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Genetic operators based on backbone constraint angles for protein structure prediction

Genetic operators based on backbone constraint angles for pr...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Karina B. Santos Fábio L. Custódio Helio J. C. Barbosa Laurent E. Dardenne The National Laboratory for Scientific Computing (LNCC) RJ BRAZIL

This paper describes the development of genetic operators for protein structure prediction by using the frequency of occurrence of the main chain dihedral angles observed in 5,387 experimentally determined protein structures. The use of constraints in the conformation space accelerates the search by the genetic algorithm and improves its predictive capacity. The operators were tested in a crowding-based steady-state genetic algorithm implemented in the GAPF program, using a test set of eight proteins belonging to different classes (i.e., preferably α, α/β and preferably β). The results show that using operators with restricted backbone dihedral angles ranges, for each amino acid and secondary structure type, reduces up to 75% the number of energy function evaluations required to obtain equivalent results to those obtained without the new operators. Furthermore, these new operators increased the capacity of the algorithm to obtain better protein models, since for the two largest proteins from the test set, i.e. 1BDD and 1GYZ, the best predicted models had their GDT-TS value increased by more than 10% comparing with those values obtained using the standard version of the algorithm.

关键词： Proteins Amino acids Genetics computational modeling Protocols Genetic algorithms Predictive models

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A computational Pipeline to Identify New Potential Regulatory Motifs in Melanoma Progression 7th

A Computational Pipeline to Identify New Potential Regulator...

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5th International Conference on bioinformatics Models, Methods and Algorithms (bioinformatics) is part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Politano, Gianfranco Benso, Alfredo Di Carlo, Stefano Orso, Francesca Savino, Alessandro Taverna, Daniela Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy MBC Turin Italy Univ Turin Ctr Mol Syst Biol Turin Italy Consorzio Interuniv Nazl Informat Verres AO Italy

ISBN: (纸本)9783319261294;9783319261287

Molecular biology experiments allow to obtain reliable data about the expression of different classes of molecules involved in several cellular processes. This information is mostly static and does not give much clue about the causal relationships (i.e., regulation) among the different molecules. A typical scenario is the presence of a set of modulated mRNAs (up or down regulated) along with an over expression of one or more small non-coding RNAs molecules like miRNAs. To computationally identify the presence of transcriptional or post-transcriptional regulatory modules between one or more miRNAs and a set of target modulated genes, we propose a computational pipeline designed to integrate data from multiple online data repositories. The pipeline produces a set of three types of putative regulatory motifs involving coding genes, intronic miRNAs, and transcription factors. We used this pipeline to analyze the results of a set of expression experiments on a melanoma cell line that showed an over expression of miR-214 along with the modulation of a set of 73 other genes. The results suggest the presence of 27 putative regulatory modules involving miR-214, NFKB1, SREBPF2, miR33a and 9 out of the 73 miR-214 modulated genes (ALCAM, POSTN, TFAP2A, ADAM9, NCAM1, SEMA3A, PVRL2, JAG1, EGFR1). As a preliminary experimental validation we focused on 9 out of the 27 identified regulatory modules that involve miR-33a and SREBF2. The results confirm the importance of the predictions obtained with the presented computational approach.

关键词： microRNA miR-214 Melanomas Biological pathways Gene regulation Post-transcriptional regulation

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Using multi-objective genetic programming to evolve stochastic logic gate circuits

Using multi-objective genetic programming to evolve stochast...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Brian J. Ross Department of Computer Science Brock University St. Catharines ON Canada

A new stochastic logic gate language is presented. Blossey et al.'s stochastic gene gate language is extended with a complete set of stochastic Boolean gates. Although the gates have behavioural similarities to conventional logic gates, a major difference is that they operate on quantities of products or substances that dynamically vary over time. A gene gate circuit's behaviour is characterized by a time-course plot of the substance quantities. The paper studies the Boolean gate language by using multi-objective genetic programming to evolve logic gate circuits that conform to a number of different target systems. Circuit behaviour is characterized by sets of up to 15 time course statistics, and sum of ranks is used as a many-objective scoring strategy. Results show that the language is highly compositional, just like conventional logic expressions, and that multiple circuits can exhibit similar behaviours. The new gate language uses Blossey et al.'s gates as a rudimentary basis within evolved circuits, with the advantage of using higher-level Boolean gates when necessary. The identification of candidate solutions can be challenging, however, and must account for noise inherent in the time course behaviours. Circuit behaviour is also highly dependent on channel rates, and future work applying the language to real-world data will need to address this sensitivity.

关键词： Logic gates Stochastic processes computational modeling Integrated circuit modeling Biological system modeling Reactive power Delays

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FragGeneScan-plus for scalable high-throughput short-read open reading frame prediction

FragGeneScan-plus for scalable high-throughput short-read op...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Dongjae Kim Aria S. Hahn Shang-Ju Wu Niels W. Hanson Kishori M. Konwar Steven J. Hallam Department of Computer Science University of British Columbia Vancouver Canada Department of Microbiology University of British Columbia Vancouver Canada Graduate Program in Bioinformatics University of British Columbia Vancouver Canada

A fundamental step in the analysis of environmental sequence information is the prediction of potential genes or open reading frames (ORFs) encoding the metabolic potential of individual cells and entire microbial communities. FragGeneScan, a software designed to predict intact and incomplete ORFs on short sequencing reads combines codon usage bias, sequencing error models and start/stop codon patterns in a hidden Markov model to find the most likely path of hidden states from a given input sequence, provides a promising route for gene recovery in environmental datasets with incomplete assemblies. However, the current implementation of FragGeneScan does not scale efficiently with increasing input data size. Thus, FragGeneScan cannot be applied to contemporary environmental datasets that can exceed 100s of Gb. Here, we present FragGeneScan-Plus, an improved implementation of the FragGeneScan gene prediction model that leverages algorithmic thread synchronization and efficient in-memory data management to utilize multiple CPU cores without blocking I/O operations. FragGeneScan-Plus can process data approximately 5-times faster than FragGeneScan using a single core and approximately 50-times faster using eight hyper-threaded cores when benchmarked against simulated and real world environmental datasets.

关键词： Instruction sets Buffer storage Hidden Markov models Synchronization Predictive models Prediction algorithms Soil

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Identifying differentially expressed transcripts associated with prostate cancer progression using RNA-Seq and machine learning techniques

Identifying differentially expressed transcripts associated ...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Siva Singireddy Abed Alkhateeb Iman Rezaeian Luis Rueda Dora Cavallo-Medved Lisa Porter School of Computer Science University of Windsor Windsor Ontario Canada Department of Biological Sciences University of Windsor Wiodsor Ontario Canada

Background: Prostate cancer is complicated by a high level of unexplained variability in the aggressiveness of newly diagnosed disease. Given that this is one of the most prevalent cancers worldwide, finding biomarkers to effectively stratify high risk patient populations is a vital next step in improving survival rates and quality of life after treatment. Materials and Methods: In this study, we selected a dataset consisting of 106 prostate cancer samples, which represent various stages of prostate cancer and developed by RNA-Seq technology. Our objective is to identify differentially expressed transcripts associated with prostate cancer progression using pair-wise stage comparisons. Results: Using machine learning techniques, we identified 44 transcripts that are correlated to different stages of progression. Expression of an identified transcript, USP13, is reduced in stage T3 in comparison with stage T2c, a pattern also observed in breast cancer tumourigenesis. We also identified another differentially expressed transcript, PTGFR, which has also been reported to be involved in prostate cancer progression and has also been linked to breast, ovarian and renal cancers. Conclusions: The results support the use of RNA-Seq along with machine learning techniques as an essential tool in identifying potential biomarkers for prostate cancer progression. Further studies elucidating the biochemical role of identified transcripts in vitro are crucial in validating the use of these biomarkers in the prediction of disease progression and development of effective therapeutic strategies.

关键词： Prostate cancer Proteins Tumors Diseases bioinformatics Biomarkers

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ieee Conference on computational intelligence in bioinformatics and computational biology

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ieee computational intelligence Magazine 2015年第1期10卷 3-3页

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

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A Heuristic Method to Bias Protein's Primary Sequence in Protein Structure Prediction

A Heuristic Method to Bias Protein's Primary Sequence in Pro...

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Signal Processing and Intelligent Systems Conference (SPIS)

作者： Mozavani, Nasser Parineh, Hossein Iran Univ Sci & Technol Dept Comp Sci Tehran Iran

ISBN: (纸本)9781509001392

Protein Structure Prediction (PSP) is one of the most studied topics in the field of bioinformatics. Regarding the intrinsic hardness of the problem, during last decades several computational methods mainly based on artificial intelligence have been proposed to approach the problem. In this paper we broke the main process of PSP into two steps. The first step is making a bias in the sequence, i. e. providing a very fast yet considerably better energy of conformation compared to the primary sequence with zero energy. The second step, which is studied in the other essay, is feeding this biased sequence to another algorithm to find the best possible conformation. For the first step, we developed a new heuristic method to find a low-energy structure of a protein. The main concept of this method is based on rule extraction from previously determined conformations. We'll call this method Fast-Bias-Algorithm (FBA) mainly because it provides a modified structure with better energy from a primary (linear) structure of a protein in a remarkably short time, comparing to the time needed for the whole process. This method was implemented in Netlogo. We have tested this algorithm on several benchmark sequences ranging from 20 to 50-mers in two dimensional Hydrophobic Hydrophilic lattice models. Comparing with the result of the other algorithms, our method in less than 2% of their time reached up to 62% of the energy of their best conformation.

关键词： computational biology Protein Structure Prediction Heuristic method Fast Bias Algorithm HP Model 2D lattice Netlogo

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Combining CCA and CFP for Enhancing the Performance in the Hybrid BCI System

Combining CCA and CFP for Enhancing the Performance in the H...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li-Wei Ko S. Sai Kalyan Ranga Brain Research Center National Chiao Tung University Hsinchu Taiwan Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu TW

Hybrid Brain Computer Interface (BCI) is gaining attention as it can provide better performance or increase the number of user commands to control an external device. Hybrid BCI system using Motor imagery (MI) and Steady-state visually evoked potential (SSVEP) is one such system. Maintaining the performance during channel reduction is important in practical applications. In this paper we propose a combined feature extraction method using Canonical Correlation Analysis (CCA) and Common Frequency Pattern (CFP) method, where the features obtained from these methods were combined for classification. We used LDC and PARZEN for estimating the classification accuracy for the proposed method and individual method. Highest accuracy of 96.1 % is obtained for combined feature method (CCA+CFP). Whereas, the accuracy is 89.6% with CCA and 91.6% with CFP method. A significance test has shown that the performance of the proposed method is significantly different from both the individual methods (p

关键词： Feature extraction Electroencephalography Correlation Data mining Eigenvalues and eigenfunctions Yttrium Statistical analysis

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The influence of the background model on DNA motif prediction: An assessment for zinc finger transcription factor ZFX

The influence of the background model on DNA motif predictio...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Andrei Lihu Stefan Holban Department of Computer Science Politehnica University of Timisoara Timisoara Romania

Motif finding is a computationally expensive procedure subject to noise and false positives, but of major importance in understanding gene expression and cancer. Several authors argued in favor of using higher order background models to better discriminate motifs. This paper studies the effect of using Markov higher order models in three commonly used algorithms to identify the ZFX transcription factor's binding sites from a mouse embryonic stem cells dataset. We conclude that there are particular Markov orders that yield improved outcomes for each algorithm.

关键词： Markov processes Algorithm design and analysis DNA computational modeling bioinformatics Predictive models Stem cells

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Tutorial II: Representations for evolutionary computation in games

Tutorial II: Representations for evolutionary computation in...

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ieee symposium on computational intelligence and Games, CIG

作者： Daniel Ashlock Department of Mathe-matics and Statistics at the University of Guelph in Guelph Ontario Canada

Representation is a central issue in evolutionary computation. The no free lunch theorem demonstrates that there is no intrinsic advantage in a particular algorithm when considered against complete spaces of problems. The corollary is that algorithms should be fitted to the problems they are solving. Choice of representation is the primary point in the design of an evolutionary algorithm where the designer can incorporate domain knowledge and, in effect, choose the adaptive landscape he is searching. This tutorial will introduces representations for level design, agents for playing mathematical games, and for the design of non-player characters. Time permitting, other examples of representation will be included.

关键词： Games Evolutionary computation Shape bioinformatics Tutorials Algorithm design and analysis Context

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