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Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 673-687页

作者： Bandyopadhyay, Sanghamitra Mallik, Saurav Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers (SCMs) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., CoMEx) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support (WS) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential SCITs. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

关键词： SCM CoMEx (maximal biclustered) frequent closed homogeneous genesets sample-based weighted support measure gene expression and methylation data

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RFL-Score: Random Forest with Lasso Scoring Function for Protein-Ligand Molecular Docking 21

RFL-Score: Random Forest with Lasso Scoring Function for Pro...

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21st ieee conference on computational intelligence in bioinformatics and computational biology, CIBCB 2024

作者： Arrua, Oscar E. Aderhold, Andrej Werhli, Adriano V. Dos Santos MacHado, Karina Center for Computational Sciences Universidade Federal do Rio Grande RS Rio Grande Brazil

ISBN: (纸本)9798350356632

Due to the growth computational power and easy access to vast amounts of biological data, machine learning (ML) models are being increasingly considered within the field of Rational Drug Design (RDD). In RDD, molecular docking methods are employed to predict the optimal binding mode of a small molecule (ligand) in the binding site of a target receptor (typically a protein). Molecular docking software utilizes Scoring Functions (SFs) to estimate the protein-ligand binding affinity. These SFs can be classified according to their development methodology: Physics-based, Empirical, Knowledge-based, and ML-based. ML-based SFs are trained using features extracted from protein-ligand complexes with known experimental affinities, relying heavily on the attributes used during training. Both the input data and the selected features can significantly impact the accuracy of the SFs. Additionally, different ML algorithms can be applied to select the most appropriate features and propose the SF model. In this study we propose RFL-Score, a novel ML-based SF using Random Forest with Lasso regression. RFL-Score is a ready-to-use SF developed with a straightforward methodology and trained exclusively on open-access biomolecular data. Only open-source software was employed for extracting features from the proteins, ligands, and complexes. The proposed SF was evaluated using the well-established CASF-2016 benchmark achieving outstanding performance compared to other SFs. The RFL-Score scoring function and elements used for training are freely available on GitHub at https://***/rflscore. © 2024 ieee.

关键词： Molecular docking

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Multithreaded Parallel Sequence Alignment Based on Needleman-Wunsch Algorithm 19

Multithreaded Parallel Sequence Alignment Based on Needleman...

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19th Annual ieee International conference on bioinformatics and Bioengineering (BIBE)

作者： Gancheva, Veska Georgiev, Ivaylo Tech Univ Sofia Dept Programming & Comp Technol Sofia Bulgaria Stephan Angeloff Inst Microbiol Sofia Bulgaria

ISBN: (纸本)9781728146171

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： bioinformatics DNA Needleman-Wunsch algorithm OpenMP parallel performance sequence alignment

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The Role of Reward Structure, Coordination Mechanism and Net Return in the Evolution of Cooperation

The Role of Reward Structure, Coordination Mechanism and Net...

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ieee conference on computational intelligence and Games (CIG)

作者： Rajagopalan, Padmini Rawal, Aditya Miikkulainen, Risto Wiseman, Marc A. Holekamp, Kay E. Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Michigan State Univ Dept Zool E Lansing MI 48824 USA

ISBN: (纸本)9781457700095

biology can offer insight into how realistic artificial agents and complex interactions between them can be created. For instance, spotted hyenas of Western Africa typically hunt alone, but cooperate once in a while to catch zebras. Using hyenas as motivation, this paper evaluates three potential factors that affect the evolution of cooperation in a team of game agents: reward structure (i.e. whether fitness reward is given to an individual agent or shared by the team), coordination mechanism (i.e. stigmergic vs. direct communication), and net return (i.e. the size of reward relative to the difficulty of obtaining it). Through neuroevolution using Multi-Component ESP [1], three predators were evolved in a field containing multiple fixed-behavior prey. Six such experiments show that (1) although shared reward strongly promotes cooperation, it is not strictly necessary for evolving cooperation;(2) although stigmergic coordination works well in simple, unambiguous tasks (i.e. with a single prey), direct communication is more effective in more complex tasks (i.e. with multiple prey);and (3) the predators evolve to hunt alone or cooperatively based on which approach results in higher net return. Insights from these computational simulations can be used to develop more intelligent behaviors for game agents in the future.

关键词： Biological neural networks computational modeling Computer architecture conferences Games Neurons Predator prey systems artificial agents biology computational simulations cooperation evolution coordination mechanism game agents game theory multi-agent systems multicomponent ESP net return neuroevolution reward structure

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Identification of Regulatory Binding Sites on mRNA Using in Vivo Derived Informations and SVMs

Identification of Regulatory Binding Sites on mRNA Using in ...

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6th International conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Livi, Carmen Maria Paillard, Luc Blanzieri, Enrico Audic, Yann Univ Trento Dept Comp Sci DISI Trento Italy Univ Rennes 1 Inst Genetique Developpement Rennes CNRS F-35014 Rennes France

ISBN: (纸本)9783642288388

Proteins able to interact with ribonucleic acids (RNA) are involved in many cellular processes. A detailed knowledge about the binding pairs is necessary to construct computational models which can avoid time consuming biological experiments. This paper addresses the creation of a model based on support vector machines and trained on experimentally validated data. The goal is the identification of RNA molecules binding specifically to a regulatory protein, called CELF1.

关键词： Support vector machines bioinformatics machine learning classification RNA binding site prediction

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On the Full Control of Boolean Networks 16th

On the Full Control of Boolean Networks

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16th International conference on computational Methods in Systems biology (CMSB)

作者： Paul, Soumya Pang, Jun Su, Cui Interdisciplinary Ctr Secur Reliabil & Trust Esch Sur Alzette Luxembourg Univ Luxembourg Fac Sci Technol & Commun Esch Sur Alzette Luxembourg

ISBN: (纸本)9783319994291;9783319994284

Boolean networks (BNs), introduced by Kauffman [3], is a popular and well-established framework for modelling gene regulatory networks and their associated signalling pathways. The main advantage of this framework is that it is relatively simple and yet able to capture the important dynamical properties of the system under study, thus facilitating the modelling and analysis of large biological networks as a whole.

关键词： Artificial intelligence

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Evolutionary granular kernel trees and applications in drug activity comparisons

Evolutionary granular kernel trees and applications in drug ...

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2005 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB '05

作者： Jin, Bo Zhang, Yan-Qing Wang, Binghe IEEE Department of Computer Science Georgia State University Atlanta GA 30302-3994 United States Department of Chemistry Georgia State University Atlanta GA 30303 United States

ISBN: (纸本)0780393872

Kernel methods, specifically support vector machines (SVMs), have been widely used In many fields for data classification and pattern recognition. The performance of SVMs is mainly affected by kernel functions. With the growing interest of biological data prediction and chemical data prediction such as structure-property based molecule comparison, protein structure prediction and long DNA sequence comparison, more powerful and flexible kernels need to be designed in order effectively to express the prior knowledge and relationships within each data item. In this paper, the granular kernel concept is presented and related properties are described in detail. A hierarchical kernel design method is proposed to construct granular kernel trees (GKTs). For a particular problem, genetic algorithms (GAs) are used to find the optimum parameter settings of GKTs. In applications, SVMs with new kernel trees are employed for the comparisons of drug activities. The experimental results show that SVMs with GKTs and evolutionary GKTs can achieve better performances than SVMs with traditional RBF kernels in terms of prediction accuracy. © 2005 ieee.

关键词： Trees (mathematics)

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Automaton Models of computational Genetic Regulatory Networks with Combinatorial Gene-Protein Logical Interactions

Automaton Models of Computational Genetic Regulatory Network...

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International Joint conference on bioinformatics, Systems biology and Intelligent Computing

作者： Chen, Peter C. Y. Weng, Yulin Natl Univ Singapore Dept Mech Engn Singapore Singapore

ISBN: (纸本)9780769537399

This article presents a formal framework for modeling computational genetic regulatory networks as automata, with multi-threshold protein concentration and combinatorial logical gene-protein interactions. By computing the intersection of the languages generated by these automata we obtain the complete set of pathways in a gene network. We illustrate the effectiveness of our framework in the modeling of (i) morphogenesis in Arabidopsis and (ii) regulation in phage lambda.

关键词： Gene networks automata combinatorial logic

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Protein Secondary Structure Prediction Using Support Vector Machines (SVMs)

Protein Secondary Structure Prediction Using Support Vector ...

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ieee International conference on Machine intelligence Research and Advancement (ICMIRA)

作者： Patel, Mayuri Shah, Hitesh GH Patel Coll Engn & Technol Dept Informat Technol Vallabh Vidyanagar 388120 Gujarat India GH Patel Coll Engn & Technol Elect & Commun Engn Dept Vallabh Vidyanagar 388120 Gujarat India

ISBN: (纸本)9780769550138

bioinformatics or computational biology is field of science in which biology, computer science and information technology merges into a single discipline. In modern computation biology, protein secondary structure prediction is a major problem. Secondary structure prediction is depends on its amino acid sequence. Current studies prefer machine learning techniques for classification and regression task. Recently many researchers used various data mining and machine learning tool for protein structure prediction. Our intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and our objective is to enhance the prediction of 2D protein structure problem using advance machine learning techniques like, linear and non-linear support vector machine with different kernel functions. The datasets used for this problem are Protein Data Bank (PDB) sets, which is based on structural classification of protein (SCOP), RS126 and CB513.

关键词： bioinformatics CB513 Feature Selection Protein Data Bank (PDB) RS126

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Side effect machines for quaternary edit metric decoding

Side effect machines for quaternary edit metric decoding

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2010 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2010

作者： Brown, Joseph Alexander Houghten, Sheridan K. Ashlock, Daniel School of Computer Science University of Guelph Guelph ON N1G 2W1 Canada Department of Computer Science Brock University St. Catharines ON L2S 3A1 Canada Department of Mathematics and Statistics University of Guelph Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781424467662

DNA edit metric codes are used as labels to track the origin of sequence data. This study is the first to treat sophisticated decoders for these error-correcting codes. Side effect machines can provide efficient decoding algorithms for such codes. Two methods for automatically producing decoding algorithms are presented. Side Effect Machines (SEMs), generalizations of finite state automata, are used in both. Single Classifier Machines (SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines (LSEMs) use multiple side effect machines to create a tree structured iterated classification. This study examines these techniques and provides new decoders for existing codes. Presented are ideas for best practises for the creation of these two types of new edit metric decoders. Codes of the form (n,M, d)4 are used in testing due to their suitability for bioinformatics problems. A group of (12, 54-56, 7)4 codes are used as an example of the process. © ieee.

关键词： bioinformatics

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