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2nd International symposium on Mathematical and computational Oncology, ISMCO 2020

ISBN: (纸本)9783030645106

The proceedings contain 11 papers. The special focus in this conference is on Mathematical and computational Oncology. The topics include: Discriminative Localized Sparse Representations for Breast Cancer Screening;Activation vs. Organization: Prognostic Implications of T and B Cell Features of the PDAC Microenvironment;on the Use of Neural Networks with Censored Time-to-Event Data;tugHall: A Tool to Reproduce Darwinian Evolution of Cancer Cells for Simulation-Based Personalized Medicine;The Potential of Single Cell RNA-Sequencing Data for the Prediction of Gastric Cancer Serum Biomarkers;theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant Detection;preface;plasticity in Cancer Cell Populations: biology, Mathematics and Philosophy of Cancer;Detecting Subclones from Spatially Resolved RNA-Seq Data.

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Promoting Behavioral Diversity via Multi-Objective/Quality-Diversity Novelty Producing Synaptic Plasticity

Promoting Behavioral Diversity via Multi-Objective/Quality-D...

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ieee symposium Series on computational intelligence (SSCI)

作者： Edoardo Bizzotto Anil Yaman Giovanni Iacca Computational and Integrative Biology University of Trento Trento Italy Korea Adv. Inst. of Sci. and Technology Daejeon Republic of Korea University of Trento Trento Italy

ISBN: (纸本)9781728190495

Learning in contexts where measuring the performance of the agents is either impossible or misleading requires different approaches in the search of the solution. These problems require either a complete exploration of the search space, or the use of reward-independent approaches, which may not be feasible in some situations. The Novelty Producing Synaptic Plasticity (NPSP) algorithm was recently proposed as a means to obtain successful learning in such contexts, by evolving synaptic plasticity rules able to generate as many novel behaviors as possible. Here, we consider a deceptive maze navigation task and extend the NPSP paradigm to a multi-objective case, by applying NSGA2 to maximizing a goal-agnostic metric (novelty) while minimizing a goal-aware metric (distance), in order to find the possible trade-offs. We then introduce an additional goal-agnostic metric (exploration) and apply MAP-Elites to “illuminate” the feature space projected by novelty and exploration. Lastly, we consider modified settings where 1) sensors are affected by random noise, and 2) the sensor perception is augmented, in order to assess the generalizability of the evolved synaptic rules across settings. Overall, our results show that both multi-objective and MAP-Elites based NPSP can find successful solutions in the different settings of the task.

关键词： Measurement Navigation Diversity reception Reinforcement learning Search problems Sensors Task analysis

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Proposal of Self-generation of Reward for danger avoidance by disregarding specific situations

Proposal of Self-generation of Reward for danger avoidance b...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yuya Ono Kentaro Kurashige Afiqe Anuar Bin Muhammad Nor Hakim Sosuke Kondo Kodai Fukuzawa Division of Information and Electronic Engineering Muroran Institute of Technology Japan ASTEC Co Ltd. Japan

ISBN: (纸本)9781728190495

Reward design in reinforcement learning should be less burdensome on the designer and be able to respond to changes in the environment and task. Therefore, we are studying the Self-generation of Reward, a method of reward design that does not depend on changes in the environment or task. Self-generation of Reward is a method in which an agent generates its own reward by evaluating sensor information from the outside world from multiple perspectives, using the sense of danger avoidance of biological life as an index. The only desired indicator of danger avoidance is a negative evaluation when the sensor information is dangerous. However, when the state is not dangerous, the evaluation is positive. Positive evaluation means that the agent is desired to achieve the goal, but the non-dangerous state has nothing to do with achieving the goal. Therefore, there is a problem that the measure of risk avoidance includes the evaluation of the achievement of undesired goals. To solve this problem, we propose a method to control whether sensor information is evaluated or not. Improve the indicator of risk avoidance to generate only negative evaluations by not including positive evaluations in the sensor evaluation if they are indicated. In this study, we conducted a simulation experiment using path finding on a grid map to verify whether the proposed method can be used for learning. As a result, we found that learning was possible by adjusting the reward given to each action.

关键词： Design methodology Reinforcement learning biology Indexes Proposals Task analysis computational intelligence

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Analysis of Gene Variants for Functional Annotations 16

Analysis of Gene Variants for Functional Annotations

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

作者： Al-Mubaid, Hisham Univ Houston Clear Lake Dept Comp Sci Houston TX 77058 USA

ISBN: (纸本)9781728114620

The study and analysis of functional annotations of human genes and gene variants are important for understanding functional genomics and gene-disease associations. This paper investigates the process of functional annotation of gene variants using the gene ontology. The paper also presents a method for annotating gene variants with biological process terms from the gene ontology. The presented method relies on identifying the enriched biological processes of the genes associated with the given genetic variants. With significant level of p<0.02, the results found to be biologically significant. Furthermore, we found that certain ontology annotations e.g. {GO:0031325;positive regulation of cellular metabolic process} are more connected with mutations via certain genes compared with normal biological process terms in the gene ontology.

关键词： Diseases Genomics bioinformatics Ontologies Biological processes DNA

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Guest Editors' Introduction to the Special Section on bioinformatics Research and Applications

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第4期16卷 1362-1363页

作者： Mandoiu, Ion I. Skums, Pavel Zelikovsky, Alexander Univ Connecticut Comp Sci & Engn Dept Storrs CT 06269 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA IM Sechenov First Moscow State Med Univ Lab Bioinformat Moscow 119991 Russia

The papers in this special section were presented at the 12th International symposium on bioinformatics Research and Application (ISBRA), which was held at Belarusian State University in Minsk, Belarus on June 5-8, 2016.

关键词： Special issues and sections bioinformatics Meetings computational biology Genomics

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Conference Report on 2017 ieee International Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB 2017) [Conference Reports]

Conference Report on 2017 IEEE International Conference on C...

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作者： Allmendinger, Richard University of Manchester United Kingdom

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Genetic Algorithm for Optimizing Global Alignment of Protein-Protein Interaction Network 16

Genetic Algorithm for Optimizing Global Alignment of Protein...

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

作者： Baker, Qanita Bani Al-Bataineh, Manar K. Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan

ISBN: (纸本)9781728114620

Protein-protein interactions can be modeled as networks. The huge amount of produced Protein-Protein Interaction (PPI) data has many applications in biological studies. These studies improved human knowledge in the life process and diseases. One of these studies is PPI network alignment, which finds the similarity between PPI networks as a biological similarity. Aligning these networks is important to investigate evolutionary pathways or protein complexes. The main challenge of all global PPI network alignment is to improve both accuracy and efficiency. In this research, the accuracy and efficiency of global PPI network alignment are improved by using the genetic algorithm instead of the greedy algorithm and applied in HubAlign framework. The performance of the proposed method is compared with HubAlign based on the total execution time for the alignment process and the correctness of the results of the aligned networks. The new approach enhanced the overall accuracy and reduce execution time compared to the HubAlign approach.

关键词： Protein-Protein Interaction Network Optimization bioinformatics Genetic Algorithm

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Scalable and Extensible Robinson-Foulds for Comparative Phylogenetics

Scalable and Extensible Robinson-Foulds for Comparative Phyl...

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ieee International symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Alvin Chon Pawel Gò recki Oliver Eulenstein Xiaoqiu Huang Ali Jannesari Bioinformatics and Computational Biology Iowa State University Ames USA Institute of Informatics University of Warsaw Warsaw Poland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Robinson-Foulds(RF) is a widely used metric in various phylogenetic analyses including clustering and generating consensus or most-parsimonious trees. Current methods are limited by one or more of the following: 1 versus 1 computation, limited to the basic RF calculation, use one tree collection, are not scalable, and restrict taxa. This paper presents Bipartition Frequency Hash Robinson-Foulds (BFHRF), a scalable and exten-sible approach for computing the average RF between disparate binary evolutionary tree collections. The novelty of our approach is utilizing a bipartition frequency hash data structure to perform parallelized tree versus hash comparisons in substitution of all possible tree versus tree comparisons. The data structure and updated computation algorithm results in an order of magnitude reduction in both runtime and memory usage. It is 39x faster and 22x reduction in memory compared to HashRF, a fast current method. Additionally, the tree collection distribution can be modified for RF variants and variable taxa due to the lack of restrictions imposed by the hash and retention of all bipartitions. Lastly, BFHRF is implemented in a modular way and provides an easy to use installation and interface for calculating the average RF of query trees against a collection of reference trees. https://***

关键词： Radio frequency Measurement Distributed processing Runtime Conferences Data structures Market research

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Compression of Biological Networks using a Genetic Algorithm with Localized Merge 16

Compression of Biological Networks using a Genetic Algorithm...

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

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

ISBN: (纸本)9781728114620

Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.

关键词： Genetic algorithms Biological cells Sociology Statistics Proteins Merging

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ProCell: Investigating cell proliferation with Swarm intelligence 16

ProCell: Investigating cell proliferation with Swarm Intelli...

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

作者： Nobile, Marco S. Vlachou, Thalia Spolaor, Simone Cazzaniga, Paolo Mauri, Giancarlo Pelicci, Pier Giuseppe Besozzi, Daniela Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy IRCCS European Inst Oncol IEO Milan Italy Univ Bergamo Dept Human & Social Sci Bergamo Italy Univ Milan Dept Oncol & Hematooncol Milan Italy SYSBIO IT Ctr Syst Biol Milan Italy

ISBN: (纸本)9781728114620

computational methods represent an effective mean for the analysis of complex biological processes, such as cell proliferation, especially when combined to well established experimental protocols. In particular, mathematical modeling coupled with computational intelligence algorithms can be successfully exploited to investigate different aspects of cell population dynamics in the context of tumor growth. To this aim, we defined ProCell, a modeling and simulation framework specifically designed for the investigation of cell proliferation, which makes use of Fuzzy Self-Tuning Particle Swarm Optimization to estimate the unknown parameters of cell population models. ProCell is here applied to the analysis of cell proliferation in acute myeloid leukemia, a hematological malignancy characterized by an inherent intratumoral heterogeneity that plays an important role in disease recurrence and resistance to chemotherapy. ProCell allowed to provide new insights on the intricate organization of cells with highly heterogeneous proliferative potential, and to highlight the important role of different cell types in the progression and evolution of the disease.

关键词： Modeling simulation acute myeloid leukemia parameter estimation Fuzzy Self-Tuning PSO computational intelligence

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