| The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis TM Walker, P Miotto, CU Köser, PW Fowler, J Knaggs, Z Iqbal, M Hunt, ... The Lancet Microbe 3 (4), e265-e273, 2022 | 195 | 2022 |
| Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test AAS Soltan, S Kouchaki, T Zhu, D Kiyasseh, T Taylor, ZB Hussain, T Peto, ... The Lancet Digital Health 3 (2), e78-e87, 2021 | 171* | 2021 |
| Application of machine learning techniques to tuberculosis drug resistance analysis S Kouchaki, Y Yang, TM Walker, A Sarah Walker, DJ Wilson, TEA Peto, ... Bioinformatics 35 (13), 2276-2282, 2019 | 98 | 2019 |
| A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics CRyPTIC Consortium PLoS biology 20 (8), e3001721, 2022 | 84 | 2022 |
| Tensor based singular spectrum analysis for automatic scoring of sleep EEG S Kouchaki, S Sanei, EL Arbon, DJ Dijk IEEE Transactions on Neural Systems and Rehabilitation Engineering 23 (1), 1-9, 2014 | 83 | 2014 |
| Improving time–frequency domain sleep EEG classification via singular spectrum analysis SM Mohammadi, S Kouchaki, M Ghavami, S Sanei Journal of neuroscience methods 273, 96-106, 2016 | 79 | 2016 |
| Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis C Consortium European Respiratory Journal, 2022 | 63 | 2022 |
| Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms CRyPTIC Consortium PLoS biology 20 (8), e3001755, 2022 | 60 | 2022 |
| Quaternion singular spectrum analysis of electroencephalogram with application in sleep analysis S Enshaeifar, S Kouchaki, CC Took, S Sanei IEEE Transactions on Neural Systems and Rehabilitation Engineering 24 (1), 57-67, 2015 | 58 | 2015 |
| DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis Y Yang, TM Walker, AS Walker, DJ Wilson, TEA Peto, DW Crook, ... Bioinformatics 35 (18), 3240-3249, 2019 | 56 | 2019 |
| A group decision-making tool for the application of membrane technologies in different water reuse scenarios SMK Sadr, DP Saroj, S Kouchaki, AA Ilemobade, SK Ouki Journal of environmental management 156, 97-108, 2015 | 54 | 2015 |
| Machine learning techniques for soybean charcoal rot disease prediction E Khalili, S Kouchaki, S Ramazi, F Ghanati Frontiers in plant science 11, 590529, 2020 | 46 | 2020 |
| Cryo-EM structure and resistance landscape of M. tuberculosis MmpL3: An emergent therapeutic target O Adams, JC Deme, JL Parker, PW Fowler, SM Lea, S Newstead Structure 29 (10), 1182-1191. e4, 2021 | 45 | 2021 |
| A multi expert decision support tool for the evaluation of advanced wastewater treatment trains: A novel approach to improve urban sustainability SMK Sadr, DP Saroj, JC Mierzwa, SJ McGrane, G Skouteris, R Farmani, ... Environmental science & policy 90, 1-10, 2018 | 42 | 2018 |
| Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis L Sonnenkalb, JJ Carter, A Spitaleri, Z Iqbal, M Hunt, KM Malone, ... The Lancet Microbe 4 (5), e358-e368, 2023 | 41 | 2023 |
| GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes DJ Wilson Molecular biology and evolution 37 (8), 2450-2460, 2020 | 40 | 2020 |
| Bridging the gap: Attending to discontinuity in identification of multiword expressions O Rohanian, S Taslimipoor, S Kouchaki, LA Ha, R Mitkov arXiv preprint arXiv:1902.10667, 2019 | 39 | 2019 |
| Multi-label random forest model for tuberculosis drug resistance classification and mutation ranking S Kouchaki, Y Yang, A Lachapelle, TM Walker, AS Walker, ... Frontiers in microbiology 11, 667, 2020 | 36 | 2020 |
| Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene … Nature communications 15 (1), 488, 2024 | 30 | 2024 |
| Network analysis to identify symptoms clusters and temporal interconnections in oncology patients E Kalantari, S Kouchaki, C Miaskowski, K Kober, P Barnaghi Scientific reports 12 (1), 17052, 2022 | 28 | 2022 |
David A. CliftonChair of Clinical Machine Learning, University of OxfordZweryfikowany adres z eng.ox.ac.uk
