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Mining of vaccine-associated IFN-γ gene interaction networks using the Vaccine Ontology

Journal of Biomedical Semantics 2011年第2期2卷 1-16页

作者： Özgür, Arzucan Xiang, Zuoshuang Radev, Dragomir R. He, Yongqun Istanbul Technical University Faculty of Computer and Informatics Maslak Istanbul 34469 Turkey University of Michigan Department of Electrical Engineering and Computer Science Ann Arbor 48109 MI United States University of Michigan Unit for Laboratory Animal Medicine Ann Arbor 48109 MI United States University of Michigan Department of Microbiology and Immunology Ann Arbor 48109 MI United States University of Michigan Center for Computational Medicine and Bioinformatics Ann Arbor 48109 MI United States University of Michigan School of Information Ann Arbor 48109 MI United States

Background: Interferon-gamma (IFN-γ) is vital in vaccine-induced immune defense against bacterial and viral infections and tumor. Our recent study demonstrated the power of a literature-based discovery method in extraction and comparison of the IFN-γ and vaccine-mediated gene interaction networks. The Vaccine Ontology (VO) contains a hierarchy of vaccine names. It is hypothesized that the application of VO will enhance the prediction of IFN-γ and vaccine-mediated gene interaction network. Results: In this study, 186 specific vaccine names listed in the Vaccine Ontology (VO) and their semantic relations were used for possible improved retrieval of the IFN-γ and vaccine associated gene interactions. The application of VO allows discovery of 38 more genes and 60 more interactions. Comparison of different layers of IFN-γ networks and the example BCG vaccine-induced subnetwork led to generation of new hypotheses. By analyzing all discovered genes using centrality metrics, 32 genes were ranked high in the VO-based IFN-γ vaccine network using four centrality scores. Furthermore, 28 specific vaccines were found to be associated with these top 32 genes. These specific vaccine-gene associations were further used to generate a network of vaccine-vaccine associations. The BCG and LVS vaccines are found to be the most central vaccines in the vaccine-vaccine association network. Conclusion: Our results demonstrate that the combined usages of biomedical ontologies and centrality-based literature mining are able to significantly facilitate discovery of gene interaction networks and gene-concept associations. Availability: VO is available at: http://***/vaccineontology;and the SVM edit kernel for gene interaction extraction is available at: http://***/ifngvonet/int_ext_*** © 2011 Özgür et al;licensee BioMed Central Ltd.

关键词： Biomedical Ontology Gene Interaction Network Bacterial Vaccine Specific Vaccine Generic IFNG

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Pothole Detection Based on Disparity Transformation and Road Surface Modeling

arXiv

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arXiv 2019年

作者： Fan, Rui Ozgunalp, Umar Hosking, Brett Liu, Ming Pitas, Ioannis The Robotics Institute Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The Electrical and Electronic Engineering Near East University Nicosia Cyprus The National Oceanography Centre Southampton United Kingdom The School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

Pothole detection is one of the most important tasks for road maintenance. computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu’s thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamic programming

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Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models

arXiv

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arXiv 2021年

作者： Sadiq, Ismail Perez-Alday, Erick A. Shah, Amit J. Rad, Ali Bahrami Sameni, Reza Clifford, Gari D. Department of Electrical & Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics Emory University United States Department of Epidemiology Rollins School of Public Health Emory University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States

MSC Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10Objective: Transfer learning has been shown to be an effective strategy for pre-training a deep neural network (DNN) to boost performance on smaller data-sets. This is particularly useful when it is difficult or costly to acquire high quality data, such as in medicine. In this study we evaluated the additional utility of pre-training on a computationally efficient but realistic physiological model which can be tuned to explore a wide variety of states for a specific condition. In this current work, we focused on the existence of T wave alternans (TWAs) as a marker of post traumatic stress disorder (PTSD) in a small cohort of 36 veteran twins. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without TWAs, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology. We then took a previously developed state-of-the-art DNN, trained on over 70,000 patients to classify 25 different rhythms, modified the output layer to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and performed transfer learning on the artificial data. In a final transfer learning step, real the DNN was trained on ECGs from 12 individuals with PTSD and 24 controls, and evaluated using leave-one-subject-out cross-validation. The training and testing processes were repeated with and without each of the three data sets (rhythm data, artificial data, PTSD data) to evaluate the contribution of each transfer learning step. The area under receiver operating characteristic (AUROC) curve, accuracy (Acc), F1-score for PTSD and balanced accuracy (BAcc.) classification was reported for each trained model. Main results: The best performing approach (AUROC = 0.77, Acc = 0.72, F1-score = 0.64, BAcc. = 0.73) was found by performing both transfer learning steps, using the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related ECG

关键词： Electrocardiography

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Neurodegenerative Disease Prediction via Transferable Deep Networks

Neurodegenerative Disease Prediction via Transferable Deep N...

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IEEE International Symposium on Biomedical Imaging

作者： Yanfu Zhang Guodong Liu Runxue Bao Liang Zhan Paul Thompson Heng Huang Department of Computer Science William and Mary Williamsburg USA Department of Computer Science University of Maryland College Park USA GE HealthCare Chicago USA Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Institute for Neuroimaging and Informatics University of Southern California Marina del Rey USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Advances in machine learning (ML) techniques allow broader and more accurate predictions of neurodegenerative conditions, and brain networks extracted from magnetic resonance imaging (MRI) play an important role in medical image analysis. However, data scarcity is common in brain datasets, due to the high expenses, ethical regulations, and missing visits of subjects. Data scarcity makes it harder to apply many ML techniques, particularly deep learning methods, to brain network analysis. To address this challenging problem, we propose a new semi-supervised domain-adaptation method to integrate heterogeneous brain network datasets for neurodegenerative condition prediction. We use a transferable batch normalization approach for deep neural networks to avoid problems related to domain shift in the data distributions. Guided by medical knowledge, we not only use labeled data from heterogeneous datasets but also employ unlabeled data via virtual adversarial training. Experimental results show that our approach outperforms baseline and related methods for predicting important clinical scores and biological brain ages. In particular, our approach enables the transfer of domain knowledge across brain network datasets, and the transferability can boost the prediction ability of ML models.

关键词： Training Knowledge engineering Magnetic resonance imaging Artificial neural networks Network analyzers Predictive models Regulation

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Predictive models for 5-year mortality after breast camcer surgery

Predictive models for 5-year mortality after breast camcer s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jyh-Horng Chou Jinn Tsong Tsai Tung-Kuan Liu Kao-Shing Hwang Hon-Yi Shi Department of Electrical Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan Depart of Mechanical and Automation Engineering National Kaohsiung First University of Science and Technology Taiwan Department of Computer Science National Pingtung University of Education Pingtung Taiwan Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan

ISBN: (纸本)9781479942145

Few studies of breast cancer surgery outcomes have used longitudinal data for more than five years. To validate the use of artificial neural network (ANN) models in predicting 5-year mortality for breast cancer surgery patients and to compare predictive accuracy between an ANN model and a multiple logistic regression (MLR) model. This study compared the performance of ANN and MLR models based on retrospective clinical data of 3,632 breast cancer surgery patients treated during 1996-2010. Global sensitivity score and analysis approach were also employed to assess the relative importance of variables and the relative significance of input parameters in the system model. In the training, testing, and validation groups of breast cancer surgery patients, the ANN model significantly outperformed the MLR model in terms of specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curves. Surgeon volume was the most influential variable affecting 5-year mortality followed by hospital volume, age, and Charlson co-morbidity index (CCI) score. The ANN model achieved higher overall performance indices and was more accurate in predicting 5-year mortality, compared with the conventional MLR model.

关键词： Analytical models Predictive models Breast Surgery Artificial neural networks Abstracts Sensitivity

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Multi-Task Learning for Resource Allocation in Wireless Networks of Dynamic Dimensionality

Multi-Task Learning for Resource Allocation in Wireless Netw...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Nikos A. Mitsiou Pavlos S. Bouzinis Panagiotis D. Diamantoulakis Panagiotis G. Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece MetaMind Innovations P.C Kozani Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Deep neural networks (DNNs) have demonstrated their efficacy in delivering accurate solutions to a range of optimization problems. However, in the context of wireless communications, the size of these problems may vary across adjacent time slots, due to fast changes in the networks’ architecture, e.g., the number of users. It is essential to note that this time-varying dimensionality of optimization problems in wireless networks necessitates adjustments in the DNN architecture, resulting in different numbers of input and output nodes. To address this challenge, in our paper, optimization problems of varying size are treated as distinct tasks. To tackle these tasks, a multi-task learning (MTL) approach based on modular sharing is proposed. The multi-task approach consists of a DNN, which is used to extract the solutions for all the optimization problems, and a router which manages which nodes and layers of the input and output layer of the DNN to be used during the forward propagation of each task. Consequently, all tasks share common parameters of the DNN, while the DNN dynamically adjusts to the number of nodes of its output and input layers. Numerical results demonstrate the superiority of the suggested approach over zero-padding, which is the current solution for handling resource allocation problems of varying size.

关键词： Wireless networks Simulation Artificial neural networks Reinforcement learning Evolutionary computation Multitasking Dynamic scheduling Resource management Optimization Land mobile radio

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A New, Reconfigurable Circuit Offering Functionality of AND and OR Logic Gates for Use in Algorithms Implemented in Hardware

A New, Reconfigurable Circuit Offering Functionality of AND ...

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International Conference on Microelectronics, MIEL

作者： Tomasz Talaśka Rafał Długosz Tatjana Nikolić Goran Nikolić Tomasz Stefanśki Michał Długosz Michał Talaśka Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland Aptiv Services Poland Kraków Poland Faculty of Electronic Engineering University of Niš Niš Serbia Faculty of Electronics Telecommunications and Informatics Gdanśk Univeristy of Technology Gdanśk Poland Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Poznan Poland Complex of Electronics Schools Bydgoszcz Poland

The paper presents a programmable (using a 1-bit signal) digital gate that can operate in one of two OR or AND modes. A circuit of this type can also be implemented using conventional logic gates. However, in the case of the proposed circuit, compared to conventional solutions, the advantage is a much smaller number of transistors necessary for its implementation. Circuit is also much faster than its conventional counterpart. The logic gate was implemented in the 180nm CMOS technology and verified using Hspice simulations. There are many possible applications for this gate, mostly in artificial intelligence and pattern recognition algorithms implemented at the transistor level.

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Millimeter Wave Full-Duplex Networks: MAC Design and Throughput Optimization

Millimeter Wave Full-Duplex Networks: MAC Design and Through...

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Biennial Symposium on Communications, QBSC

作者： Shengbo Liu Wen Wu Liqun Fu Kaige Qu Qiang Ye Weihua Zhuang Sherman Shen Frontier Research Center Peng Cheng Laboratory Shenzhen China School of Informatics Xiamen University Xiamen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Computer Science Memorial University of Newfoundland St. John's Canada

Full-duplex (FD) technique can remarkably boost the network capacity in the millimeter wave (mmWave) bands by enabling simultaneous transmission and reception. However, due to directional transmission and large bandwidth, the throughput and fairness performance of a mm Wave FD network are affected by deafness and directional hidden-node (HN) problems and severe residual self-interference (RSI). To address these challenges, this paper proposes a directional FD medium access control protocol, named DFDMAC to support typical directional FD transmission modes by exploiting FD to transmit control frames to reduce signaling overhead. Furthermore, a novel busy-tone mechanism is designed to avoid deafness and directional HN problems and improve fairness of channel access. To reduce the impact of RSI on link throughput, we formulate a throughput maximization problem for different FD transmission modes and propose a power control algorithm to obtain the optimal transmit power. Simulation results show that the proposed DFDMAC can improve the network throughput and fairness by over 60% and 32%, respectively, compared with the existing MAC protocol in IEEE 802.11ay. Moreover, the proposed power control algorithm can effectively enhance the network throughput.

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Brain Image Synthesis Using Incomplete Multimodal Data

Brain Image Synthesis Using Incomplete Multimodal Data

引用

IEEE International Symposium on Biomedical Imaging

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Multimodal medical image synthesis is an important task. Previous efforts mainly focus on the task domain of medical image synthesis using the complete source data and have achieved great success. However, data collection with completeness in real life might be prohibitive due to high expenses or other difficulties, particularly in brain imaging studies. In this paper, we address the challenging and important problem of medical image synthesis from incomplete multimodal data sources. We propose to learn the modal-wise representations and synthesize the targets accordingly. Particularly, a surrogate sampler is derived to generate the target representations from incomplete observations, based on which an interpretable attention-redistribution network is designed. The experimental results synthesizing PET images from MRI images demonstrate that the proposed method can solve different missing data scenarios and outperforms related baselines consistently.

关键词： Neuroimaging Brain Image synthesis Soft sensors Magnetic resonance imaging Data collection Computational efficiency

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Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors

Feature extraction using distribution representation for col...

引用

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

作者： Tommy S. Alstrøm Raviv Raich Natalie V. Kostesha Jan Larsen Dept. of Informatics and Mathematical Modeling Technical University of Denmark Lyngby Denmark School of Electrical Engineering and Computer Science Oregon State University Corvallis OR Dept. of Micro- and Nanotechnology Technical University of Denmark Lyngby Denmark

We present a colorimetric sensor array which is able to detect explosives such as DNT, TNT, HMX, RDX and TATP and identifying volatile organic compounds in the presence of water vapor in air. To analyze colorimetric sensors with statistical methods, a suitable representation of sensory readings is required. We present a new approach of extracting features from a colorimetric sensor array based on a color distribution representation. For each sensor in the array, we construct a K-nearest neighbor classifier based on the Hellinger distances between color distribution of a test compound and the color distribution of all the training compounds. The performance of this set of classifiers are benchmarked against a set of K-nearest neighbor classifiers that is based on traditional feature representation (e.g., mean or global mode). The suggested approach of using the entire distribution outperforms the traditional approaches which use a single feature.

关键词： Explosives Sensor arrays Feature extraction Image color analysis Compounds Educational institutions Chemicals

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