检索结果-内蒙古大学图书馆

A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli 12th

A Hybrid of Simple Constrained Artificial Bee Colony Algorit...

引用

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

作者： Hon, Mei Kie Mohamad, Mohd Saberi Salleh, Abdul Hakim Mohamed Choon, Yee Wen Remli, Muhammad Akmal Ismail, Mohd Arfian Omatu, Sigeru Manuel Corchado, Juan Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Inst Artificial Intelligence & Big Data City Campus Kota Baharu 16100 Kelantan Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli CampusLock Bag 100 Jeli 17600 Kelantan Malaysia Univ Malaysia Pahang Fac Comp Syst & Software Engn Soft Comp & Intelligent Syst Res Grp Kuantan 26300 Pahang Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Univ Salamanca BISITE Res Grp Biomed Res Inst Salamanca Salamanca Spain

ISBN: (纸本)9783319987026;9783319987019

In the past decades, metabolic engineering has received great attention from different sectors of science due to its important role in enhancing the over expression of the target phenotype by manipulating the metabolic pathway. The advent of metabolic engineering has further laid the foundation for computational biology, leading to the development of computational approaches for suggesting genetic manipulation. Previously, conventional methods have been used to enhance the production of lactate and succinate in E. coli. However, these products are always far below their theoretical maxima. In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. The hybrid algorithm employed the Simple Constrained Artificial Bee Colony (SCABC) algorithm, using swarm intelligence as an optimization algorithm to optimize the objective function, where lactate and succinate productions are maximized by simulating gene knockout in E. coli. In addition, Flux Balance Analysis (FBA) is used as a fitness function in the SCABC algorithm to assess the growth rate of E. coli and the productivity of lactate and succinate. As a result of the research, the gene knockout list which induced the highest production of lactate and succinate is obtained.

关键词： Gene knockout strategies Escherichia coli Lactate Succinate Simple Constrained Artificial Bee Colony Flux Balance Analysis computational intelligence

来源：评论

学校读者我要写书评

暂无评论

Bioinformatic insights from metagenomics through visualization

Bioinformatic insights from metagenomics through visualizati...

引用

ieee computational Systems bioinformatics conference

作者： Havre, SL Webb-Robertson, BJ Shah, A Posse, C Gopalan, B Brockman, FJ Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)0769523447

Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. Systems-level analysis requires the integration and fusion of such data, typically through advanced statistics and mathematics. Visualization is a complementary computational approach that supports integration and analysis of complex data or its derivatives. We present a bioinformatics visualization prototype, Juxter, which depicts categorical information derived from or assigned to these diverse data for the purpose of comparing patterns across categorizations. The visualization allows users to easily, discern correlated and anomalous patterns in the data. These patterns, which might not be detected automatically by, algorithms, may, reveal valuable information leading to insight and discovery. We describe the visualization and interaction capabilities and demonstrate its utility in a new field, metagenomics, which combines molecular biology and genetics to identify and characterize genetic material from multi-species microbial samples.

关键词： bioinformatics visualization metagenomics categorical nominal

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

A Simplified Description of Child Tables for Sequence Similarity Search

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第6期15卷 2067-2073页

作者： Frith, Martin C. Shrestha, Anish M. S. Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 3058567 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 1138654 Japan Waseda Univ CBBD OIL AIST Tokyo 1698050 Japan

Finding related nucleotide or protein sequences is a fundamental. diverse, and incompletely-solved problem in bioinformatics. It is often tackled by seed-and-extend methods, which first find "seed" matches of diverse types, such as spaced seeds, subset seeds, or minimizers. Seeds are usually found using an index of the reference sequence(s), which stores seed positions in a suffix array or related data structure. A child table is a fundamental way to achieve fast lookup in an index, but previous descriptions have been overly complex. This paper aims to provide a more accessible description of child tables, and demonstrate their generality: they apply equally to all the above-mentioned seed types and more. We also show that child tables can be used without LCP (longest common prefix) tables, reducing the memory requirement.

关键词： biology and genetics indexing methods data structures arrays trees

来源：评论

学校读者我要写书评

暂无评论

Prediction of Five-Year Survival Rate for Rectal Cancer Using Markov Models of Convolutional Features of RhoB Expression on Tissue Microarray

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 3195-3204页

作者： Pham, Tuan D. Prince Mohammad Bin Fahd Univ Ctr Artificial Intelligence Khobar 31952 Saudi Arabia

The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).

关键词： Rectal cancer RhoB protein deep learning convolutional neural networks Markov models linear predictive coding vector quantization

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

Keynote address: The role of algorithmic research in computational genomics 2

Keynote address: The role of algorithmic research in computa...

引用

2nd International computational Systems bioinformatics conference

作者： Karp, RM Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)0769520006

in the early 1990s, after more than three decades of studying algorithms within the framework of theoretical computer science, I shifted my focus to algorithmic problems arising in genomics. There is a fundamental difference between the views of algorithms in the two fields: in theoretical computer science the input-output behavior of an algorithm is rigorously specified in advance, whereas in computational biology an algorithm is merely a vehicle for discovering Nature's ground truth. In order to be effective in computational genomics I have had to radically change my approach to research. On the occasion of this keynote address I will share some of the lessons I have learned, in the hole of making the way easier for computer scientists and mathematicians entering this field. These lessons will be encapsulated in a list of aphorisms, accompanied by illustrative examples.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Editorial: computational Genomics and Molecular Medicine for Emerging COVID-19

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1227-1229页

作者： Wei, Dong-Qing Kaushik, Aman Chandra Selvaraj, Gurudeeban Pan, Yi Shanghai Jiao Tong Univ State Key Lab Microbial Metab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Jiangnan Univ Wuxi Sch Med Wuxi 214122 Jiangsu Peoples R China Concordia Univ Ctr Res Mol Modeling CERMM Montreal PQ H4B 1R6 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

关键词： Special Issues And Sections COVID 19 Pandemics Genomics Drugs bioinformatics Machine Learning RNA Proteomics Molecular biology Coronaviruses

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

Graph mining of networks from genome biology

Graph mining of networks from genome biology

引用

7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Chin Jr., George Nakamura, Grant C. Chavarria, Daniel G. Sofia, Heidi J. Computational Sciences and Mathematics Division Pacific Northwest National Laboratory Richland WA 99352 United States

ISBN: (纸本)1424415098

We are developing an advanced toolkit for biological networks, using problems from genome biology to drive this work. We now share our experiences in graph analysis and visualization of microbial genome networks using a collection of new and existing graph mining tools and techniques. We address three key problems in genome biology: the organization of complete genome protein networks, feature extraction across chromosomes in microbial strains, and hierarchical structure of protein families and superfamilies. ©2007 ieee.

关键词： biology

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：