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检索条件"任意字段=IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology"

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Prediction of directly regulated genes of transcription factors important for ascidian early development

Prediction of directly regulated genes of transcription fact...

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The International conference on computational and System biology 2009(ieee计算生物与系统生物国际会议)(iCCSB2009)

作者： Xuyang Yuan Atsushi Kubo Yutaka Satou Kenta Nakai Institute for Bioinformatics Research and Development(BIRD) Japan Science and Technology Agency(JST)Kawaguchi 332-0012Japan Department of Computer Science Graduate School of Information Science and TechnologyUniversity of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8656Japan Human Genome Center Institute of Medical ScienceUniversity of Tokyo4-6-1 ShirokanedaiMinato-kuTokyo108-8639Japan Human Genome Center Institute of Medical ScienceUniversity of Tokyo4-6-1 ShirokanedaiMinato-kuTokyo108-8639Japan Department of ZoologyGraduate School of ScienceKyoto UniversityKitashirakawa-Oiwake-choSakyo-kuKyoto 606-8502Japan Department of Computer ScienceGraduate School of Information Science and TechnologyUniversity of Tokyo7-3-1 HongoBunkyo-kuTokyo 113-8656Japan

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Estimation of continuous environments by robot swarms: Correlated networks and decision-making

Estimation of continuous environments by robot swarms: Corre...

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ieee International conference on Robotics and Automation (ICRA)

作者： Mohsen Raoufi Pawel Romanczuk Heiko Hamann Science of Intelligence Research Cluster of Excellence Berlin Germany Department of Biology Institute for Theoretical Biology Humboldt Universität zu Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Bernstein Center for Computational Neuro-science Berlin Germany Department of Computer and Information Science University of Konstanz Konstanz Germany

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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Hardware image assessment for wireless endoscopy capsules

Hardware image assessment for wireless endoscopy capsules

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

作者： M. A. Khorsandi N. Karimi S. Samavi M. Hajabdollahi S. M.R. Soroushmehr K. Ward K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Michigan Center for Integrative Research in Critical Care Emergency Medicine Department University of Michigan Ann Arbor U.S.A. Department of Computational Medicine and Bioinformatics Department of Emergency Medicine Michigan Center for Integrative Research in Critical Care University of Michigan Ann Arbor U.S.A.

ISBN: (纸本)9781457702198

Wireless capsule endoscopy is a new technology in the realm of telemedicine that has many advantages over the traditional endoscopy systems. Transmitted images should help diagnosis of diseases of the gastrointestinal tract. Two important technical challenges for the manufacturers of these capsules are power consumption and size of the circuitry. Also, the system must be fast enough for real-time processing of image or video data. To solve this problem, many hardware designs have been proposed for implementation of the image processing unit. In this paper we propose an architecture that could be used for the assessment of endoscopy images. The assessment allows avoidance of transmission of medically useless images. Hence, volume of data is reduced for more efficient transmission of images by the endoscopy capsule. This is done by color space conversion and moment calculation of images captured by the capsule. The inputs of the proposed architecture are RGB image frames and the outputs are images with converted colors and calculated image moments. Experimental results indicate that the proposed architecture has low complexity and is appropriate for a real-time application.

关键词： Image color analysis Hardware Computer architecture Endoscopes Power demand Pipelines Wireless sensor networks

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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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Evolvable hardware: on the automatic synthesis of analog control systems

Evolvable hardware: on the automatic synthesis of analog con...

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ieee conference on Aerospace

作者： R.S. Zebulum M. Vellasco M.A. Pacheco H.T. Sinohara ICA: Applied Computational Intelligence Laboratory Electric Engineering Department Catholic University of Rio de Janeiro Rio de Janeiro Brazil

The automatic design of analog control circuits is the focus of this work. Particularly, we apply Evolutionary Computation to carry out the process of circuit synthesis. Evolutionary Computation encompasses a set of search algorithms, called Evolutionary Algorithms (EAs), which borrow from biological evolution their main principles. This particular area of research, where EAs are applied to electronic circuit synthesis, receives the name of Evolvable Hardware (EHW). Our technique differs from conventional approaches in that it considers specific circuit's implementation requirements, such as power dissipation, intrinsic noise and fault tolerance, in addition to standard specifications for control systems, such as rise time and over-signal. Our results compare well with conventionally designed controllers presented in the literature, exhibiting a lower degree of oscillation, using lesser components, and achieving better fault tolerance properties.

关键词： Hardware Automatic control Circuit synthesis Evolutionary computation Evolution (biology) Electronic circuits Control system synthesis Power dissipation Circuit noise Fault tolerant systems

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Experimental Studies of a Neural Oscillator for Biped Locomotion with QRIO

Experimental Studies of a Neural Oscillator for Biped Locomo...

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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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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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Automated Abnormality Detection in Patient Retinal Function: A Deep Learning-Powered Electroretinogram Analysis System

Automated Abnormality Detection in Patient Retinal Function:...

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

作者： Binh Duong Giap Keely Likosky Karthik Srinivasan Naheed W. Khan Nambi Nallasamy Department of Ophthalmology & Visual Sciences University of Michigan Ann Arbor MI USA Medical School University of Michigan Ann Arbor MI USA Department of Vitreo Retinal Aravind Eye Hospital Chennai Tamil Nadu India Department of Computational Medicine & Bioinformatics Ann Arbor MI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The electroretinogram (ERG) is an ophthalmic electrophysiology test designed to objectively measure the electrical response of the photoreceptor cells in the human retina. The analysis of the ERG is highly useful in evaluating various retinal diseases arising from both inherited and acquired disorders. However, analyzing ERG test results can be a time-intensive process for electrophysiologists, requiring meticulous examination of recorded ERG waveforms for accurate diagnosis and assessment. In this paper, we present a novel deep learning-based ERG analysis system to automatically detect abnormalities in the retinal function of patients. To train and validate the proposed system, a dataset of 5,640 ERG tracings recorded from 470 patients, along with diagnoses from experienced electrophysiologists, was built. Our results indicate that the proposed system demonstrates effectiveness in detecting anomalies in retinal function on full-field ERGs with an F1-score of 77.59%, and an inference time of 1.2 milliseconds. This system can be used as an effective tool to assist electrophysiologists and ophthalmologists by providing rapid and reliable automated assessments.

关键词： Electric potential Accuracy Electric variables measurement Electrophysiology Retina Photoreceptors Recording Reliability Engineering in medicine and biology Diseases

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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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Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis

Directed Scattering for Knowledge Graph-Based Cellular Signa...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Aarthi Venkat Joyce Chew Ferran Cardoso Rodriguez Christopher J. Tape Michael Perlmutter Smita Krishnaswamy Program for Computational Biology and Bioinformatics Yale University Department of Mathematics University of California Los Angeles Department of Oncology University College London Cancer Institute Department of Mathematics Boise State University Departments of Genetics and Computer Science Yale University

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.

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