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International conference on bioinformatics and Systems biology (BSB)

作者： Devasia, Jeethu V. Chandran, Priya Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut 673601 Kerala India

ISBN: (纸本)9781509022618

The problem of identifying disease causing genes and dysregulated pathways has attained a key position in computational biology research, as it helps in understanding major causal genes and their interactions behind a disease state and thereby enables proposing new drug targets. The development of computational approaches for the inference of disease causing genes and associated pathways can improve the accuracy and efficiency and reduce the cost of biomedical analysis. Identification of disease causing genes from the large set of genes produced by high throughput experiments is a time consuming and costly process. Based on the fact that interactions among several genes results in certain phenotypes, the molecular interaction network is a major resource for computational approaches to identify disease causing genes and associated pathways. Executing computations on the huge molecular interaction network is also major challenge. Here, we address the problem of inferring disease causing genes and their pathways using graph theoretical approaches which focus on reducing the execution time by using graph pruning techniques, without compromising on accuracy of results. Experimentation on real biological data shows reduced execution time and increased accuracy than other methods reported in literature on benchmark datasets, on using the proposed technique.

关键词： Molecular interaction Network Target Genes Causal genes Dysregulated pathway Graph pruning

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Ensemble Learning Based Gene Regulatory Network Inference 33

Ensemble Learning Based Gene Regulatory Network Inference

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ieee 33rd International conference on Tools with Artificial intelligence (ICTAI)

作者： Peignier, Sergio Sorin, Baptiste Calevro, Federica Univ Lyon INSA Lyon INRAE BF2IUMR0203 F-69621 Villeurbanne France

ISBN: (纸本)9781665408981

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners, have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles, were evaluated and compared to well-established single and ensemble methods, on real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base-learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.

关键词： bioinformatics Gene Regulatory Network Inference Ensemble Learning

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Overcoming the curse of dimensionality in a statistical geometry based computational protein mutagenesis

Overcoming the curse of dimensionality in a statistical geom...

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10th ieee International conference on Data Mining Workshops, ICDMW 2010

作者： Masso, Majid Department of Bioinformatics and Computational Biology George Mason University Manassas VA United States

A computational mutagenesis is detailed whereby each single residue substitution in a protein chain of primary sequence length N is represented as a sparse N-dimensional feature vector, whose M

ISBN: (纸本)9780769542577

A computational mutagenesis is detailed whereby each single residue substitution in a protein chain of primary sequence length N is represented as a sparse N-dimensional feature vector, whose M

关键词： computational geometry

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A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1811-1820页

作者： Yuan, Xiguo Li, Junping Bai, Jun Xi, Jianing Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Shaanxi Prov Peoples Hosp Dept Med Oncol Xian 710068 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710072 Peoples R China

Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

关键词： Genomics bioinformatics Sequential analysis Tumors Computer science computational modeling Next generation networking Copy number variation local outlier factor next generation sequencing boxplot procedure

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SVS: Data and knowledge integration in computational biology

SVS: Data and knowledge integration in computational biology

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33rd Annual International conference of the ieee Engineering-in-Medicine-and-biology-Society (EMBS)

作者： Zycinski, Grzegorz Barla, Annalisa Verri, Alessandro Univ Genoa DISI Dept Informat & Comp Sci I-16146 Genoa Italy

ISBN: (纸本)9781424441228

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.

关键词： bioinformatics Databases Gene expression Genomics Machine learning Program processors SVS biological techniques biology computing computational biology data integration data integration data mining data mining database database theory feature selection hi

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The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2016年第1期13卷 76-85页

作者： Fang, Yi Sun, Mengtian Dai, Guoxian Ramain, Karthik New York Univ Abu Dhabi Dept Elect & Comp Engn Abu Dhabi U Arab Emirates NYU Dept Comp Sci Engn New York NY 11201 USA Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA

Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods.

关键词： Protein protein interaction network complex network computational biology

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23^rd InternationalWorkshop on Data Mining in bioinformatics (BIOKDD 2024) 24

23<SUP>rd</SUP> InternationalWorkshop on Data Mining in Bioi...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Yan, Da Hamed, Ahmed Abdeen Chen, Jake Y. Zaki, Mohammed J. Indiana Univ Bloomington Dept Comp Sci Bloomington IN 47405 USA SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY USA Univ Alabama Birmingham Birmingham AL USA Rensselaer Polytech Inst Dept Comp Sci Troy NY USA

ISBN: (纸本)9798400704901

The goal of the 22(nd) International Workshop on Data Mining in bioinformatics (BIOKDD 2023) is to encourage KDD researchers to solve the numerous problems and challenges in bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2023 features the theme "Large-Scale Data-Driven Methods for bioinformatics". This theme encourages the use of high-performance computing (HPC) to support the training of large machine learning models for problems in bioinformatics and computational biology. The key goal is to accelerate the convergence between Data Mining and bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare. The goal of the 23(rd) International Workshop on Data Mining in bioinformatics (BIOKDD 2024) is to encourage KDD researchers to solve the numerous problems and challenges in bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2024 features the theme "Advancing bioinformatics with LLMs and GenAI". This theme encourages the use of large language models and generative artificial intelligence to solve problems in bioinformatics and computational biology. The key goal is to accelerate the convergence between Data Mining and bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

关键词： bioinformatics health informatics AI

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Heuristics for Placing the Spawn Points in Multiplayer First Person Shooters

Heuristics for Placing the Spawn Points in Multiplayer First...

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ieee conference on Games (ieee COG)

作者： Ballabio, Marco Loiacono, Daniele Politecn Milan Dipartimento Elettron Informaz & Bioinformat Milan Italy

ISBN: (纸本)9781728118840

Level design in first person shooters is a critical and complex process with many facets. Among them, the placement of the spawn points, i.e., the positions of the level where players starts the game after being killed, has a huge impact on the game experience. In this work, we propose a novel approach to this problem that combines a set of design rules with a graph-based analysis of the level. As a results, we design a set of heuristics that, based on the topological features of the graphs extracted from the levels, can be used for the placement of spawn point. Finally, we test our approach with a small user study on three different levels, comparing our heuristic placement with an uniform strategy. Although preliminary, our results are promising and suggest that the level design principles are effectively captured by our heuristics.

关键词： Games Weapons Layout Feature extraction Navigation Software bioinformatics

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computational Tools for Strain Optimization by Tuning the Optimal Level of Gene Expression

Computational Tools for Strain Optimization by Tuning the Op...

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

作者： Goncalves, Emanuel Rocha, Isabel Rocha, Miguel Univ Minho CCTC Campus Gualtar P-4710057 Braga Portugal Univ Minho CEB IBB P-4710057 Braga Portugal

ISBN: (纸本)9783642288388

In this work, a plug-in for the OptFlux Metabolic Engineering platform is presented, implementing methods that allow the identification of sets of genes to over/under express, relatively to their wild type levels. The optimization methods used are Simulated Annealing and Evolutionary Algorithms, working with a novel representation and operators. This overcomes the limitations of previous approaches based solely on gene knockouts, bringing new avenues for Biotechnology, fostering the discovery of genetic manipulations able to increase the production of certain compounds using a host microbe. The plug-in is made freely available together with appropriate documentation.

关键词： Metabolic engineering

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Chronology-Sensitive Hierarchical Clustering of Pyrosequenced DNA Samples of E. coli: A Case Study

Chronology-Sensitive Hierarchical Clustering of Pyrosequence...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Montana, Aldrin Dekhtyar, Alex Neal, Emily Black, Michael Kitts, Chris Calif Polytech State Univ San Luis Obispo Comp Sci Dept San Luis Obispo CA 93407 USA Calif Polytech State Univ San Luis Obispo Biol Dept San Luis Obispo CA 93407 USA

ISBN: (纸本)9780769545745

Hierarchical clustering is used in computational biology as a method of comparing sequenced bacterial strain DNA and determining bacterial isolates that belong to the same strain. However, the results of the hierarchical clustering are, at times, difficult to read and interpret. This paper is a case study for the use of a modified hierarchical clustering algorithm, which takes into account the underlying structure of the bacterial DNA isolate collection to which it is applied.

关键词： bioinformatics clustering pyrosequence pyrogram

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