Imperial College London

ProfessorYi-KeGuo

Faculty of EngineeringDepartment of Computing

Professor of Computing Science
 
 
 
//

Contact

 

+44 (0)20 7594 8182y.guo Website

 
 
//

Assistant

 

Ms Diana O'Malley +44 (0)20 7594 0991

 
//

Location

 

211AWilliam Penney LaboratorySouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

249 results found

Dong H, Wu C, Wei Z, Guo Yet al., 2018, Dropping Activation Outputs With Localized First-Layer Deep Network for Enhancing User Privacy and Data Security, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 13, Pages: 662-670, ISSN: 1556-6013

JOURNAL ARTICLE

Rossios C, Pavlidis S, Hoda U, Kuo C-H, Wiegman C, Russell K, Sun K, Loza MJ, Baribaud F, Durham AL, Ojo O, Lutter R, Rowe A, Bansal A, Auffray C, Sousa A, Corfield J, Djukanovic R, Guo Y, Sterk PJ, Chung KF, Adcock IM, Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes U-BIOPRED Consortia Project Teamet al., 2018, Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma., J Allergy Clin Immunol, Vol: 141, Pages: 560-570

BACKGROUND: Sputum analysis in asthmatic patients is used to define airway inflammatory processes and might guide therapy. OBJECTIVE: We sought to determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared with nonsmoking patients with mild/moderate asthma. METHODS: Induced sputum was obtained from nonsmoking patients with SA, smokers/ex-smokers with severe asthma, nonsmoking patients with mild/moderate asthma (MMAs), and healthy nonsmoking control subjects. Differential cell counts, microarray analysis of cell pellets, and SOMAscan analysis of sputum analytes were performed. CRID3 was used to inhibit the inflammasome in a mouse model of SA. RESULTS: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in patients with SA compared with MMAs. Forty-two genes probes were upregulated (>2-fold) in nonsmoking patients with severe asthma compared with MMAs, including IL-1 receptor (IL-1R) family and nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NRLP3) inflammasome members (false discovery rate < 0.05). The inflammasome proteins nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 1 (NLRP1), NLRP3, and nucleotide-binding oligomerization domain (NOD)-like receptor C4 (NLRC4) were associated with neutrophilic asthma and with sputum IL-1β protein levels, whereas eosinophilic asthma was associated with an IL-13-induced TH2 signature and IL-1 receptor-like 1 (IL1RL1) mRNA expression. These differences were sputum specific because no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsy specimens in patients with SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammati

JOURNAL ARTICLE

Bai L, Cheng X, Liang J, Guo Yet al., 2017, Fast graph clustering with a new description model for community detection, INFORMATION SCIENCES, Vol: 388, Pages: 37-47, ISSN: 0020-0255

JOURNAL ARTICLE

Bai L, Cheng X, Liang J, Shen H, Guo Yet al., 2017, Fast density clustering strategies based on the k-means algorithm, PATTERN RECOGNITION, Vol: 71, Pages: 375-386, ISSN: 0031-3203

JOURNAL ARTICLE

Dong H, Supratak A, Mai L, Liu F, Oehmichen A, Yu S, Guo Yet al., 2017, TensorLayer: A versatile library for efficient deep learning development, Pages: 1201-1204

© 2017 Copyright held by the owner/author(s). Recently we have observed emerging uses of deep learning techniques in multimedia systems. Developing a practical deep learning system is arduous and complex. It involves labor-intensive tasks for constructing sophisticated neural networks, coordinating multiple network models, and managing a large amount of training-related data. To facilitate such a development process, we propose TensorLayer which is a Python-based versatile deep learning library. TensorLayer provides high-level modules that abstract sophisticated operations towards neuron layers, network models, training data and dependent training jobs. In spite of offering simplicity, it has transparent module interfaces that allows developers to flexibly embed low-level controls within a backend engine, with the aim of supporting fine-grain tuning towards training. Real-world cluster experiment results show that TensorLayer is able to achieve competitive performance and scalability in critical deep learning tasks. TensorLayer was released in September 2016 on GitHub. Since after, it soon become one of the most popular open-sourced deep learning library used by researchers and practitioners.

CONFERENCE PAPER

Dong H, Supratak A, Pan W, Wu C, Matthews PM, Guo Yet al., 2017, Mixed Neural Network Approach for Temporal Sleep Stage Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, ISSN: 1534-4320

IEEE This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory (LSTM) network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electrooculogram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

JOURNAL ARTICLE

Dong H, Yang G, Liu F, Mo Y, Guo Yet al., 2017, Automatic brain tumor detection and segmentation using U-net based fully convolutional networks, Pages: 506-517, ISSN: 1865-0929

© Springer International Publishing AG 2017. A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

CONFERENCE PAPER

Du Y, Ma C, Wu C, Xu X, Guo Y, Zhou Y, Li Jet al., 2017, A Visual Analytics Approach for Station-Based Air Quality Data, SENSORS, Vol: 17, ISSN: 1424-8220

JOURNAL ARTICLE

He S, Yong M, Matthews PM, Guo Yet al., 2017, tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles, BIOINFORMATICS, Vol: 33, Pages: 787-788, ISSN: 1367-4803

JOURNAL ARTICLE

Hekking P-P, Loza MJ, Pavlidis S, de Meulder B, Lefaudeux D, Baribaud F, Auffray C, Wagener AH, Brinkman P, Lutter R, Bansal AT, Sousa AR, Bates SA, Pandis Y, Fleming LJ, Shaw DE, Fowler SJ, Guo Y, Meiser A, Sun K, Corfield J, Howarth PH, Bel EH, Adcock IM, Chung KF, Djukanovic R, Sterk PJ, U-BIOPRED Study Groupet al., 2017, Pathway discovery using transcriptomic profiles in adult-onset severe asthma., J Allergy Clin Immunol

BACKGROUND: Adult-onset severe asthma is characterized by highly symptomatic disease despite high-intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease is needed for the development of targeted treatments. Gene set variation analysis is a statistical technique used to identify gene profiles in heterogeneous samples. OBJECTIVE: We sought to identify gene profiles associated with adult-onset severe asthma. METHODS: This was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at age ≥18 years) as compared with childhood-onset severe asthma (<18 years) were selected from the U-BIOPRED cohort. Gene expression was assessed on the total RNA of induced sputum (n = 83), nasal brushings (n = 41), and endobronchial brushings (n = 65) and biopsies (n = 47) (Affymetrix HT HG-U133+ PM). Gene set variation analysis was used to identify differentially enriched predefined gene signatures of leukocyte lineage, inflammatory and induced lung injury pathways. RESULTS: Significant differentially enriched gene signatures in patients with adult-onset as compared with childhood-onset severe asthma were identified in nasal brushings (5 signatures), sputum (3 signatures), and endobronchial brushings (6 signatures). Signatures associated with eosinophilic airway inflammation, mast cells, and group 3 innate lymphoid cells were more enriched in adult-onset severe asthma, whereas signatures associated with induced lung injury were less enriched in adult-onset severe asthma. CONCLUSIONS: Adult-onset severe asthma is characterized by inflammatory pathways involving eosinophils, mast cells, and group 3 innate lymphoid cells. These pathways could represent useful targets for the treatment of adult-onset severe asthma.

JOURNAL ARTICLE

Kuo C-HS, Pavlidis S, Loza M, Baribaud F, Rowe A, Pandis I, Hoda U, Rossios C, Souse A, Wilson SJ, Howarth P, Dahlen B, Dahlen S-E, Chanez P, Shaw D, Krug N, Sandstrom T, De Meulder B, Lefaudeux D, Fowler S, Fleming L, Corfield J, Auffray C, Sterk PJ, Djukanovic R, Guo Y, Adcock IM, Chung KFet al., 2017, A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 195, Pages: 443-455, ISSN: 1073-449X

JOURNAL ARTICLE

Kuo C-HS, Pavlidis S, Loza M, Baribaud F, Rowe A, Pandis I, Sousa A, Corfield J, Djukanovic R, Lutter R, Sterk PJ, Auffray C, Guo Y, Adcock IM, Chung KFet al., 2017, T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED, EUROPEAN RESPIRATORY JOURNAL, Vol: 49, ISSN: 0903-1936

JOURNAL ARTICLE

Lefaudeux D, De Meulder B, Loza MJ, Peffer N, Rowe A, Baribaud F, Bansal AT, Lutter R, Sousa AR, Corfield J, Pandis I, Bakke PS, Caruso M, Chanez P, Dahlen S-E, Fleming LJ, Fowler SJ, Horvath I, Krug N, Montuschi P, Sanak M, Sandstrom T, Shaw DE, Singer F, Sterk PJ, Roberts G, Adcock IM, Djukanovic R, Auffray C, Chung KFet al., 2017, U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 139, Pages: 1797-1807, ISSN: 0091-6749

JOURNAL ARTICLE

Lertvittayakumjorn P, Wu C, Liu Y, Mi H, Guo Yet al., 2017, Exploratory analysis of big social data using MIC/MINE statistics, Pages: 513-526, ISSN: 0302-9743

© 2017, Springer International Publishing AG. A major goal of Exploratory Data Analysis (EDA) is to understand main characteristics of a dataset, especially relationships between variables, which are helpful for creating a predictive model and analysing causality in social science research. This paper aims to introduce Maximal Information Coefficient (MIC) and its by-product statistics to social science researchers as effective EDA tools for big social data. A case study was conducted using a historical data of more than 3,000 country-level indicators. As a result, MIC and some by-product statistics successfully provided useful information for EDA complementing the traditional Pearson’s correlation. Moreover, they revealed several significant, including nonlinear, relationships between variables which are intriguing and able to suggest further research in social sciences.

CONFERENCE PAPER

Molina-Solana M, Birch D, Guo Y-K, 2017, Improving data exploration in graphs with fuzzy logic and large-scale visualisation, APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946

JOURNAL ARTICLE

Nie L, Yang X, Matthews PM, Xu Z-W, Guo Y-Ket al., 2017, Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation, INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING, Vol: 14, Pages: 371-385, ISSN: 1476-8186

JOURNAL ARTICLE

Pavlidis S, Guo Y, Sun K, Rossios C, Rowe A, Loza M, Baribaud F, Hoda U, Sousa A, Corfield J, Djukanovic R, Sterk PJ, Adcock I, Chung KFet al., 2017, Comparison Between Bronchial And Nasal Brushings Gene Expression In The U-Biopred Cohort, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X

CONFERENCE PAPER

Supratak A, Dong H, Wu C, Guo Yet al., 2017, DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 25, Pages: 1998-2008, ISSN: 1534-4320

JOURNAL ARTICLE

Xie J, Liu Y, Zhou Z, Wang R, Kong Y, Guo Y, Zhang Wet al., 2017, A Disease Risk Model for Health Data, JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vol: 7, Pages: 536-541, ISSN: 2156-7018

JOURNAL ARTICLE

Yang X, Pan W, Guo Y, 2017, Sparse Bayesian classification and feature selection for biological expression data with high correlations, PLOS ONE, Vol: 12, ISSN: 1932-6203

JOURNAL ARTICLE

Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar H-J, Guo Y-K, Gut IG, Hanbury A, Hanif S, Hilgers R-D, Honrado A, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Mueller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, Redon i Mas J, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaino JA, Wubbe JPM, Zanetti Get al., 2016, Making sense of big data in health research: Towards an EU action plan, GENOME MEDICINE, Vol: 8, ISSN: 1756-994X

JOURNAL ARTICLE

Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar H-J, Guo Y-K, Gut IG, Hanbury A, Hanif S, Hilgers R-D, Honrado A, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Muller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, Redon i Mas J, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaino JA, Wubbe JPM, Zanetti Get al., 2016, Making sense of big data in health research: towards an EU action plan (vol 8, pg 71, 2016), GENOME MEDICINE, Vol: 8, ISSN: 1756-994X

JOURNAL ARTICLE

Dong H, Matthews PM, Guo Y, 2016, A New Soft Material Based In-the-Ear EEG Recording Technique, 38th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5709-5712, ISSN: 1557-170X

CONFERENCE PAPER

Li Y, Guo Y, 2016, Wild-Health: From Quantified Self to Self-Understanding, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 56, Pages: 333-359, ISSN: 0167-739X

JOURNAL ARTICLE

Li Y, Pandis I, Guo Y, 2016, Enabling Virtual Sensing as a Service, Informatics, Vol: 3, Pages: 3-3

JOURNAL ARTICLE

Mares MA, Wang S, Guo Y, 2016, Combining Multiple Feature Selection Methods and Deep Learning for High-dimensional Data, Transactions on Machine Learning and Data Mining, Vol: 9, Pages: 27-45, ISSN: 1865-6781

Feature or variable selection when the number of features is relativelylarge to the number of samples or n << p is a challenge in many machine learningapplications. A large number of statistical methods have been developed to addressthis challenge. Each method uses different statistical assumptions about theshape of the regression function relating the predicted variable to the predictors.In this paper we propose an alternative: combining results from different featureselection methods relying on disjoint assumptions about the regression function.We show that our method will lead to better sensitivity than using different methodsindividually, on synthetic datasets and datasets from the UCI machine learningrepository. Our empirical studies on data with n << p show that the accuracyobtained when training deep neural networks with variables selected using ourmethod is at least as good as the accuracy obtained when not selecting variablesin advance. Our first conclusion is that the feature selection results are improvedby enlarging the body of limiting assumptions about the function relating the predictedvariable to the predictors. Our second conclusion is that, feature selectioncan improve accuracy in deep learning at least on data with n << p.

JOURNAL ARTICLE

May Y, Li Y, Raffel J, Craner M, Hemingway C, Giovannoni G, Overell J, Hyde R, Van Beek J, Thomas F, Guo Y, Matthews Pet al., 2016, OPTIMISE - A Web-Based Solution for Recording and Analyzing Longitudinal Multiple Sclerosis Data, 68th Annual Meeting of the American-Academy-of-Neurology (AAN), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0028-3878

CONFERENCE PAPER

May Y, Li Y, Raffel J, Craner M, Hemingway C, Giovannoni G, Overell J, Hyde R, Van Beek J, Thomas F, Guo Y, Matthews Pet al., 2016, OPTIMISE - A Web-Based Solution for Recording and Analyzing Longitudinal Multiple Sclerosis Data, 68th Annual Meeting of the American-Academy-of-Neurology (AAN), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0028-3878

CONFERENCE PAPER

McGinn D, Birch D, Akroyd D, Molina-Solana M, Guo Y, Knottenbelt WJet al., 2016, Visualizing Dynamic Bitcoin Transaction Patterns, BIG DATA, Vol: 4, Pages: 109-119, ISSN: 2167-6461

JOURNAL ARTICLE

Nie L, Matthews PM, Guo Y, 2016, Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 63, Pages: 2505-2517, ISSN: 0018-9294

JOURNAL ARTICLE

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00100008&limit=30&person=true