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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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Breeding a diversity of Super Mario behaviors through interactive evolution

Breeding a diversity of Super Mario behaviors through intera...

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ieee symposium on computational intelligence and Games, CIG

作者： Patrikk D. Sørensen Jeppeh M. Olsen Sebastian Risi Center for Computer Games Research IT University of Copenhagen Copenhagen Denmark

Creating controllers for NPCs in video games is traditionally a challenging and time consuming task. While automated learning methods such as neuroevolution (i.e. evolving artificial neural networks) have shown promise in this context, they often still require carefully designed fitness functions. In this paper, we show how casual users can create controllers for Super Mario Bros. through an interactive evolutionary computation (IEC) approach, without prior domain or programming knowledge. By iteratively selecting Super Mario behaviors from a set of candidates, users are able to guide evolution towards behaviors they prefer. The result of a user test show that the participants are able to evolve controllers with very diverse behaviors, which would be difficult through automated approaches. Additionally, the user-evolved controllers perform as well as controllers evolved with a traditional fitness-based approach in terms of distance traveled. The results suggest that IEC is a viable alternative in designing diverse controllers for video games that could be extended to other games in the future.

关键词： Games IEC Neural networks Evolutionary computation Artificial intelligence Evolution (biology) Sociology

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Applied informatics: A paradigm shift in artificial embryonic systems development 16

Applied informatics: A paradigm shift in artificial embryoni...

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16th ieee International symposium on computational intelligence and Informatics (CINTI)

作者： Csaba, Szasz Tech Univ Cluj Dept Elect Machines & Drives Cluj Napoca Romania

ISBN: (纸本)9781467385206

As it is well known, the synthesis of sophisticated cellular biology processes in artificial silicon structures is quoted as one of today's most highly ranked scientific challenges. However, the failure of most approaches which try to faithfully model cellular biology represents a major impediment which inhibits rapid progress in artificial biological structures development. In other words, the main obstacle seems to be the impossibility to full comprehend biological cell processes evolution and growth in their entirety and especially because of the lack of a precise and authentically models being to reproduce with high fidelity cellular and embryonic phenomena's. In an attempt to devise a model which more closely mimics cellular biology, this paper discusses an original artificial organism model, developed upon a two-layer coarse-fine-grid network approach. The strength of this approach is that it endeavors to capture the complexity of both the cellular networks as well as that of the biological cell itself, by implementing the internal biological phenomena of an organism into a two different network topology hardware layer. In essence, this model not only keeps the full advantages of previously created models that enable the implementation of similar self-replicating or self-repairing, abilities akin to those expressed by its cellular equivalents in nature, but there (according to the nature of cell biology) the inherent need of artificial cell structures to fulfill the entire role of a biological cell in the network is also expressed. At this stage of the research, the main goal it was to demonstrate that the chosen configuration operates correctly and supports the modeling assumptions. Not least of all, such architectures are suitable to deliver useful information about a wide range of imitated cellular processes in a short time and could become a helpful framework for researchers to confirm theoretical principles and experimental work quickly, with the full adv

关键词： bioinformatics embryonic system artificial cell coarse-fine-grid network artificial organism

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DAPD: A Knowledgebase for Diabetes Associated Proteins

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2015年第3期12卷 604-610页

作者： Gopinath, Krishnasamy Jayakumararaj, Ramaraj Karthikeyan, Muthusamy Alagappa Univ Dept Bioinformat Karaikkudi 630004 Tamil Nadu India Govt Arts Coll Dept Bot Melur 625106 Tamil Nadu India

Recent advancements in genomics and proteomics provide a solid foundation for understanding the pathogenesis of diabetes. Proteomics of diabetes associated pathways help to identify the most potent target for the management of diabetes. The relevant datasets are scattered in various prominent sources which takes much time to select the therapeutic target for the clinical management of diabetes. However, additional information about target proteins is needed for validation. This lacuna may be resolved by linking diabetes associated genes, pathways and proteins and it will provide a strong base for the treatment and planning management strategies of diabetes. Thus, a web source "Diabetes Associated Proteins Database (DAPD)" has been developed to link the diabetes associated genes, pathways and proteins using PHP, MySQL. The current version of DAPD has been built with proteins associated with different types of diabetes. In addition, DAPD has been linked to external sources to gain the access to more participatory proteins and their pathway network. DAPD will reduce the time and it is expected to pave the way for the discovery of novel anti-diabetic leads using computational drug designing for diabetes management. DAPD is open accessed via following url ***/dapd.

关键词： biology and genetics bioinformatics (protein) databases database design diabetes

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Finding Influential Genes Using Gene Expression Data and Boolean Models of Metabolic Networks

Finding Influential Genes Using Gene Expression Data and Boo...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Takeyuki Tamura Tatsuya Akutsu Chun-Yu Lin Jinn-Moon Yang Bioinformatics Center Kyoto University Uji Kyoto Japan Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu Taiwan

Selection of influential genes using gene expression data from normal and disease samples is an important topic in bioinformatics. In this paper, we propose a novel computational method for the problem, which combines gene expression patterns from normal and disease samples with a mathematical model of metabolic networks. This method seeks a set of k genes knockout of which drives the state of the metabolic network towards that in the disease samples. We adopt a Boolean model of metabolic networks and formulate the problem as a maximization problem under an integer linear programming framework. We applied the proposed method to selection of influential genes using gene expression data from normal samples and disease (head and neck cancer) samples. The result suggests that the proposed method can select more biologically relevant genes than an existing P-value based ranking method can.

关键词： Biochemistry Gene expression Compounds Cancer bioinformatics Mathematical model

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ieee CIBCB 2014-computational intelligence in bioinformatics and computational biology

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ieee computational intelligence MAGAZINE 2014年第4期9卷 10-11页

作者： Corns, Steven Missouri Univ Sci & Technol Rolla MO 65409 USA

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Evolving DNA Classifiers with Extinction Based Ring Optimization

Evolving DNA Classifiers with Extinction Based Ring Optimiza...

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ieee Conference on computational intelligence in bioinformatics and computational biology CIBCB

作者： Ashlock, Daniel Gillis, Sierra Garner, Jennifer Fogel, Gary Univ Guelph Dept Math & Stat Guelph ON N1G 2W1 Canada Univ Guelph Dept Chem Guelph ON N1G 2W1 Canada Nat Select Inc San Diego CA USA

ISBN: (纸本)9781479969265

Extinction is a natural process that drives biological evolution. In this study, the impact of four different extinction operators on the evolution of side-effect machines with a ring optimizer was investigated. Side-effect machines are an emerging technology used to generate features for DNA classification. Ring optimization is a type of evolutionary algorithm inspired by the biological concept of ring species. Previous work showed that ring optimization was an efficient technique for locating good side effect machines with substantial robustness against parameter choice for the optimizer. This study extends that research by incorporating extinction, which has been shown to substantially improve the performance of the ring optimizer on discrete and numerical test problems. Two of the four extinction operators improved the quality of the best outcome, while all four were able to reset the ring optimizer into a more exploratory state.

关键词： bioinformatics

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Predicting Time Sequences in Gene Expression Using Dynamic Neuro-Fuzzy Networks

Predicting Time Sequences in Gene Expression Using Dynamic N...

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ieee Conference on computational intelligence in bioinformatics and computational biology CIBCB

作者： Ahmadi, Ali Tari, Sadaf Iranpour KN Toosi Univ Technol Fac Comp Engn Tehran Iran

ISBN: (纸本)9781479969265

Gene regulation at the cellular level is a dynamic process;and neural networks with the capability of being trained and regulated based on training data, and fuzzy systems with their interpretability, are suitable algorithms for this type of computations. In this research, methods based on neuro-fuzzy networks were employed for predicting the complicated relationships that exist among genes. In the proposed method, genes that have the greatest effect on each other are found and considered as regulatory genes. Then the types of their relationships including those with inhibitory, activating, and neutralizing effects are determined, and the gene regulatory network is mapped. The standard microarray dataset related to 12 typical genes that influence the germination cycle of the yeast species Saccharomyces cerevisiae was used for training. Obtained results were validated using previous biological laboratory results. It was found that the proposed method reduced the number of extracted rules for input-output space fragmentation by 15%, which substantially reduced the required computations, while the sum square error of the algorithm declined by -0.3 compared to the method that resembled the proposed algorithm the most.

关键词： bioinformatics gene expression gene regulatory network neuro-fuzzy networks

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A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for computational and Systems biology Applications 11th

A Fine-Grained CUDA Implementation of the Multi-objective Ev...

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11th International Meeting on computational intelligence Methods for bioinformatics and Biostatistics (CIBB)

作者： D'Agostino, Daniele Pasquale, Giulia Merelli, Ivan Natl Res Council Italy Inst Appl Math & Informat Technol I-16149 Genoa Italy Natl Res Council Italy Inst Biomed Technol I-20090 Segrate Mi Italy

ISBN: (纸本)9783319244624;9783319244617

Many computational and systems biology challenges, in particular those related to big data analysis, can be formulated as optimization problems and therefore can be addressed using heuristics. Beside the typical optimization problems, formulated with respect to a single target, the possibility of optimizing multiple objectives (MO) is rapidly becoming more appealing. In this context, MO Evolutionary Algorithms (MOEAs) are one of the most widely used classes of methods to solve MO optimization problems. However, these methods can be particularly demanding from the computational point of view and, therefore, effective parallel implementations are needed. This fact, together with the wide diffusion of powerful and low-cost general-purpose Graphics Processing Units, promoted the development of software tools that focus on the parallelization of one or more computational phases among the steps characterizing MOEAs. In this paper we present a fine-grained parallelization of the Fast Non-dominating Sorting Genetic Algorithm (NSGA-II) for the CUDA architecture. In particular, we will discuss how this solution can be exploited to solve multi-objective optimization task in the field of computational and systems biology.

关键词： Graphics Processing Units CUDA-accelerated architecture Multi-objective optimization computational and Systems biology

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A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2016年第3期13卷 549-556页

作者： Liang, Cheng Li, Yue Luo, Jiawei Hunan Univ Coll Informat Sci & Elect Engn Changsha 410082 Hunan Peoples R China MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their targets. They are known to work cooperatively with genes and play important roles in numerous cellular processes. Identification of miRNA regulatory modules (MRMs) would aid deciphering the combinatorial effects derived from the many-to-many regulatory relationships in complex cellular systems. Here, we develop an effective method called BiCliques Merging (BCM) to predict MRMs based on bicliques merging. By integrating the miRNA/mRNA expression profiles from The Cancer Genome Atlas (TCGA) with the computational target predictions, we construct a weighted miRNA regulatory network for module discovery. The maximal bicliques detected in the network are statistically evaluated and filtered accordingly. We then employed a greedy-based strategy to iteratively merge the remaining bicliques according to their overlaps together with edge weights and the gene-gene interactions. Comparing with existing methods on two cancer datasets from TCGA, we showed that the modules identified by our method are more densely connected and functionally enriched. Moreover, our predicted modules are more enriched for miRNA families and the miRNA-mRNA pairs within the modules are more negatively correlated. Finally, several potential prognostic modules are revealed by Kaplan-Meier survival analysis and breast cancer subtype analysis. Availability: BCM is implemented in Java and available for download in the supplementary materials, which can be found on the Computer Society Digital Library at http://***. org/10.1109/ TCBB.2015.2462370.

关键词： MicroRNA regulatory module maximal bicliques bicliques merging survival analysis breast cancer subtype analysis

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