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Accurately Predicting Transcription Start Sites Using Logitlinear Model and Local Oligonucleotide Frequencies

7th International conference on Intelligent Computing (ICIC)

作者： Wang, Jia Ma, Chuang Zhou, Dao Zhang, Libin Zhou, Yanhong Huazhong Univ Sci & Technol Hubei Bioinformat & Mol Imaging Key Lab Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783642245527;9783642245534

In this study, we construct a transcription start site (TSS) prediction model using the logitlinear model and the genomic context features mined in promoter regions. We also develop a computational program named ProKey that is able to accurately predict TSSs in long DNA sequences. Performance evaluation results on the whole human genome show that ProKey could achieve 71.2% sensitivity and 76.3% specificity at the resolution level of 2000bp. Further comparison results exhibit that the correlation coefficient (CC) value of ProKey is higher than that of DragonGSF and Eponine.

关键词： Transcription start site Promoter Oligonucleotide frequency Context feature Logitlinear model computational biology bioinformatics

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Neural Network based Cloud computing Platform for bioinformatics

Neural Network based Cloud computing Platform for Bioinforma...

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9th Annual conference on ieee Long Island Systems, Applications and Technology (LISAT)

作者： Ikram, Ataul Aziz Ibrahim, Salma Sardaraz, Muhammad Tahir, Muhammad Bajwa, Hassan Bach, Christian Iqra Univ Dept Comp & Technol Islamabad 44000 Pakistan Univ Bridgeport Dept Biomed Engn Bridgeport CT 06604 USA Univ Bridgeport Dept Elect Engn Bridgeport CT 06604 USA

ISBN: (纸本)9781467362443

Advances in genomics, proteomics, and bioinformatics have revolutionized the drug discovery and drug development. computational systems biology, computational bioinformatics, and many biomedical applications are also growing at a rapid pace with an increasing demand for processing power. Hardware clusters and grid computing solutions are approached to fulfill the high demand for the processing power. The grid clusters approach proved success but introduced the need of frameworks to hide the complexity of parallel programming and enable the programmer to focus on the application logic. In this paper we present a novel cloud computing based neural network framework. We will further present results of implementation of Multiple Sequence Alignment (MSA) algorithms in cloud architecture. The experiments show optimal results in terms of computational complexity and preserve accuracy as well.

关键词： component Artificial Neural Networks Cloud Computing bioinformatics

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Considerations in making microarray cross-platform correlations

Considerations in making microarray cross-platform correlati...

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2005 ieee computational Systems bioinformatics conference, Workshops and Poster Abstracts

作者： Kollegal, Manohar Adak, Sudeshna Shippy, Richard Sendera, Timothy Computational Biology and Biostatistics Lab. GE Global Research Bangalore India GE Healthcare Bio-Sciences Chandler AZ United States

ISBN: (纸本)0769524427

Comparison and integration of expression data derived from diverse microarray platforms is challenging. Factors affecting cross-platform comparison of microarray data include the choice of normalization method used, annotation differences, presence of splice variants, RNA degradation and probe distance from 3' end. A thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations is described here. © 2005 ieee.

关键词： Data reduction

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Extended Ramp Goal Module: Low-Cost Behaviour Arbitration for Real-Time Controllers based on Biological Models of Dopamine Cells

Extended Ramp Goal Module: Low-Cost Behaviour Arbitration fo...

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ieee conference on computational intelligence and Games (CIG)

作者： Gaudl, Swen E. Bryson, Joanna J. Univ Bath Dept Comp Sci Bath BA2 7AY Avon England

ISBN: (纸本)9781479935475

The current industrial focus in virtual agents and digital games is on complex systems that more accurately simulate the real world, including cognitive characters. This trend introduces a multitude of control parameters generally accompanied by high computational costs. The resulting complexity limits the applicability of AI in these domains. One solution to this problem is to focus on light-weight flexible AI architectures which can be simultaneously generated, controlled and run in parallel. The resulting systems should then be able to control individual game characters, scaling up to large numbers of characters, forming even complex social systems. Here we contribute one element of such a system: a light-weight systems-engineering approach for enriching behaviour arbitration in action selection. Our mechanism-ERGo-improves high-level goal arbitration in existing light-weight action selection mechanisms. ERGo provides easy and reliable non-deterministic control of goal switching, activation and inhibition, allowing natural behaviour maintenance. This mechanism can aid agent design in cases where static, linear, predefined priorities are undesirable. The model underlying our approach is biomimetic, based on neuro-cognitive research on the dopaminic cells responsible for controlling goal switching and maintenance in the mammalian brain. We demonstrate and evaluate our mechanism in a real-time, game-like simulation environment, using a previously-published system as a baseline for comparison. We demonstrate that ERGo is effective, and betters the previous approach.

关键词： biology Feeds Switches Switches Fodder FEEDS Biological Models Arbitrating Biological Science Disciplines biology real-time controller timing controller or element Artificial intelligence Artificial Insemination Simulated Environment control parameter

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Classification of Caenorhabditis Elegans Locomotion Behaviors With Eigenfeature-Enhanced Long Short-Term Memory Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第1期20卷 206-216页

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

The free-living nematode Caenorhabditis elegans is an ideal model for understanding behavior and networks of neurons. Experimental and quantitative analyses of neural circuits and behavior have led to system-level understanding of behavioral genetics and process of transformation fromsensory integration in stimulus environments to behavioral outcomes. The ability to differentiate locomotion behavior between wild-type and mutant Caenorhabditis elegans strains allows precise inference on and gaining insights into genetic and environmental influences on behaviors. This paper presents an eigenfeature-enhanced deep-learning method for classifying the dynamics of locomotion behavior of wild-type and mutant Caenorhabditis elegans. Classification results obtained frompublic benchmark time-series data of eigenworms illustrate the superior performance of the newmethod over several existing classifiers. The proposed method has potential as a useful artificial-intelligence tool for automated identification of the nematode wormbehavioral patterns aiming at elucidating molecular and genetic mechanisms that control the nervous system.

关键词： Locomotion behavior nervous system time series deep learning nonlinear dynamics eigenvalues

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computational intelligence Methods for bioinformatics and Biostatistics 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Massimo Bartoletti Annalisa Barla Andrea Bracciali Gunnar W. Klau Leif Peterson Alberto Policriti Roberto Tagliaferri

ISBN: (数字)9783030141608

ISBN: (纸本)9783030141592

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Meeting on computational. intelligence Methods for bioinformatics and Biostatistics, CIBB 2017, held in Cagliari, Italy, in September 2017.;The 19 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers deal with the application of computational intelligence to open problems in bioinformatics, biostatistics, systems and synthetic biology, medical informatics, computational approaches to life sciences in general.

关键词： computational biology/bioinformatics Artificial intelligence Mathematical Logic and Formal Languages Algorithm Analysis and Problem Complexity Computing Milieux

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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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Guest Editorial Guest Editorial for the 20th Asia Pacific bioinformatics conference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第6期21卷 1601-1603页

作者： Lam, Su Datt Yam, Wai Keat Chen, Yi-Ping Phoebe Univ Kebangsaan Malaysia Dept Appl Phys Bangi 43600 Selangor Malaysia IMU Univ Sch Business & Technol Dept Digital Hlth & Hlth Informat Kuala Lumpur 57000 Malaysia La Trobe Univ Dept Comp Sci & Informat Technol Bundoora Vic 3086 Australia

The four papers in this special section were presented at the 20th Asia Pacific bioinformatics conference (APBC), which was held in Malaysia 26-28 April 2022.

关键词： Special Issues And Sections Meetings bioinformatics

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Analyzing genomic features with predictive chromatin interaction models: A comprehensive evaluation 9

Analyzing genomic features with predictive chromatin interac...

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9th ieee International conference on bioinformatics and computational biology, ICBCB 2021

作者： Kou, Yi Zhao, Daniel Molecular and Computational Biology University of Southern California Los AngelesCA United States Harvard University Department of Mathematics CambridgeMA United States

ISBN: (纸本)9780738132020

Enhancer-Promoter (EP) interactions reflected by Hi-C technology are crucial to understanding genomic functions. Particularly, identifying 'unique' genomic features that are characteristically important in a specific cell line can further our current understanding of the mechanisms that drive cell differentiation, tissue development, and disease progression. However, classic prediction models such as TargetFinder provide little valuable insight towards the large disparity between important genomic features across different cell lines. To comprehensively approach this question, herein we first evaluated seven classifiers to predict EP interactions using high-resolution Hi-C maps of genome loci across six classic cell lines, surpassing TargetFinder in all benchmark metrics. We then evaluated the model's predictive performance with features provided by seven feature selection methods from the embedded, wrapper and filter categories. Moreover, groups of features were aggregated from the results of two or more feature methods and analyzed based on the model's performance. Finally, we examined the distinguishing features across six cell lines. Our study suggests the existence of 'unique' genomic features that are especially predictive of EP interactions only in specific cell lines. © 2021 ieee.

关键词： Genome

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Protein secondary structure prediction using BLAST and exhaustive RT-RICO, the search for optimal segment length and threshold

Protein secondary structure prediction using BLAST and exhau...

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

作者： Lee, Leong Leopold, Jennifer L. Frank, Ronald L. Department of Computer Science Austin Peay State University Clarksville TN United States Department of Computer Science Missouri University of Science and Technology Rolla MO United States Department of Biological Sciences Missouri University of Science and Technology Rolla MO United States

ISBN: (纸本)9781467311892

Protein secondary structure prediction from its amino acid sequence is a well studied computational problem in bioinformatics and data mining. It can be viewed as an intermediate research objective to solving the more challenging protein three-dimensional structure prediction problem, which is one of the most important research goals of bioinformatics. Although the secondary structure prediction problem was first defined in the 1960s, the prediction accuracy of the most modern methods still hovers around 80%. In [1] this research team presented a protein secondary structure prediction method, BLAST-RT-RICO (Relaxed Threshold Rule Induction from Coverings), that employs a modified association rule learning approach, utilizing multiple sequence alignment information, to predict secondary structures. Despite producing higher prediction accuracy than many other contemporary methods, that preliminary research study identified some crucial areas in need of improvements, such as determining the optimal segment length, finding the optimal threshold value, and improving the time complexity for the rule generation algorithm. In this paper, we present a modified method, BLAST-ERT-RICO (Exhaustive Relaxed Threshold Rule Induction from Coverings), which has an improved time complexity, as well as more optimal choices of segment length and threshold value. Preliminary test results showed that with a segment length of 9 amino acid residues, and a threshold value of 0.8, BLAST-ERT-RICO achieved a Q3 score of 92.19% on the standard test dataset RS126, which suggests that this approach may be even more useful as a secondary structure prediction method in the future. © 2012 ieee.

关键词： bioinformatics

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