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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Soyemi, Jumoke Isewon, Itunuolwa Ogunlana, Olubanke Solomon, Rotimi Oyelade, Jelili Adebiyi, Ezekiel Fed Polytech Dept Comp Sci Ilaro Nigeria Covenant Univ Dept Comp & Inform Sci Ota Nigeria Covenant Univ Dept Biol Sci Ota Nigeria

ISBN: (纸本)9781538613993

In this study, differentially expressed genes for the trophozoite and schizont stages of Plasmodium falciparum's life cycle were extracted from a time series RNA-Seq gene expression experiment. About 28% of the 5,270 genes used in the experiment were found to show significant expression at these stages. Enrichment analysis using Gene Ontology implicated a total of 62 functions as highly enriched from the list of differentially expressed genes (DEGs). Some include;protein targeting to membrane, protein import, establishment of proteins localization to organelle, ribonucleic protein complex, nucleotide-excision repair and processes related to the mitochondria. A protein interaction network (PIN) for the DEGs at the schizont stage was extracted from experimental data of protein-protein interactions and supplemented with data from a protein interaction database. We predicted a number of protein-protein interactions in Plasmodium falciparum that may be implicated in invasion of the human red blood cells (RBCs). Some of these predictions are consistent with those from previous studies while quite a number of them are novel. We also identified 16 protein complexes from the PIN using the Molecular Complex Detection (MCODE) algorithm. The functional enrichment of the identified protein complexes showed functions related to gene expression, translation, RNA transport and metabolic/biological processes which have been identified to be important in the invasion process. The result from this study is meant to provide better insight into disease at hand.

关键词： RNA-Seq gene expression data Plasmodium falciparum Gene Ontology MCODE Algorithm Differentially Expressed genes

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Cross-validation and cross-study validation of kidney cancer with machine learning and whole exome sequences from the National Cancer Institute

Cross-validation and cross-study validation of kidney cancer...

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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Aljouie, Abdulrhman Patel, Nihir Roshan, Usman King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Dept Biostat & Bioinformat Riyadh 11426 Saudi Arabia Mt Sinai Hosp Icahn Sch Med Hess Ctr Sci & Med Dept Genet & Genom Sci New York NY 10029 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781538613993

Accurate cancer risk prediction from genetic and environment variables is a key problem in medicine. One approach is to use somatic mutations which could potentially be used in early detection and prevention. SNP based studies are the most common ones utilizing this approach, however most studies lack a cross-study validation component across at least two independent studies. Here we explore the cross-validation and cross-study validation of predicting kidney cancer case and controls with SNPs obtained from whole exome sequences at the National Cancer Institute. From the Genomics Data Commons portal we obtained aligned whole exome sequences of two different kidney cancer studies: 110 cases and controls of KIRP for renal papillary cell carcinoma and 34 cases and controls of KICH for kidney chromophobe cell carcinoma. We performed a rigorous quality control procedure to obtain SNPs and rank them with feature selection. On top ranked SNPs we find the support vector machine to obtain a cross-validation accuracy of 71% (with 10 SNPs) and 72% (with 20 SNPs) in KIRP and KICH respectively. We then learn a model on KIRP and with 10 SNPs achieve an accuracy of 66% on the KICH samples. Our work shows that we can predict kidney chromophobe carcinoma from a kidney papillary carcinoma dataset with better than a random classification which would have 50% accuracy. In continuing work we are expanding these sample sizes and extending crossstudy to other kidney cancer datasets in the NCI GDC portal.

关键词： Cancer Kidney Support vector machines bioinformatics Genomics Correlation Machine learning

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Deep Sequencing Data Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 482-483页

作者： Ghosh, Bijoy K. Datta, Aniruddha Pal, Ranadip Texas Tech Univ Dept Math & Stat 1108 Mem Circle Lubbock TX 79409 USA Texas A&M Univ Dept Elect & Comp Engn 188 Bizzell St College Stn TX 77843 USA Texas Tech Univ Dept Elect & Comp Engn 1012 Boston Ave Lubbock TX 79409 USA

This paper discussed the recent advances in Deep Sequencing Data Analysis for systems biology research. Deep sequencing technologies have been primarily applied to genomic sequencing but have recently been applied for transcriptomic profiling or mapping histone modifications. Deep Sequencing technology shows clear advantages over existing profiling technologies in terms of amount of sequence coverage, revealing new transcriptomic insights, measurement of expression of different transcript isoforms and accuracy of defining transcription level. However, being a relatively newer method for transcriptomic profiling, standardized approaches for analysis of deep sequencing expression data are still being developed. The analysis and application of deep sequencing data presents enormous challenges in the areas of machine learning, signal processing, systems theory and statistics. The emphasis of the special issue is on the latest computational challenges and finding rigorous and novel engineering approaches to tackle structural and functional systems biology problems using deep sequencing technologies

关键词： Biological Techniques biology Computing Genomics Learning Artificial intelligence Molecular Biophysics RNA Systems Theory Signal Processing Machine Learning Deep Sequencing Expression Data Transcriptomic Insights Sequence Coverage Profiling Technologies Histone Modifications Transcriptomic Profiling Genomic Sequencing Systems biology Research Deep Sequencing Data Analysis Special Issues And Sections Sequential Analysis Bayes Methods computational Modeling Fault Detection Data Analysis Machine Learning Signal Processing Systems Engineering And Theory

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Multithreaded Parallel Sequence Alignment Based on Needleman-Wunsch Algorithm

Multithreaded Parallel Sequence Alignment Based on Needleman...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Veska Gancheva Ivaylo Georgiev Department of Programming and Computer Technology Technical University of Sofia Sofia Bulgaria The Stephan Angeloff Institute of Microbiology Sofia Bulgaria

ISBN: (纸本)9781728146188

Biocomputing and molecular biology are areas that change knowledge and skills for acquisition, storing, management, analysis, interpretation and dissemination of biological information. This requires the utilization of high performance computers and innovative software tools for management of the vast information, as well as deployment of innovative algorithmic techniques for analysis, interpretation and prognostication of data in order to get to insight of the design and validation of life-science experiments. Sequence alignment is an important method in DNA and protein analysis. The paper describes the computational challenges in biological sequence processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for biological sequence alignment. An investigation of the efficiency of sequence alignment based on parallel multithreaded program implementation of Needleman-Wunsch algorithm is presented in this paper. Parallel computational model based on Needleman-Wunsch algorithm is designed. The proposed parallel model is verified by multithreaded parallel program implementation utilizing OpenMP. A number of experiments have been carried out for the case of various data sets and a various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability both in respect to the workload and machine size.

关键词： Heuristic algorithms Proteins computational modeling Dynamic programming bioinformatics Software algorithms

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Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第1期15卷 168-180页

作者： Leale, Guillermo Emilio Baya, Ariel Milone, Diego H. Granitto, Pablo M. Stegmayer, Georgina Consejo Nacl Invest Cient & Tecn Ctr R&D Informat Syst CIDISI RA-3000 Santa Fe Argentina Consejo Nacl Invest Cient & Tecn FICH UNL Res Inst Signals Syst & Computat Intelligence Sin RA-3000 Santa Fe Argentina UNR CONICET French Argentine Int Ctr Informat & Syst Sci CIFASIS RA-2000 Rosario Santa Fe Argentina

Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at http://***/sinc/webdemo/gamma-am/.

关键词： Clustering gene ontology machine learning bioinformatics

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The Evolution of Reinforcement Learning *

The Evolution of Reinforcement Learning *

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ieee symposium Series on computational intelligence (SSCI)

作者： Dean Frederick Hougen Syed Naveed Hussain Shah School of Computer Science University of Oklahoma Norman OK USA MS Office Microsoft Corporation Redmond WA USA

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In both the natural and artificial realms, evolution and reinforcement learning are parallel adaptive processes that work on different scales but with similar feedback mechanisms. This makes combined and coordinated study of these phenomena synergistic. In the natural world, evolution is antecedent to all forms of learning but can, nonetheless, be influenced by them. In artificial intelligence, evolutionary computation and reinforcement learning were initially developed independently and in parallel but numerous studies of their interactions exist. As reinforcement learning gains prominence in machine learning, and as it is integrated into more complex learning systems such as deep neural networks, greater understanding of how it has and could be evolved becomes increasingly relevant. This survey covers major trends in the evolution of reinforcement learning and suggests important directions for future research.

关键词： Evolution (biology) Learning (artificial intelligence) Machine learning Organisms Evolutionary computation Sociology

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Modeling the Population Growth of the Co-cultured Blood Cells Exposed by Microbubble-mediated Ultrasound

Modeling the Population Growth of the Co-cultured Blood Cell...

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ieee symposium Series on computational intelligence (SSCI)

作者： Xinxing Duan Sujia Yin Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ultrasound-based drug delivery inspires rigorous experimental studies on the cell experiments involving microbubble-mediated ultrasound. Yet few tools can be found to accelerate the cellular exposure experiments on the heterogeneous-population-based bioeffects under various conditions. In this study, we propose a 2D model which can be used to simulate the population evolution of the co-cultured cells after microbubble-mediated ultrasound is induced. Two update rules based on the experimental data were proposed and incorporated in the simulation procedures. The bioeffects found in the previous experiment, such as cell cycle arrest, are taken into account in the model to simulate the population growth in a co-cultured setting. This model may provide a predictive tool for the multi-cell type responses to ultrasound-induced perforation and facilitate the future experiment design.

关键词： Sociology Statistics Ultrasonic imaging Cells (biology) computational modeling Blood Biological system modeling

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ieee SSCI 2011 - symposium Series on computational intelligence - CIBCB 2011: 2011 ieee symposium on computational intelligence in bioinformatics and computational biology

IEEE SSCI 2011 - Symposium Series on Computational Intellige...

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2011 ieee symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2011

ISBN: (纸本)9781424498970

The proceedings contain 26 papers. The topics discussed include: predicting coding region candidates in the DNA sequence based on visualization without training;protein secondary structure prediction using BLAST and relaxed threshold rule induction from coverings;regularized linear discriminant analysis and its recursive implementation for gene subset selection;combined covariance model for non-coding RNA gene finding;secondary structure element voting for RNA gene finding;comparative analysis of machine learning techniques for the prediction of LogP;illumination field estimation through background detection in optical microscopy;an incremental method for mosaicing of optical microscope imagery;tackling the challenging motif problem through hybrid particle swarm optimized alignment clustering;and comparative study of protein interaction networks for virulence factors of co-existing bacteria responsible for nosocomial infections.

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NAHAL-Flex: A Numerical and Alphabetical Hinge Detection Algorithm for Flexible Protein Structure Alignment

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第3期15卷 934-943页

作者： Fotoohifiroozabadi, Samira Mohamad, Mohd Saberi Deris, Safaai Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Locked Bag 01 Kota Baharu 16300 Kelantan Malaysia

Flexible proteins are proteins that have conformational changes in their structures. Protein flexibility analysis is critical for classifying and understanding protein functionality. For that analysis, the hinge areas where proteins show flexibility must be detected. To detect the location of the hinges, previous methods have utilized the three-dimensional (3D) structure of proteins, which is highly computational. To reduce the computational complexity, this study proposes a novel text-based method using structural alphabets (SAs) for detecting the hinge position, called NAHAL-Flex. Protein structures were encoded to a particular type of SA called the protein folding shape code (PFSC), which remains unaffected by location, scale, and rotation. The flexible regions of the proteins are the only places in which letter sequences can be distorted. With this knowledge, it is possible to find the longest alignment path of two letter sequences using a dynamic programming (DP) algorithm. Then, the proposed method looks for regions where the alphabet sequence is distorted to find the most probable hinge positions. In order to reduce the number of hinge positions, a genetic algorithm (GA) was utilized to find the best candidate hinge points. To evaluate the method's effectiveness, four different flexible and rigid protein databases, including two small datasets and two large datasets, were utilized. For the small dataset, the NAHAL-Flex method was comparable to state-of-the-art structural flexible alignment methods. The result for the large datasets show that NAHAL-Flex outperforms some well-known alignment methods, e.g., DaliLite, Matt, DeepAlign, and TM-align;the speed of NAHAL-Flex was faster and its result was more accurate than the other methods.

关键词： Protein structure alignment protein flexibility linear alignment structure comparison

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Normative and Fuzzy Aspects of Medical AI

Normative and Fuzzy Aspects of Medical AI

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Aspassia Daskalopulu Eleutherios Tsoukalas Dimitrios Bargiotas University of Thessaly Volos Greece

ISBN: (纸本)9781728146188

Normative and fuzzy aspects of Medical AI are examined and a framework is presented for the purpose of designing architectures that can accommodate the lifelong growth of data and knowledge pertaining to an individual's health, together with relevant privileges and permissions for access, growth and utilization of such a system. Considering disease through the normativistic perspective advanced by Sadegh-Zadeh, an action-theoretical analysis is adopted suitable for Medical AI. In this approach diseases are viewed as social artifacts mapped into deontic constructs. In a sense, they are action provoking states of affairs amenable to fuzzy computational approaches that in the past have found broad applicability to industrial process control.

关键词： Process control Computer architecture History Artificial intelligence Deductive databases Medical diagnostic imaging Diseases

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