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Towards cognitive analysis of DNA

Towards cognitive analysis of DNA

引用

ieee International conference on Cognitive Informatics

作者： Witold Kinsner Experimental Media Research Group Telecommunications Research Laboratories Institute of Industrial Mathematical Sciences Canada Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

Summary form only given. Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Marko

关键词： DNA Fractals bioinformatics Cognitive informatics Computers Genomics Humans

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Multi-scale Strategy Based 3D Dual-Encoder Brain Tumor Segmentation Network with Attention Mechanism

Multi-scale Strategy Based 3D Dual-Encoder Brain Tumor Segme...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yazhou Zhu Xiang Pan Jing Zhu Lihua Li School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Life Information Science and Instrument Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Magnetic resonance imaging (MRI) is a widely used diagnostic technique in the initial evaluation of patients with primary brain tumor. Automatic segmentation algorithms for brain tumor plays an important role in diagnosis of tumor subregions and treatments for patients. In this paper, we propose a novel 3D convolutional neural network for segmentation of brain tumor, which is based on the traditional encoder-decoder architecture and inspired by multi-scale strategy. The proposed network employs multi-scale strategy with the designed dualencoder structure which can be considered as two scales streams to extract features from two scales inputs respectively. To integrate the output features from two scales encoders, a multiscale attention is model designed to weight and integrated the multi-scale features. Moreover, with the purpose of modeling long-range feature dependencies more efficiently, hidden features from different positions of stream are also fused and then utilized by attention mechanism in each scale encoder. To validate the performance of the proposed network, we conduct experiments on the brain tumor segmentation dataset BraTS2019 against several state-of-art methods. The results show that proposed method has the effective and competitive performance in brain tumor segmentation.

关键词： Feature extraction Convolution Three-dimensional displays Image segmentation Tumors computational modeling Decoding

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Expiratory flow limitation in mechanically ventilated patients: a simulation study

Expiratory flow limitation in mechanically ventilated patien...

引用

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

作者： C. Brighenti G. Gnudi G. Cevenini P. Barbini Department of Electronics Computer Science and Systems (DEIS) University of Bologna Bologna Italy Department of Electronics Computer Science and Systems (DEIS) University of Bologna Cesena Italy Department of Surgery and Bioengineering Siena Italy

A non-linear lumped-parameter model of the tracheobronchial tree has been recently proposed to simulate expiratory flow limitation (EFL) in mechanical ventilation. The model is based on Weibel's symmetrical description of lung anatomy subdivided into 24 generations, where the first 17 generations correspond to conductive airways and the last 7 generations belong to the respiratory zone. Thanks to an accurate description of airway viscoelastic properties, the model enables both normal and severe chronic obstructive pulmonary disease (COPD) conditions to be reproduced. In particular, in the present work, normal and COPD cases have been studied and compared during tidal breathing. Presence of EFL in COPD conditions has been detected by means of the "external resistance method," which on the contrary revealed no EFL in normal case. Changes in diameter and resistance of conductive airways have been analyzed giving an increased airways resistance in COPD condition with respect to normal case. Simulation results show as, in patient suffering from COPD, EFL can be ascribed to large modifications in geometry and in elastic characteristics of the conductive airways, combined with a high resistance of lower airways.

关键词： Immune system Ventilation Lungs computational modeling Computer science Diseases Artificial intelligence Surgery Biomedical engineering Computer simulation

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STATISTICAL LEARNING FOR DEPENDENCE MINING AND PROBLEM SOLVING:FUNDAMENTALS,CHALLENGES,AND A UNIFIED THEORY

STATISTICAL LEARNING FOR DEPENDENCE MINING AND PROBLEM SOLVI...

引用

2005 International conference on Neural Networks and Brain(ICNN&B’05)

作者： Lei Xu Chinese University of Hong Kong

An intelligent system is featured by both its abilities of interpreting what are observed via discovering knowledge about the world it survives,and its problem solving skills of handling each issue encountered in the ***,the abilities and skills are obtained by two types of learning via evidences or data from the *** to noises in observation and a finite size of samples,learning is statistical in nature,which faces two key *** is finding appropriate mathematical representations to suit various dependence structures underlying world. The other is getting a good theory to guide learning such that dependence structures are not only learned into mathematical representations but also with an appropriate complexity that matches the size of samples(i.e.,learning reliable structures of underlying world).This paper consists of part *** first two parts summarize typical dependence structures for tackling the challenge one and typical learning theories for tackling for tackling the challenge *** third part introduces Bayesian Ying Yang(BYY) system as a general framework that unifies typical dependence structures and BYY harmony learning for the challenge two,with several favorable *** illustrate this BYY learning,in the fourth part we further introduce fundamentals of independence subspaces and advances obtained from BYY harmony learning on typical independence subspaces,including PCA,MCA,DCA,ICA,FA,TFA,NFA,BFA,LMSER,as well as their temporal ***,a concluding remark is made and new results of BYY learning in other learning areas are also briefly listed.

关键词： Statistical learning Problem-solving Bayesian methods Humans bioinformatics biology Decision making Neurons Pattern recognition Intelligent systems

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Face recognition from one example view

Face recognition from one example view

引用

International conference on Computer Vision (ICCV)

作者： D. Beymer T. Poggio Artifical Intelligence Laboratory and Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge MA USA

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of real example views at different poses. But what if we only have one real view available, such as a scanned passport photo-can we still recognize faces under different poses? Given one real view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views for a view-based, pose-invariant face recognizer. Oar experiments suggest that among the techniques for expressing prior knowledge of faces, 2D example-based approaches should be considered alongside the more standard 3D modeling techniques.< >

关键词： Face recognition Face detection Laboratories Prototypes Artificial intelligence biology computing Lighting Humans Computer graphics Learning

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Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data

Leveraging Vision Transformers for Enhanced Accuracy in Pneu...

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

作者： Kanishka Ranaweera Pubudu N. Pathirana School of Engineering Deakin University Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Medical image analysis has witnessed a paradigm shift with the advent of artificial intelligence, particularly the application of Vision Transformers (ViTs). In this study, we leverage the unique attention mechanisms of ViTs to enhance the representation learning of pneumonia-related patterns in medical images. We propose a novel framework that integrates ViTs into the diagnostic pipeline, aiming to capitalize on their ability to capture long-range dependencies and contextual information. The Vision Transformer’s unique ability to capture intricate patterns within radiological images illuminates previously unseen insights, establishing it as a promising tool for revolutionizing pneumonia diagnosis in clinical settings. The experimental evaluation conducted on a comprehensive dataset demonstrates superior performance compared to traditional approaches. The results highlight the potential of ViTs as a valuable tool in advancing the accuracy of pneumonia detection from medical imaging data, contributing to the ongoing efforts in improving diagnostic capabilities for respiratory diseases.

关键词： Representation learning Computer vision Pneumonia Accuracy Image analysis computational modeling Transformers Medical diagnostic imaging X-ray imaging Residual neural networks

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A convex gradient coil design for vertical field open MRI

A convex gradient coil design for vertical field open MRI

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

作者： S.Y. Lee M.H. Cho C.H. Moon H.W. Park Department of Medical Engineering Graduate School of East-West Medicine Kyung Hee University South Korea Department of Medical Engineering Graduate School of East-West Medicine Kyung Hee University South Korea Department of Electrical Engineering KAIST South Korea

ISBN: (纸本)0780364651

We introduce a convex gradient coil that can be used for interventional studies using a vertical field open MRI system. By placing gradient coil wires on two coaxial convex surfaces seeing each other, we can improve the accessibility to the patient during the scan, while maintaining the space for imaging. We have defined convex surfaces on hyperboloids at the prolate spheroidal coordinate. If we take account of accessibility at the figure of merit for the gradient coil performance, the convex gradient coil has better performance than conventional planar gradient coils.

关键词： Magnetic resonance imaging Coils Current density Magnetic fields Cost function Wires Coaxial components Design methodology Equations computational intelligence Society

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Automatic target localization using microelectrode recordings

Automatic target localization using microelectrode recording...

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

作者： R.A. Santiago J. McNames H. Falkenberg K. Burchiel Northwest Computational Intelligence Laboratory Portland State University OR USA Biomedical Signal Processing Laboratory Electrical & Computer Engineering Portland State University OR USA Department of Neurological Surgery Oregon Health and Sciences University OR USA

We describe an algorithm that objectively and automatically identifies target regions in the brain for ablation or stimulation during neurosurgery for Parkinson's disease and other movement disorders. The algorithm uses microelectrode recordings to distinguish between the target and adjacent anatomic structures during stereotactic neurosurgery. This algorithm uses a novel method of signal feature extraction that enables standard classification algorithms such as support vector machines to perform well. The algorithm was validated on microelectrode recordings acquired near the globus pallidus internus and labeled by the neurosurgeon.

关键词： Microelectrodes Surgery Signal processing algorithms Neurosurgery Parkinson's disease Magnetic resonance imaging Neurons Laboratories Support vector machines Support vector machine classification

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Neurobiological data sustaining opponent processing operations on self organizing networks as tools for the modeling of hippocampal dynamics

Neurobiological data sustaining opponent processing operatio...

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International Joint conference on Neural Networks (IJCNN)

作者： R.W.F. Vitral G.F. de Araujo F.C. de Oliveira D.M.S. Martins E. Christo C.M. Vitral D.M. Abramov NIPAN-Center for Computational Intelligence Adaptive Systems and Neurophysiology Department of Physiology Biological Sciences Institute Federal University of Juiz de Fora Minas Gerais Brazil

This paper intends to show how the advancement on neurobiological knowledge within an adaptive timing fashion sustains the use of past mathematical described operations such as opponent processing for the modeling of hippocampal dynamics. It is showed that two distinct functional classes of afferents, i.e., excitatory afferents to principal cells and both interneurons and glia acting as either inhibitory or secondarily excitatory afferents to these principal cells form very distinct functional states that sustain a double system for the balanced activity into the hippocampus. We did represent principal cells as dipoles of opponent processing. The last component (node) of each dipole, X5, was considered as the resulting activity of the cell (action potential or null response). Dependent on the oscillations and coherence between the two afferent systems to each dipole, would be created different sets for the processing of hippocampal dynamics. Thus, we could generate very distinct hippocampal electrophysiological states, which would be the correspondent of specific behaviors. We show how this network could, for example, generates place fields when added patterns of self-organization. Our results reproduce the pattern of place cells firing that are dependent on the hippocampal oscillations.

关键词： Self-organizing networks Hippocampus Biological system modeling computational intelligence Adaptive systems Neurophysiology Physiology biology Timing Mathematical model

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Enhancing Accuracy of Mental Fatigue Classification using Advanced computational intelligence in an Electroencephalography System

Enhancing Accuracy of Mental Fatigue Classification using Ad...

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

作者： Rifai Chai Yvonne Tran Ashley Craig Sai Ho Ling Hung T. Nguyen Key Centre for Health Technologies Faculty of Engineering and Information Technology University of Technology Key Centre for Health Technologies University of Technology Rehabilitation Studies Unit Sydney Medical School The University of Sydney

ISBN: (纸本)9781424479276

A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

关键词： Fatigue Electroencephalography Accuracy Sensitivity Feature extraction Artificial neural networks Brain modeling

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