst Technol Mesra Ranchi Bihar India

ISBN: (纸本)9781509064007

Transcription Factor Sp1, also known as specificity protein 1* is a protein that in humans is encoded by the SP1 gene. Using Machine Learning algorithms like Logistic Regression, Linear Discriminant Analysis-Nearest Neighbours, Decision Trees, Gaussian Naive Bayes, Support Vector Machines and some ensemble algorithms like AdaBoost, Gradient Boosting, Random Forests and Extra Trees here a classifier is built which analyses the sequence which is 14 nucleobase length and predicts whether the chromosome1 is a SP1 binding site or not.

关键词： Machine Learning k-Nearest Neighbors bioinformatics Chromosomes Naive Bayes Support Vector Machine (SVM) Adaboost Random Forest Decision Trees

来源：评论

学校读者我要写书评

暂无评论

Feature Selection and Polydispersity Characterization for QSPR Modelling: Predicting a Tensile Property 12th

Feature Selection and Polydispersity Characterization for QS...

引用

12th International conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Cravero, Fiorella Schustik, Santiago Jimena Martinez, Maria Barranco, Carlos D. Diaz, Monica F. Ponzoni, Ignacio UNS CONICET PLAPIQUI Bahia Blanca Buenos Aires Argentina CIC Bahia Blanca Buenos Aires Argentina UNS CONICET Dept Ciencias & Ingn Comp Inst Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina Pablo de Olavide Univ Intelligent Data Anal DATAi Div Comp Sci Seville 41013 Spain UNS Dept Ingn Quim Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783319987026;9783319987019

QSPR (Quantitative Structure-Property Relationship) models proposed in Polymer Informatics typically use reduced computational representations of polymers for avoiding the complex issues related with the polydispersion of these industrial materials. In this work, the aim is to assess the effect of this oversimplification in the modelling decisions and to analyze strategies for addressing alternative characterizations of the materials that capture, at least partially, the polydispersion phenomenon. In particular, a cheminformatic study for estimating a tensile property of polymers is presented here. Four different computational representations are analyzed in combination with several machine learning approaches for selecting the most relevant molecular descriptors associated with the target property and for learning the corresponding QSPR models. The obtained results give insight about the limitations of using oversimplified representations of polymers and contribute with alternative strategies for achieving more realistic models.

关键词： QSPR Polydispersity Artificial intelligence Feature selection

来源：评论

学校读者我要写书评

暂无评论

Support Vector Machine Prediction of Drug Solubility on GPUs 3

Support Vector Machine Prediction of Drug Solubility on GPUs

引用

3rd International Work-conference on bioinformatics and Biomedical Engineering (IWBBIO)

作者： Cano, Gaspar Garcia-Rodriguez, Jose Orts-Escolano, Sergio Pena-Garcia, Jorge Kumar-Yadav, Dharmendra Perez-Garrido, Alfonso Perez-Sanchez, Horacio Univ Alicante Dept Comp Technol E-03080 Alicante Spain Catholic Univ Murcia UCAM Comp Sci Dept Bioinformat & High Performance Comp Res Grp BIO H E-30107 Murcia Spain Univ Delhi Dept Chem Delhi I110007 India

ISBN: (纸本)9783319164809;9783319164793

The landscape in the high performance computing arena opens up great opportunities in the simulation of relevant biological systems and for applications in bioinformatics, computational biology and computational Chemistry. Larger databases increase the chances of generating hits or leads, but the computational time needed increases with the size of the database and with the accuracy of the Virtual Screening (VS) method and the model. In this work we discuss the benefits of using massively parallel architectures for the optimization of prediction of compound solubility using computational intelligence methods such as Support Vector Machines (SVM) methods. SVMs are trained with a database of known soluble and insoluble compounds, and this information is being exploited afterwards to improve VS prediction. We empirically demonstrate that GPUs are well-suited architecture for the acceleration of computational intelligence methods as SVM, obtaining up to a 15 times sustained speedup compared to its sequential counterpart version.

关键词： SVM GPU CUDA bioinformatics computational biology

来源：评论

学校读者我要写书评

暂无评论

computational Management of Alignment of Multiple Protein Sequences Using ClustalW 1st

Computational Management of Alignment of Multiple Protein Se...

引用

1st International conference on Sustainable Technologies for computational intelligence (ICTSCI)

作者： Sharma, Riddhi Dubey, Sanjay Kumar Amity Univ Noida Uttar Pradesh India

ISBN: (纸本)9789811500299;9789811500282

The astounding and multidisciplinary field of bioinformatics has had its origin in the year 1900, a decade before the concept of DNA sequencing became feasible and applicable to researchers. The era of the "new biology" that has a subtle link with the modern-day computers emerged and was accompanied by the birth as well as the development of other streams of sciences such as bioinformatics (it conceptualizes biology in reference to molecules and then applies "informatics techniques" (derived from computer science in this paper) to organize and understand the information that is related to these molecules. The several categories in bioinformatics focus on genomics (gene study), proteomics (protein study: which will be discussed in this paper), metabolomics (study of metabolic pathway within a cell), etc. and computational biology. bioinformatics focuses on the application of IT and statistics. It is very significant to note here that bioinformatics does not have to do anything in the real world. Rather, it creates a virtual image of our living system having computerized biological knowledge, sequence, and structural information fed in the CPU and by using virtual-based techniques like mathematical modeling and through stimulation that helps in basic principles leading to its practical application. In bioinformatics, a sequence is subjected (or susceptible towards) to certain range of analytical methods in order to understand its function, or features, or structure, or evolution. Analysis of sequences can be very efficiently used to assign roles to certain genes and proteins as a result of the study of the various similarities between the compared sequences. In this research paper, comparison and alignment of multiple protein sequences are provided. It will help to generate a phylogenetic tree and also draw the relationships between the aligned sequences.

关键词： Multiple sequence alignment Dynamic programming Pairwise alignment ClustalW Phylogenetic tree

来源：评论

学校读者我要写书评

暂无评论

computational Prediction of Toxicity

Computational Prediction of Toxicity

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Mishra, Meenakshi Fei, Hongliang Huan, Jun Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA

ISBN: (纸本)9781424483075

As the number of new chemicals developed and being used keep adding every year, having the toxic profiles of each chemical becomes a daunting challenge. To meet this information gap, EPA suggested that certain in vitro assays and computational methods, which predict toxicity related information in much lesser time and cost than traditional in vivo methods, may be used. In this paper, we use computational techniques to use results from certain in vitro assays applied on 309 chemicals (whose toxicity profile is readily available) along with the molecular descriptors and other computed physical-chemical properties of the chemicals to predict the toxicity caused by chemical at a particular endpoint. The dataset is available from EPA TOXCAST group online. We show that Random Forest and Naive Bayes have a good performance on this dataset. We also show that using small and related trees in random forest help to further improve the performance.

关键词： Bayes methods EPA TOXCAST group biological techniques biology computing chemical toxic profile chemistry computing computational methods in vitro assays molecular descriptors naive Bayes method physical-chemical properties random forest method toxicity co

来源：评论

学校读者我要写书评

暂无评论

Robust algorithm for finding weak motifs

Robust algorithm for finding weak motifs

引用

2005 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '05

作者： Yang, Xiao Rajapakse, Jagath C. IEEE Biolnformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)0780393872

The Challenge Problem posed by Pevzner et al. showed that special algorithms are needed to detect weak motifs in bio-sequences, where the classical approaches, such as MEME and Gibbs Sampler, fail. Though several algorithms have since been developed to solve the weak motif recognition problem, their focus has been on exact datasets and their performances show poor tolerance to the noisy datasets, i.e., for datasets bearing sequences without any motif instances. We propose a novel approach to find weak motifs that is robust to noise in the datasets. The experiments with synthetic datasets show that our algorithm has less running time and higher accuracy in detecting weak motifs over the existing approaches and is more robust to the presense of noise. The application of the algorithm on some promoter datasets from yeast genomes found previously-proven binding sites. © 2005 ieee.

关键词： Algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：