检索结果-内蒙古大学图书馆

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Javier A. Cruz-López Vincent Boyer Didier El-Baz Graduate Program in Systems Engineering Universidad Autónoma de Nuevo León Mexico LAAS-CNRS CNRS Toulouse France

This paper presents two parallel implementations of the Back-propagation algorithm, a widely used approach for Artificial Neural Networks (ANNs) training. These implementations permit one to increase the number of ANNs trained simultaneously taking advantage of the thread-level massive parallelism of GPUs and multi-core architecture of modern CPUs, respectively. Computational experiments are carried out with time series taken from the product demand of a Mexican brewery company; the goal is to optimize delivery of products. We consider also time series of the M3-competition benchmark. The results obtained show the benefits of training several ANNs in parallel compared to other forecasting methods used in the competition. Indeed, training several ANNs in parallel yields to a better fitting of the weights of the network and allows to train in a short time many ANNs for different time series.

关键词： Neurons Graphics processing units Training Instruction sets Mathematical model Time series analysis Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: Paternal restraint stress affects offspring metabolism via ATF-2 dependent mechanisms in Drosophila melanogaster germ cells

Communications biology

引用

Communications biology 2020年第1期3卷 251页

作者： Ki-Hyeon Seong Nhung Hong Ly Yuki Katou Naoko Yokota Ryuichiro Nakato Shinnosuke Murakami Akiyoshi Hirayama Shinji Fukuda Siu Kang Tomoyoshi Soga Katsuhiko Shirahige Shunsuke Ishii RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. ki-hyeon.seong@riken.jp. PRESTO Japan Science and Technology Agency Kawaguchi Japan. ki-hyeon.seong@riken.jp. RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. Laboratory of Genome Structure and Function Institute for Quantitative Biosciences University of Tokyo Tokyo Japan. Laboratory of Computational Genomics Institute for Quantitative Biosciences University of Tokyo Tokyo Japan. Institute for Advanced Biosciences Keio University Tsuruoka Yamagata Japan. Systems Biology Program Graduate School of Media and Governance Keio University Fujisawa Kanagawa Japan. PRESTO Japan Science and Technology Agency Kawaguchi Japan. Transborder Medical Research Center University of Tsukuba Tsukuba Ibaraki Japan. Department of Bio-Systems Engineering Graduate School of Science and Engineering Yamagata University Yonezawa Yamagata Japan. RIKEN Cluster for Pioneering Research Tsukuba Ibaraki Japan. sishii@riken.jp.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Large Neighborhood Search with Learning Automata for the Unrelated Parallel Machine Scheduling Problem

An Adaptive Large Neighborhood Search with Learning Automata...

引用

2017 IEEE Congress on Evolutionary Computation, CEC 2017

作者： Cota, Luciano Perdigao Guimaraes, Frederico Gadelha De Oliveira, Fernando B. Souza, Marcone J. Freitas Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Computer and Systems Universidade Federal de Ouro Preto João Monlevade Brazil Department of Computer Science Universidade Federal de Ouro Preto Brazil

ISBN: (纸本)9781509046010

This work deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times, with the objective of minimizing the makespan. It is proposed an Adaptive Large Neighborhood Search (ALNS) metaheuristic using Learning Automata (LA) to adapt the probabilities of using removal and insertion heuristics and methods. A computable function in the LA updates the probability vector for selecting the actions, corresponding to six removal and six insertion methods. We also propose a new insertion method based on Hungarian algorithm, which is applied to solve subproblems optimally. Computational experiments are performed to verify the performance of the proposed method. A set of instances available in the literature with problems up to 150 jobs and 10 machines is employed in the experiments. The proposed LA-ALNS is compared against three other algorithms from the literature. The results suggest that our algorithm has better performance in most of cases (88%) under the defined conditions of experiments. Statistical tests also suggest that LA-ALNS is better than the other algorithms from the literature. The proposed method is able to automatically choose the most suitable heuristics for the instance of the problem, through adaptation and learning in the Learning Automata. © 2017 IEEE.

关键词： Heuristic methods

来源：评论

学校读者我要写书评

暂无评论

Designing Lead-Lag PSS Employing Backtracking Search Algorithm to Improve Power System Damping

Designing Lead-Lag PSS Employing Backtracking Search Algorit...

引用

IEEE GCC Conference & Exhibition

作者： Md Shafiullah Md Juel Rana L. S. Coelho M. A. Abido Ahmad Al-Subhi Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran KSA Industrial and Systems Engineering Graduate Program Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781538627570

The power system is a complex network having highly nonlinear dynamics. Being electrically interconnected power systems are vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations (LFOs) originated from small disturbances in any part of the network may cause the outage of the whole system. Power System Stabilizer (PSS) with optimal parameter settings is an efficient tool for the suppression of these oscillations. In this paper, a novel approach is proposed to tune lead-lag controller based PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to suppress LFOs through improvement of power system damping. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

关键词： Power system stability Sociology Statistics Damping Oscillators Lead Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices

引用

Brain and neuroscience advances 2018年 2卷 2398212818772179页

作者： Stephen Grossberg Center for Adaptive Systems Graduate Program in Cognitive and Neural Systems Departments of Mathematics & Statistics Psychological & Brain Sciences Biomedical Engineering Boston University Boston MA USA.

BACKGROUND:The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions. METHODS:This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions. RESULTS:Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks. CONCLUSION:This article hereby proposes a unified neural theory of prefrontal cortex and its functions.

关键词： Category learning amygdala basal ganglia cognitive-emotional interactions contextual cueing dorsolateral prefrontal cortex frontal eye fields incentive motivational learning inferotemporal cortex orbitofrontal cortex parahippocampal cortex perirhinal cortex reinforcement learning ventral bank of the principal sulcus ventral prearcuate gyrus ventrolateral prefrontal cortex working memory

来源：评论

学校读者我要写书评

暂无评论

Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

引用

NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

来源：评论

学校读者我要写书评

暂无评论

Synthetic BZLF1-targeted transcriptional activator for efficient lytic induction therapy against EBV-associated epithelial cancers

引用

Nature communications 2024年第1期15卷 3729页

作者： Man Wu Pok Man Hau Linxian Li Chi Man Tsang Yike Yang Aziz Taghbalout Grace Tin-Yun Chung Shin Yee Hui Wing Chung Tang Nathaniel Jillette Jacqueline Jufen Zhu Horace Hok Yeung Lee Ee Ling Kong Melissa Sue Ann Chan Jason Ying Kuen Chan Brigette Buig Yue Ma Mei-Ru Chen Charles Lee Ka Fai To Albert Wu Cheng Kwok-Wai Lo Department of Anatomical and Cellular Pathology Prince of Wales Hospital The Chinese University of Hong Kong Hong Kong SAR China. State Key Laboratory of Translational Oncology Sir YK Pao Centre for Cancer The Chinese University of Hong Kong Hong Kong SAR China. Ming Wai Lau Centre for Reparative Medicine Karolinska Institutet Shatin Hong Kong SAR China. Department of Surgery Prince of Wales Hospital The Chinese University of Hong Kong Hong Kong SAR China. Center for Neuromusculoskeletal Restorative Medicine Hong Kong Science Park Hong Kong SAR China. College of Chemistry and Green Catalysis Center Zhengzhou University Zhengzhou China. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. School of Biological and Health Systems Engineering Arizona State University Tempe AZ 85281 USA. Department of Otorhinolaryngology Head and Neck Surgery Prince of Wales Hospital The Chinese University of Hong Kong Hong Kong SAR China. Department of Clinical Oncology State Key Laboratory of Translational Oncology Charlie Lee Precision Immuno-oncology program Sir Y.K. Pao Centre for Cancer The Chinese University of Hong Kong Hong Kong SAR China. Graduate Institute and Department of Microbiology College of Medicine National Taiwan University Taipei 100233 Taiwan. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. albert.cheng@***. School of Biological and Health Systems Engineering Arizona State University Tempe AZ 85281 USA. albert.cheng@***. Department of Genetics and Genome Sciences University of Connecticut Health Center Farmington CT 06030 USA. albert.cheng@***. Institute for Systems Genomics University of Connecticut Health Center Farmington CT 06030 USA. albert.cheng@***. The Jackson Laboratory Cancer Center Bar Harbor ME 04609 USA. albert.cheng@***. State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences Beijing China. albert.cheng@ioz.ac.cn. Key Laboratory of Organ R

The unique virus-cell interaction in Epstein-Barr virus (EBV)-associated malignancies implies targeting the viral latent-lytic switch is a promising therapeutic strategy. However, the lack of specific and efficient therapeutic agents to induce lytic cycle in these cancers is a major challenge facing clinical implementation. We develop a synthetic transcriptional activator that specifically activates endogenous BZLF1 and efficiently induces lytic reactivation in EBV-positive cancer cells. A lipid nanoparticle encapsulating nucleoside-modified mRNA which encodes a BZLF1-specific transcriptional activator (mTZ3-LNP) is synthesized for EBV-targeted therapy. Compared with conventional chemical inducers, mTZ3-LNP more efficiently activates EBV lytic gene expression in EBV-associated epithelial cancers. Here we show the potency and safety of treatment with mTZ3-LNP to suppress tumor growth in EBV-positive cancer models. The combination of mTZ3-LNP and ganciclovir yields highly selective cytotoxic effects of mRNA-based lytic induction therapy against EBV-positive tumor cells, indicating the potential of mRNA nanomedicine in the treatment of EBV-associated epithelial cancers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

B-Spline Neural Network and Chaotic Harmony Search Applied to Yo-yo Motion System Identification

B-Spline Neural Network and Chaotic Harmony Search Applied t...

引用

IEEE Symposium Series on Computational Intelligence

作者： Rafael B. Grebogi Roberto Z. Freire Viviana C. Mariani Leandro dos S. Coelho Mechanical Engineering Graduate Program Federal Institute of Santa Catarina Brazil Industrial and Systems Engineering Graduate Program PUCPR Curitiba Brazil Mechanical Engineering Graduate Program UFPR Curitiba Brazil Industrial and Systems Engineering Graduate Program UFPR Curitiba Brazil

In an attempt to increase the accuracy in modelling nonlinear systems, a wide variety of mathematical models were developed, such as the Volterra series, Wiener models, bilinear models, Hammerstein-Wiener models, among others. Recently, artificial neural networks (ANNs) have generated considerable interest as alternative nonlinear system identification technique and regression tool. Unlike other models, ANNs are easy to adapt and to apply to different engineering problems. One example is the B-spline neural network (BSNN), which consists in a variation of the basis function artificial neural network, which is usually trained by gradient-based methods. However, gradient-based methods have the characteristic of frequently fall into local minimum during learning procedures. To overcome this problem found in conventional learning methods, Harmony Search (HS), a stochastic metaheuristic approach, can be adopted. This optimization technique was originally conceptualized using musical improvisation process of searching for a perfect state of harmony, and can be considered to adjust the control parameters of BSNNs. In the same way, this paper proposes uses a Chaotic Harmony Search (CHS) approach, based on Chaotic Logistic Map, to train BSNNs to reproduce the dynamic of a nonlinear yo-yo system. Numerical results indicate that the proposed CHS approach is effective in building a reasonable BSNN model for nonlinear identification.

关键词： Nonlinear identification B-spline neural network Harmony search Optimization

来源：评论

学校读者我要写书评

暂无评论

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

引用

arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：