Imperial College London

ProfessorYi-KeGuo

Faculty of EngineeringDepartment of Computing

Professor of Computing Science
 
 
 
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Contact

 

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

 
 
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Assistant

 

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

 
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Location

 

211AWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

263 results found

Pavlidis S, Monast C, Loza MJ, Branigan P, Chung KF, Adcock IM, Guo Y, Rowe A, Baribaud Fet al., 2019, I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment., PLoS Comput Biol, Vol: 15

Crohn's disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions. We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment. We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn's disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn's disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enri

JOURNAL ARTICLE

Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, Liu F, Arridge S, Keegan J, Guo Y, Firmin Det al., 2018, DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 1310-1321, ISSN: 0278-0062

JOURNAL ARTICLE

Takahashi K, Pavlidis S, Kwong FNK, Hoda U, Rossios C, Sun K, Loza M, Baribaud F, Chanez P, Fowler SJ, Horvath I, Montuschi P, Singer F, Musial J, Dahlen B, Dahlen S-E, Krug N, Sandstrom T, Shaw DE, Lutter R, Bakke P, Fleming LJ, Howarth PH, Caruso M, Sousa AR, Corfield J, Auffray C, De Meulder B, Lefaudeux D, Djukanovic R, Sterk PJ, Guo Y, Adcock IM, Chung KFet al., 2018, Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis, EUROPEAN RESPIRATORY JOURNAL, Vol: 51, ISSN: 0903-1936

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 PJet al., 2018, Pathway discovery using transcriptomic profiles in adult-onset severe asthma, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 141, Pages: 1280-1290, ISSN: 0091-6749

JOURNAL ARTICLE

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

Bai L, Liang J, Du H, Guo Yet al., 2018, A novel community detection algorithm based on simplification of complex networks, KNOWLEDGE-BASED SYSTEMS, Vol: 143, Pages: 58-64, ISSN: 0950-7051

JOURNAL ARTICLE

Kuo C-HS, Liu C-Y, Pavlidis S, Lo Y-L, Wang Y-W, Chen C-H, Ko H-W, Chung F-T, Lin T-Y, Wang T-Y, Lee K-Y, Guo Y-K, Wang T-H, Yang C-Tet al., 2018, Unique immune gene expression Patterns in Bronchoalveolar lavage and Tumor adjacent non-neoplastic lung Tissue in non-small cell lung cancer, FRONTIERS IN IMMUNOLOGY, Vol: 9, ISSN: 1664-3224

JOURNAL ARTICLE

Wang Z, Xiao D, Fang F, Govindan R, Pain CC, Guo Yet al., 2018, Model identification of reduced order fluid dynamics systems using deep learning, INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Vol: 86, Pages: 255-268, ISSN: 0271-2091

JOURNAL ARTICLE

Li K, Liu F, Dong H, Vinas PH, Guo Y, Georgiou Pet al., 2018, A DEEP LEARNING PLATFORM FOR DIABETES BIG DATA ANALYSIS, Publisher: MARY ANN LIEBERT, INC, Pages: A116-A116, ISSN: 1520-9156

CONFERENCE PAPER

Dong H, Supratak A, Pan W, Wu C, Matthews PM, Guo Yet al., 2018, Mixed Neural Network Approach for Temporal Sleep Stage Classification, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 26, Pages: 324-333, ISSN: 1534-4320

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 IMet al., 2018, Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 141, Pages: 560-570, ISSN: 0091-6749

JOURNAL ARTICLE

Liao B, Zhang J, Wu C, McIlwraith D, Chen T, Yang S, Guo Y, Wu Fet al., 2018, Deep Sequence Learning with Auxiliary Information for Traffic Prediction, Publisher: ASSOC COMPUTING MACHINERY

WORKING PAPER

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

Ezzati M, Bentham J, Di Cesare M, Bilano V, Bixby H, Zhou B, Stevens GA, Riley LM, Taddei C, Hajifathalian K, Lu Y, Savin S, Cowan MJ, Paciore CJ, Chirita-Emandi A, Hayes AJ, Katz J, Kelishadi R, Kengne AP, Khang Y-H, Laxmaiah A, Li Y, Ma J, Miranda JJ, Mostafa A, Neovius M, Padez C, Rampal L, Zhu A, Bennet JE, Danaei G, Bhutta ZA, Ezzati M, Abarca-Gomez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, Adams RJ, Aekplakorn W, Afsana K, Aguilar-Salinas CA, Agyemng C, Ahmadvand A, Ahrens W, Ajlouni K, Akhtaeva N, Al-Hazzaa HM, Al-Othman AR, Al-Raddadi R, AlBuhairan F, AlDhukai S, Ali MM, Ali O, Alkerwi A, Alvarez-Pedrerol M, Aly E, Amarapurkar DN, Amouyel P, Amuzu A, Andersen LB, Anderssen SA, Andrade DS, Angquist LH, Anjana RM, Aounallah-Skhiri H, Araujo J, Arianse I, Aris T, Arlappa N, Arveiler D, Aryal KK, Aspelund T, Assah FK, Assuncao MCF, Aung MS, Avdicova M, Azevedo A, Azizi F, Babu BV, Bahijri S, Baker JL, Balakrishna N, Bamoshmoosh M, Banach M, Bandosz P, Banegas JR, Barbagallo CM, Barcelo A, Barkat A, Barros AJD, Barros MVG, Bata I, Batieha AM, Batista RL, Batyrbek A, Baur LA, Beaglehole R, Ben Romdhane H, Benedics J, Benet M, Bennet JE, Bernabe A, Bernotiene G, Bettiol H, Bhagyalaxmi A, Bharadwaj S, Bhargava SK, Bhatti Z, Bhutta ZA, Bi H, Bi Y, Biehl A, Bikbov M, Bista B, Bjelica DJ, Bjerregaard P, Bjertnes E, Bjness MB, Bjorkelund C, Blokstra A, Bo S, Bobak M, Boddy LM, Boehm BO, Boeing H, Boggia JG, Boissonnet CP, Bonaccio M, Bongard V, Bovet P, Braeckevelt L, Braeckman L, Bragt MCE, Brajkovich I, Branca F, Breckenkamp J, Breda J, Brenner H, Brewster LM, Brian GR, Brinduse L, Bruno G, Bueno-de-Mesquita HB, Bugge A, Buoncristiano M, Burazeri G, Burns C, Cabrera de Leon A, Cacciottolo J, Cai H, Cama T, Cameron C, Camola J, Can G, Candido APCC, Capanzana M, Capuano V, Cardoso VC, Carlsson AC, Carvalho MJ, Casanueva FF, Casas JP, Caserta CA, Chamukuttan S, Chan AW, Chan Q, Chaturvedi HK, Chaturvedi N, Chen C-J, Chen F, Chen H, Chen S, Chen Zet al., 2017, Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults, LANCET, Vol: 390, Pages: 2627-2642, ISSN: 0140-6736

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

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

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

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

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

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

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

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

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

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

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

Oehmichen A, Guitton F, Sun K, Grizet J, Heinis T, Guo Yet al., 2017, eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis, IEEE International Conference on Big Data (IEEE Big Data), Publisher: IEEE, Pages: 353-360, ISSN: 2639-1589

CONFERENCE PAPER

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

Dong H, Yu S, Wu C, Guo Yet al., 2017, Semantic Image Synthesis via Adversarial Learning, 16th IEEE International Conference on Computer Vision (ICCV), Publisher: IEEE, Pages: 5707-5715, ISSN: 1550-5499

CONFERENCE PAPER

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