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ieee International conference on Robotics and Automation (ICRA)

作者： Gen Endo Jun Nakanishi Jun Morimoto G. Cheng Sony Intelligence Dynamics Laboratories Inc. Tokyo Japan ATR Computational Neuroscience Laboratories Kyoto Japan

Recently, there has been a growing interest in biologically inspired biped locomotion control with Central Pattern Generator (CPG). However, few experimental attempts on real hardware 3D humanoid robots have yet been made. Our goal in this paper is to present our achievement of 3D biped locomotion using a neural oscillator applied to a humanoid robot, QRIO. We employ reduced number of neural oscillators as the CPG model, along with a task space Cartesian coordinate system and utilizing entrainment property to establish stable walking gait. We verify robustness against lateral perturbation, through numerical simulation of stepping motion in place along the lateral plane. We then implemented it on the QRIO. It could successfully cope with unknown 3mm bump by autonomously adjusting its stepping period. Sagittal motion produced by a neural oscillator is introduced, and then overlapped with the lateral motion generator in realizing 3D biped locomotion on a QRIO humanoid robot.

关键词： Oscillators Legged locomotion Humanoid robots Robustness Hardware Neurofeedback Robot kinematics Laboratories biology computing Biological control systems

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An adaptive neighboring search using crossover-like mutation for multi modal function optimization

An adaptive neighboring search using crossover-like mutation...

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ieee International conference on Systems, Man and Cybernetics

作者： O. Takahashi S. Kobayashi Department of Computational Intelligence & Systems Science Interdisciplinary Graduate School of Science & Engineering Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology New Energy and Industrial Technology Development Organization Midori Japan

We propose a new population-based evolutionary algorithm which uses a real-coded representation and normal-distribution crossover-like mutation for generating the next searching points. This Gaussian distribution is formed based on the positional relationships between an individual and its neighbors, and is not carried with the self-adapting parameters as an inheritable trait. This algorithm causes the emergence of clusters of individuals within the population, as a result of the evolution of each individual, which does not have any actual intent to cluster. By searching independently, the emergent clusters introduce various solutions that include optima at the same time, even if the problem has strong local minima. The proposed method robustly solves a highly multi-modal 30-dimensional Fletcher-Powell function with a small population size.

关键词： Genetic mutations Evolution (biology) Gaussian distribution Genetic algorithms Clustering algorithms Robustness Evolutionary computation Search methods Electronic switching systems Computer industry

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Sparse Negative Binomial Signal Recovery for Genomic Variant Prediction in Diploid Species

Sparse Negative Binomial Signal Recovery for Genomic Variant...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Jocelyn Ornelas Munoz Erica M. Rutter Mario Banuelos Suzanne S. Sindi Roummel F. Marcia Department of Applied Mathematics University of California Merced Department of Mathematics California State University Fresno

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Structural variants (SVs) - such as insertions, deletions, and duplications of an individual's genome – are associated with genetic diseases and promotion of genetic diversity. Detecting SVs of an unknown genome is a mathematically challenging problem since SVs are rare and prone to low-coverage noise. Common approaches to detect SVs in an unknown genome require sequencing fragments of the genome, comparing them to a high-quality reference genome, and predicting SVs based on identified discordant fragments. We developed a computational method which seeks to improve existing SV detection methods in three ways: First, we implement an optimization approach using a negative binomial log-likelihood objective function. Second, we use a block-coordinate descent approach to simultaneously predict if an SV is homozygous or heterozygous given genomic data of related individuals. Third, we model a biologically realistic scenario where variants in the child are either inherited or novel. We validate our framework with simulated data and demonstrate improvements in predicting SVs and detecting false positives.

关键词： Sequential analysis Pediatrics Spirals Statistical analysis Genomics Optimization methods Prediction algorithms Signal reconstruction Noise measurement bioinformatics

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Analysis of feedback schemes in proliferating stem cells

Analysis of feedback schemes in proliferating stem cells

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ieee conference on Decision and Control

作者： A. Borri P. Palumbo A. Singh Istituto di Analisi dei Sistemi e Informatica “A. Ruberti” Italian National Research Council (IASI-CNR) Via dei Taurini 19 Roma Italy Department of Biotechnologies and Biosciences University of Milano-Bicocca Milan Italy Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Stem cells have emerged as a pivotal player in biomedical research, offering unprecedented opportunities for regenerative medicine, disease modeling, and drug discovery. These undifferentiated cells possess the unique ability to self-renew and differentiate into various specialized cell types, making them invaluable in tissue repair and regeneration. This note analyzes a stochastic model aiming at unraveling the control machinery providing the proper ratio of stem-versus-differentiated cells, in agreement with a couple of feedback schemes regulating cell proliferation. What emerges from the closed-loop schemes analysis is that in order to have an asymptotically stable, nontrivial accumulation of differentiated cells a negative feedback on cell proliferation is required. Numerical simulations in the stochastic setting validate the approximate theoretical results achieved to relate how random fluctuations propagate with respect to variations of model parameters.

关键词： Analytical models Negative feedback Fluctuations Biological system modeling Stochastic processes Stem cells Maintenance engineering Numerical simulation Mathematical models Numerical models

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Accomplishments and Challenges of Protein Ontology

Accomplishments and Challenges of Protein Ontology

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ieee Annual International Computer Software and Applications conference (COMPSAC)

作者： E. Chang F.K. Hussain T.S. Dillon A.S. Sidhu Fac. of Inf. Technol. Technol. Sydney Univ. NSW

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from a unique perspective that is, examining protein data sources through structure and hierarchy of protein ontology (PO). Various data mining algorithms and mathematical models provide methods for analyzing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein ontology is designed to meet these needs by providing a structured protein data specification for protein data representation. Protein ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for protein structure and function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO data of human proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO

关键词： Ontologies Protein engineering Databases biology Australia Data mining bioinformatics Information technology Information systems Proteomics

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Feedforward Regulation of Interneuronal Communication

Feedforward Regulation of Interneuronal Communication

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ieee conference on Decision and Control

作者： Oliver Gambrell Zahra Vahdat Abhyudai Singh Department of Electrical and Computer Engineering University of Delaware Newark DE USA Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology University of Delaware Newark DE USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Analysis of the model results in analytical expressions for the statistical moments of docked vesicle count and the number of neurotransmitters in the cleft as a function of model parameters, and the frequency of presynaptic APs. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Our results show the existence of an optimal presynaptic AP frequency that maximizes the probability of a postsynaptic AP firing. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and identify parameter regimes where the feedforward neuronal networks result in band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies.

关键词： Analytical models Neurotransmitters Vesicles Fluctuations Firing Neurons Noise Regulation Synapses Chemicals

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Advances in Conceptual Modeling – Challenges and Opportunities 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Il-Yeol Song Mario Piattini Yi-Ping Phoebe Chen Sven Hartmann Fabio Grandi Juan Trujillo Andreas L. Opdahl Fernando Ferri Patrizia Grifoni Maria Chiara Caschera Colette Rolland Carson Woo Camille Salinesi Esteban Zimányi Christophe Claramunt Flavius Frasincar Geert-Jan Houben Philippe Thiran

ISBN: (数字)9783540879916

ISBN: (纸本)9783540879909

This book constitutes the refereed joint proceedings of seven international workshops held in conjunction with the 27th International conference on Conceptual Modeling, ER 2008, in Barcelona, Spain, in October 2008. The 42 revised full papers presented were carefully reviewed and selected from 108 submissions. Topics addressed by the workshops are conceptual modeling for life sciences applications (CMLSA 2008), evolution and change in data management (ECDM 2008), foundations and practices of UML (FP-UML 2008), modeling mobile applications and services (M2AS 2008), requirements, intentions and goals in conceptual modeling (RIGiM 2008), semantic and conceptual issues in geographic information systems (SeCoGIS 2008), and Web information systems modeling (WISM 2008).

关键词： Information Systems Applications (incl. Internet) Database Management Information Storage and Retrieval User Interfaces and Human Computer Interaction Artificial intelligence computational biology/bioinformatics

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Predicting Compound-Protein Interaction by Deepening the Systemic Background via Molecular Network Feature Embedding

Predicting Compound-Protein Interaction by Deepening the Sys...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Han Wang Hangxu Zhu Wenhao Li Ming Liu Li Zhang Dong Xu School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China School of Mathematics and Statistics School of Computer Science and Engineering Changchun University of Technology Changchun China Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Identifying compound-protein interactions (CPI) is crucial for drug screening, drug repurposing, and combination therapy studies. The performance of CPI prediction depends heavily on the features extracted from compounds and target proteins. The existing prediction methods use different feature combinations, but both molecular-based and network-based models have the problem of incomplete feature representations. Therefore, completely integrating the relevant features of CPI would be an effective way to solve the existing problem. This study proposed a novel model named MCPI, which integrated the PPI (protein-protein interaction) network, CCI (compound-compound interaction) network, and structure features of CPI to improve prediction performance. We compared our model with other existing methods for predicting CPI on public datasets. The experimental results showed that MCPI outperformed the peer methods. In addition, in response to the SARS-CoV-2 pandemic, we applied the model to search for potential inhibitors among FDA-approved drugs and validated the prediction results through the literature. This work may also provide potential guidance for drug development.

关键词： Drugs Proteins Pandemics Inhibitors Semantics Predictive models Feature extraction

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Exercising High-Level Parallel Programming on Streams: A Systems biology Use Case

Exercising High-Level Parallel Programming on Streams: A Sys...

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International conference on Distributed Computing Systems Workshop

作者： Marco Aldinucci Maurizio Drocco Guilherme Peretti Pezzi Claudia Misale Fabio Tordini Massimo Torquati Computer Science Department University of Torino Italy Computer Science Department University of Pisa Italy

The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result into a computationally expensive task reducing the interactivity required in the model tuning. In this work, we advocate high-level software design as a vehicle for building efficient and portable parallel simulators for a variety of platforms, ranging from multi-core platforms to GPGPUs to cloud. In particular, the Calculus of Wrapped Compartments (CWC) parallel simulator for systems biology equipped with on-line mining of results, which is designed according to the Fast Flow pattern-based approach, is discussed as a running example. In this work, the CWC simulator is used as a paradigmatic example of a complex C++ application where the quality of results is correlated with both computation and I/O bounds, and where high-quality results might turn into big data. The Fast Flow parallel programming framework, which advocates C++ pattern-based parallel programming makes it possible to develop portable parallel code without relinquish neither run-time efficiency nor performance tuning opportunities. Performance and effectiveness of the approach are validated on a variety of platforms, inter-alia cache-coherent multi-cores, cluster of multi-core (Ethernet and Infiniband) and the Amazon Elastic Compute Cloud.

关键词： Analytical models computational modeling Biological system modeling Pipelines Trajectory Multicore processing Graphics processing units

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DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data

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BMC bioinformatics 2019年第1期20卷 678-678页

作者： Cui, Hongfei Hu, Hailin Zeng, Jianyang Chen, Ting Tsinghua Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Univ Sci & Technol Beijing DonLinks Sch Econ & Management Beijing Peoples R China Tsinghua Univ Sch Med Beijing Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China

Background: Ribosome profiling brings insight to the process of translation. A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and then assign a huge number of multiple-mapped reads to similar isoforms. Existing methods either discard the multiple mapped-reads, or allocate them randomly, or assign them proportionally according to transcript abundance estimated from RNA-seq data. Results: Here we present DeepShape, an RNA-seq free computational method to estimate ribosome abundance of isoforms, and simultaneously compute their ribosome profiles using a deep learning model. Our simulation results demonstrate that DeepShape can provide more accurate estimations on both ribosome abundance and profiles when compared to state-of-the-art methods. We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment. In the four cell invasion/metastasis genes that are translationally regulated by PP242 treatment, different isoforms show very different characteristics of translational efficiency and regulation patterns. Transcript level ribosome distributions were analyzed by "Codon Residence Index (CRI)" proposed in this study to investigate the relative speed that a ribosome moves on a codon compared to its synonymous codons. We observe consistent CRI patterns in PC3 cells. We found that the translation of several codons could be regulated by PP242 treatment. Conclusion: In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.

关键词： Ribo-seq Translation Transcript-level Multiple alignment

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