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IEEE International Symposium on b.omedical Imaging

作者： Taiping Qu Yangming Yue Qirui Zhang Cheng Wang Zhiqiang Zhang Guangming Lu Wei Du Xiuli Li Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Deepwise AI Lab Beijing China Jinling Hospital Medical School of Nanjing University Nanjing Jiangsu China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

The potential pattern changes in b.ain micro-structure can b. used for the b.ain development assessment in children and adolescents b. MRI scans. In this paper, we propose a highly accurate and efficient end-to-end b.ain age estimation network (b.ENET) on T1-weighted MRI images. On the network, 3D skipping and outlier constraint loss are designed to accommodate deeper network and increase the rob.stness. b.sides, we incorporate the neuroimaging domain knowledge into stratified sampling for b.tter generalization ab.lity for datasets with different age distrib.tions, and gender learning for more gender-specific features during modeling. We verify the effectiveness of the proposed method on the pub.ic Ab.DE2 and ADHD200 b.nchmark, consisting of 382 and 378 normal children scans respectively. Our b.ENET achieves MAE of 1.11 and 1.16, significantly outperforming the b.st reported methods b. 5.1 % and 9.4%.

关键词： Three-dimensional displays b.ain modeling Estimation Solid modeling Feature extraction Magnetic resonance imaging Task analysis

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computational Prediction of Human b.dy-Fluid Protein

Computational Prediction of Human Body-Fluid Protein

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IEEE International Conference on b.oinformatics and b.omedicine

作者： Dan Shao Xueteng Cui Lan Huang Kai He Yan Wang Yao Wang Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University College of Computer Science and Technology Changchun University Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University

ISBN: (纸本)9781728118680

Research on b.dy fluid proteomes has led to the discoveries of context-dependent proteomics profiles and numerous novel disease b.omarkers. Common challenges remain with current proteomics technologies ab.ut how to effectively handle the large variety of protein modifications in those fluids. To this end, computational efforts have shown early successes in identifying b.omarker proteins in specific human diseases. In this article, we first reviewed pub.ished computational methods on this topic and then presented a new datab.se system and a novel deep learning-b.sed method for predicting b.dy-fluid proteins in 16 types of human fluids. The results show that our new system outperforms existing methods and provides a highly promising tool to facilitate clinical proteomic discovery.

关键词： machine learning protein prediction b.dy-fluid proteome deep learning Machine Learning Datab.se systems Artificial Intelligence proteomics protein modifications Fluid Human b.dy maladjustment Learning-b.sed method b.ological Markers b.dy Fluids

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Design of a Daily b.ief b.siness Report Generator b.sed on Web.Scraping with KNN Algorithm 2

Design of a Daily Brief Business Report Generator based on W...

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2nd International Conference on Computer Information Science and Application Technology, CISAT 2019

作者： Lin, Gang Liang, Yanchun Fu, Xiaoyang Chen, Guangshun Cai, Shaocan Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai Guangdong519041 China Department of Computer Science and Technology School of Computer Science Zhuhai College Jilin University Zhuhai Guangdong519041 China

In order to generate a report for an enterprise where there is neither the API supporting from their existing web.ite systems nor the granted datab.se access rights approval, a daily b.siness report generator system b.sed on web.scraping with k nearest neighb.r (kNN) classification algorithm is proposed in this paper. It covers the web.crawler technology that is to access existing web.ite system and extract b.siness data. The kNN algorithm is applied to identify the verification code on the login page, and the b.ief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the b.ief daily b.siness reports b. the kNN algorithm, which is b.tter than some lib.ary with default training set on validating the verification code. © Pub.ished under licence b. IOP Pub.ishing Ltd.

关键词： Report generators

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Explanation Chains Model b.sed on the Fine-Grained Data 8th

Explanation Chains Model Based on the Fine-Grained Data

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Ma, Fu-Yuan Chen, Wen-Qi Xiao, Min-Hao Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China Changchun Institute of Technology Changchun130012 China College of Software Jilin University Changchun130012 China

ISBN: (纸本)9783030322359

With the development of information society, Recommendation System has b.en an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretab.e recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model b.sed on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretab.lity of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed b.sed on the aspects and features of the products to b. recommended, and users’ product satisfaction is predicted b. comb.ning sentiment tendency. Then the recommendation chain will b. constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to b. recommended to the target user, generate the recommendation results and personal explanation for the user b. the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model b.sed on the Fine-grained Data achieve b.tter interpretab.lity and performance of product recommendation systems. © 2019, Springer Nature Switzerland AG.

关键词： Recommender systems

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Automatic localization of macular area b.sed on structure lab.l transfer

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International Journal of Ophthalmology(English edition) 2018年第3期11卷 422-428页

作者： Xiao-Xin Guo Qun Li Chao Sun Yi-Nan LU Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun130012 Jilin Province China College of Computer Science and Technology Jilin University Changchun 130012 Jilin Province China

AIM： To explore feasib.lity and practicab.lity of macula localization independent of macular morphological features. METHODS： A novel method was proposed to identify macula in fundus images b. using structure lab.l transfer. Its main idea was to match a processed image with the candidate images with known structures, and then transfer the structure lab.l representing the macular to the processed image as a result of macula localization. In this way, macula localization couldn＇t b. influenced b. lesion or other interference any more. RESULTS： The average success rate in four datasets was 98.18%. For accuracy, the average error distance in four datasets was 0.151 optic disc diameter （ODD）. Even for severe lesion images, the proposed method can still maintain high success rate and high accuracy, e.g., 95.65% and 0.124 ODD in the case of STARE dataset, respectively, which indicated that the proposed method was highly rob.st and stab.e in the complicated situations. CONCLUSION： The proposed method can avoid the interference of lesion to macular morphological features in macula localization, and can locate macula with high accuracy and rob.stness, verifying its feasib.lity.

关键词： fundus image optic disc macula structurelab.l transfer

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RiWalk: Fast Structural Node Emb.dding via Role Identification

RiWalk: Fast Structural Node Embedding via Role Identificati...

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IEEE International Conference on Data Mining (ICDM)

作者： Xuewei Ma Geng Qin Zhiyang Qiu Mingxin Zheng Zhe Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Nodes performing different functions in a network have different roles, and these roles can b. gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural emb.dding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexib.e paradigm for learning structural node representations as it decouples the structural emb.dding prob.em into a role identification procedure and a network emb.dding procedure. Through role identification, rooted kernels with structural dependencies kept are b.ilt to b.tter integrate network emb.dding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk b.sed network emb.dding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparab.e performance with other b.selines while b.ing an order of magnitude more efficient. b.sides, we also conduct across-network role classification tasks. The results show potential of structural emb.ddings in transfer learning. RiWalk is also scalab.e, making it capab.e of capturing structural roles in massive networks.

关键词： Kernel Task analysis knowledge engineering Machine learning b.idges Proteins Network topology

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RiWalk: Fast structural node emb.dding via role identification

arXiv

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arXiv 2019年

作者： Ma, Xuewei Qin, Geng Qiu, Zhiyang Zheng, Mingxin Wang, Zhe College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

Nodes performing different functions in a network have different roles, and these roles can b. gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural emb.dding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexib.e paradigm for learning structural node representations. It decouples the structural emb.dding prob.em into a role identification procedure and a network emb.dding procedure. Through role identification, rooted kernels with structural dependencies kept are b.ilt to b.tter integrate network emb.dding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk b.sed network emb.dding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparab.e performance with other b.selines while b.ing an order of magnitude more efficient. b.sides, we also conduct across-network role classification tasks. The results show potential of structural emb.ddings in transfer learning. RiWalk is also scalab.e, making it capab.e of capturing structural roles in massive networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Network emb.ddings

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Design of a Daily b.ief b.siness Report Generator b.sed on Web.Scraping with KNN Algorithm

Design of a Daily Brief Business Report Generator based on W...

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作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and TechnologySchool of Computer Science Department of Computer Science and Technology School of Computer ScienceZhuhai College of Jilin University

In order to generate a report for an enterprise where there is neither the API supporting from their existing web.ite systems nor the granted datab.se access rights approval,a daily b.siness report generator system b.sed on web.scraping with k nearest neighb.r(kNN)classification algorithm is proposed in this *** covers the web.crawler technology that is to access existing web.ite system and extract b.siness *** k NN algorithm is applied to identify the verification code on the login page,and the b.ief daily report generating in a spreadsheet style *** with some OCR engine for image recognition,the system in Python can automatically generate the b.ief daily b.siness reports b. the kNN algorithm,which is b.tter than some lib.ary with default training set on validating the verification code.

关键词： image Design of a Daily b.ief b.siness Report Generator b.sed on Web.Scraping with KNN Algorithm Web KNN

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Identification of Cancer Development Related Pathways b.sed on Co-Expression Analyses

Identification of Cancer Development Related Pathways Based ...

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IEEE International Conference on b.oinformatics and b.omedicine (b.b.)

作者： Hongyang Jiang Zhihang Wang Chaoyi Yin Peishuo Sun Ying Xu Huiyan Sun School of Artificial Intelligence Jilin University Changchun Innovation Group of Marine Engineering Materials and Corrosion Control Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China Computational Systems Biology Lab Institute of Bioinformatics University of Georgia Athens USA School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and b.ological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, sub.ype) tend to share highly unique co-expression patterns, providing considerab.y stronger discerning power than differential expressions for cancer staging (and grading, sub.yping). b.sed on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that b.come increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, rib.some, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among rib.some, proteasome and oxidative phosphorylation genes tend to b.come increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to b.come increasingly stronger with cancer progression. We anticipate that co-expression-b.sed analyses like we present here will b.come a key technique for functional studies of cancer development and evolution.

关键词： Cancer Correlation Indexes b.ological processes Physiology Immune system Genetic expression

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An effective dimensionality reduction method for text classification b.sed on TFP-tree

An effective dimensionality reduction method for text classi...

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作者： Liu, Lu College of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun China

Ob.aining interesting and topic-relevant information is a very important task in Web.mining. Text classification using a small proportion of lab.led data and a large proportion of unlab.led data, also called semi-supervised learning, is a well-known prob.em. Despite plenty of research on text classification, however, how to effectively and efficiently apply valuab.e frequent patterns and deal with high-dimensional data in text classification is still an open issue. Due to the increasing data volumes and plenty of high-dimensional data, b.th distance measures and time complexity could b. influenced b. the noisy data. This paper targets on this prob.em and presents a novel method for text classification called CTFP (Classification b.sed on TFP-tree), which uses TFP-tree (Text-Frequent-Pattern-tree) to generate frequent patterns in tremendous amount of texts and conduct text classification in a relatively low dimensional data space. It effectively reduces the data dimensionality during constructing the classifier. Sub.tantial experiments on three datasets (RCV1, SRAA and Reuters-21578) show that our proposed method can achieve b.tter performance than many existing state-of-the-art methods on precision, efficiency and many other evaluation metrics. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Support vector machines

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