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MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2015年第1期12卷 4-16页

作者： Daniels, Noah M. Gallant, Andrew Ramsey, Norman Cowen, Lenore J. MIT Dept Math Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Tufts Univ Dept Comp Sci Medford MA 02451 USA

We introduce MRFy, a tool for protein remote homology detection that captures beta-strand dependencies in the Markov random field. Over a set of 11 SCOP beta-structural superfamilies, MRFy shows a 14 percent improvement in mean Area Under the Curve for the motif recognition problem as compared to HMMER, 25 percent improvement as compared to RAPTOR, 14 percent improvement as compared to HHPred, and a 18 percent improvement as compared to CNFPred and RaptorX. MRFy was implemented in the Haskell functional programming language, and parallelizes well on multi-core systems. MRFy is available, as source code as well as an executable, from http://***/.

关键词： Protein structure prediction remote homology detection structural bioinformatics

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Length-Based Substructure Mutation Policies for Improved RNA Design in Simulated Annealing

Length-Based Substructure Mutation Policies for Improved RNA...

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

作者： McBride, Ryan Tsang, Herbert H. Trinity Western Univ Appl Res Lab Langley BC Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

RNA Design is a crucial bioinformatics problem to tailor specific RNA sequences into structures that guide our biology and medicine. Because these computer generated sequences are often statistically different from observed RNA sequence and do not fold as intended in real lab conditions, methods are developed to ensure more biological consistency. One approach is substructure restriction where any solutions are composed of observed RNA substructures recorded in a database. However, studies on this restricted problem are limited: while our Applied Research Lab's Simulated Annealing solution (SIMARD) uses substructures it only considers a single mutation policy and single method of generating substructure consistent sequences. We therefore propose two new policies of mutation: uniform, randomly modifying any structure, and length proportional, substructures are swapped randomly in proportion to their RNA length to target substructures that cover the most bases of a problem. In experiments on roughly fifty RNA Design problems, we conclude the potential value of these substructure based mutation methods resulting in solutions potentially hundreds of bases closer to the target folded structure than previous Simulated Annealing solutions.

关键词： bioinformatics RNA Design Simulated Annealing Evolutionary Algorithms computational intelligence

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Marginalised Stack Denoising Autoencoders for Metagenomic Data Binning

Marginalised Stack Denoising Autoencoders for Metagenomic Da...

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

作者： Kouchaki, Samaneh Tirunagari, Santosh Tapinos, Avraam Robertson, David L. Univ Manchester Sch Biol Sci Evolut & Genom Sci Manchester Lancs England Univ Surrey Dept Comp Sci Guildford Surrey England Univ Oxford Dept Engn Sci Oxford England Univ Glasgow MRC Ctr Virus Res Glasgow Lanark Scotland

ISBN: (纸本)9781467389884

Shotgun sequencing has facilitated the analysis of complex microbial communities. Recently we have shown how local binary patterns (LBP) from image processing can be used to analyse the sequenced samples. LBP codes represent the data in a sparse high dimensional space. To improve the performance of our pipeline, marginalised stacked autoencoders are used here to learn frequent LBP codes and map the high dimensional space to a lower dimension dense space. We demonstrate its performance using both low and high complexity simulated metagenomic data and compare the performance of our method with several existing techniques including principal component analysis (PCA) in the dimension reduction step and k-mer frequency in feature extraction step.

关键词： Genomics Feature extraction bioinformatics Prototypes Data visualization Noise reduction

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Guest Editorial for Special Section on ISB/TBC 2014

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2015年第5期12卷 1074-1075页

作者： Zhang, Shihua Chen, Luonan Chinese Acad Sci Natl Ctr Math & Interdisciplinary Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Inst Biochem & Cell Biol Key Lab Syst Biol Innovat Ctr Cell Signaling Network Shanghai 200031 Peoples R China

The five papers in this special section were presented at the Eighth International conference on computational Systems biology and Fourth Translational bioinformatics conference (ISB/TBC 2014), which was held in Qingd... 详细信息

关键词： Special Issues And Sections bioinformatics Informatics Meetings

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Algorithms for computational biology: Seventh Edition

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2059-2060页

作者： Martin-Vide, Carlos Vega-Rodriguez, Miguel A. Rovira & Virgili Univ Res Grp Math Linguist Tarragona 43002 Spain Univ Extremadura ARCO Res Grp Caceres 10003 Spain

The papers in this special section were presented at the Seventh International conference on Algorithms for computational biology, AlCoB 2020, held in Missoula, Montana on November 8-11, 2021 merged with AlCoB 2021. The conference was organized by the Department of Computer Science at the University of Montana and the Institute for Research Development, Training and Advice - IRDTA, Brussels/London. AlCoB 2020 was the seventh event in a series dedicated to promoting and displaying excellent research using string and graph algorithms and combinatorial optimization to deal with problems in biological sequence analysis, genome rearrangement, phylogeny reconstruction, and structure prediction.

关键词： Special issues asnd sections bioinformatics Genomics Training data Prediction algorithms computational biology Meetings

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EvoDNN - Evolving Weights, Biases, and Activation Functions in a Deep Neural Network

EvoDNN - Evolving Weights, Biases, and Activation Functions ...

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

作者： Cui, Peiyu Wiese, Kay C. Simon Fraser Univ Sch Comp Sci Burnaby BC Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

Classification, such as classifying cell samples into cancer (malignant) or normal (benign), or the classification of genome regions into functional regions (for example coding regions or promoter regions) are important problems in computational biology. For such tasks, we have previously designed an evolutionary deep neural network that in addition to evolving the neuron's weights and biases also evolves (learns) the activation functions for each neuron and called this approach EvoDNN. EvoDNN can employ activation functions that are non-differentiable as it does not rely on back-propagation. This feature is adding flexibility in terms of activation functions EvoDNN can employ. The work presented here extends our previous work on EvoDNN by analyzing a more extensive set of data sets and studying variations of the internal topology of the EvoDNN model. In addition, we study the effect of evolving the weights and biases only while holding the activation function fixed and demonstrate that evolving activation functions indeed provides better performance. We also compare our model to several other popular models and demonstrate superior performance on several data sets. The current code for EvoDNN is available at https://***/Payuing/evoDNN.

关键词： Deep Learning Evolutionary Heterogeneous Neural Networks Evolving Neural Network Activation Functions Classification and Learning in computational biology and bioinformatics

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Single-Objective and Multi-Objective Genetic Algorithms for Compression of Biological Networks

Single-Objective and Multi-Objective Genetic Algorithms for ...

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

作者： Collins, Tyler K. Zakirov, Adel Brown, Joseph Alexander Houghten, Sheridan Brock Univ Dept Comp Sci St Catharines ON Canada Innopolis Univ Artificial Intelligence Games Dev Lab Innopolis Russia

ISBN: (纸本)9781467389884

Storage and processing of biological networks is challenging and costly due to the large sizes of many of these networks. Compression of such graphs is one possible solution to this problem. This study presents two single-objective genetic algorithms, along with one multi-objective algorithm, to address the problem of graph compression. The fitness functions were both based on the concept of merging nodes based on "similarity" but each defined that similarity in a different way. The multiobjective GA based on NSGA-II worked to find a balance between the compression ratio and the similarity. The methods were applied to three different biological networks with different characteristics. The single-objective GAs were first applied to these networks for a fixed compression ratio. Then based on the results of the multiobjective GA, target compression ratios were chosen for each graph and the single-objective GAs were applied to this target. Applying the single-objective GAs to a target identified in this manner was significantly more successful than using the results from the multiobjective GA for the same compression ratio.

关键词： Genetic algorithms Biological cells Sociology Statistics Loss measurement Distortion measurement

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A Systems biology Approach to Solving the Puzzle of Unknown Genomic Gene-Function Association Using Grid-Ready SVM Committee Machines

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ieee computational intelligence MAGAZINE 2012年第4期7卷 46-54页

作者： Lee, Tsung-Lu Michael Chiang, Jung-Hsien Kun Shan Univ Tainan Taiwan Natl Cheng Kung Univ Tainan 70101 Taiwan

Genomic researchers face the common challenge of deriving the functions of genes and proteins from high-throughput data. Experimental validation of protein function is costly and time-consuming. With the increased effectiveness of computational intelligence approaches, researchers aim to target the problem with in silico prediction of protein interactions and functions. We propose a systems biology approach that consists of machine-learning and visualization intelligence and aims to predict protein-protein interactions and enhance protein function annotation. Our machine-learning intelligence, SVM committee machines, is compatible with grid computing and large-scale data analysis. In this paper, we not only elucidate the computational power of protein interactions prediction, but also aim to emphasize the interpretation of protein function annotation through protein interaction network analysis.

关键词： Genomics bioinformatics Genetics Proteins Biological system modeling Informatics

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Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data 16

Improving the prediction of cardiovascular risk with machine...

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16th ieee International conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Cugliari, Giovanni Benevenuta, Silvia Guarrera, Simonetta Sacerdote, Carlotta Panico, Salvatore Krogh, Vittorio Tumino, Rosario Vineis, Paolo Fariselli, Piero Matullo, Giuseppe Univ Turin Dept Med Sci Turin Italy Piedmont Reference Ctr Epidemiol & Canc Prevent Turin Italy Federico II Univ Naples Dept Clin Med & Surg Naples Italy IRCCS Ist Nazl Tumori Epidemiol & Prevent Unit Milan Italy Dept Canc Registry & Histopathol Ragusa Italy Sch Publ Hlth Dept Epidemiol & Biostat London England

ISBN: (纸本)9781728114620

Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV) such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10-15 years) these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration.

关键词： Epigenetic biomarkers DNA methylation Genomics computational statistics

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Evolving Lockdown Strategies to Minimize Infections in an Epidemic

Evolving Lockdown Strategies to Minimize Infections in an Ep...

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

作者： Sargant, James Dube, Michael Houghten, Sheridan Brock Univ Dept Comp Sci St Catharines ON Canada Univ Guelph Dept Math & Stat Guelph ON Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

In this paper we evaluate the impact of different lockdown strategies upon the total number of infections during an epidemic. The strategies are based upon the percentage of the population infected during a given time step, as well as upon the amount by which interactions must be reduced during lockdown. We use a weighted personal contact network to represent the population, its interactions, and the relative strengths of those interactions. During lockdown edges from this network are removed. We use an evolutionary algorithm to choose the set of edges to be removed so as to minimize infections, comparing different strategies. We show that allowing the evolutionary algorithm to choose which edges to remove significantly reduces the overall number of infections in comparison to random selection. In fact, the EA results for the least stringent conditions were similar or better to the random results for the most stringent conditions, showing that a judicious choice of restrictions during lockdown has the greatest effect on reducing infections. The evolutionary algorithm tends to favour a situation in which during lockdown individuals would reduce their number of contacts, as opposed to lessening the strength of their connections.

关键词： Epidemics Sociology Evolutionary computation Statistics bioinformatics computational biology

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