CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer M Griffith, NC Spies, K Krysiak, JF McMichael, AC Coffman, AM Danos, ... Nature genetics 49 (2), 170-174, 2017 | 537 | 2017 |
XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks J Zaretzki, M Matlock, SJ Swamidass Journal of chemical information and modeling 53 (12), 3373-3383, 2013 | 223 | 2013 |
Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma K Krysiak, F Gomez, BS White, M Matlock, CA Miller, L Trani, CC Fronick, ... Blood, The Journal of the American Society of Hematology 129 (4), 473-483, 2017 | 192 | 2017 |
Deep learning global glomerulosclerosis in transplant kidney frozen sections JN Marsh, MK Matlock, S Kudose, TC Liu, TS Stappenbeck, JP Gaut, ... IEEE transactions on medical imaging 37 (12), 2718-2728, 2018 | 147 | 2018 |
XenoSite server: a web-available site of metabolism prediction tool MK Matlock, TB Hughes, SJ Swamidass Bioinformatics 31 (7), 1136-1137, 2015 | 83 | 2015 |
Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples EK Barnell, P Ronning, KM Campbell, K Krysiak, BJ Ainscough, LM Sheta, ... Genetics in Medicine 21 (4), 972-981, 2019 | 79 | 2019 |
ProteomeScout: a repository and analysis resource for post-translational modifications and proteins MK Matlock, AS Holehouse, KM Naegle Nucleic acids research 43 (D1), D521-D530, 2015 | 48 | 2015 |
Modeling small-molecule reactivity identifies promiscuous bioactive compounds MK Matlock, TB Hughes, JL Dahlin, SJ Swamidass Journal of chemical information and modeling 58 (8), 1483-1500, 2018 | 39 | 2018 |
Deep learning quantification of percent steatosis in donor liver biopsy frozen sections L Sun, JN Marsh, MK Matlock, L Chen, JP Gaut, EM Brunt, SJ Swamidass, ... EBioMedicine 60, 2020 | 36 | 2020 |
‘Black box’to ‘conversational’machine learning: Ondansetron reduces risk of hospital-acquired venous thromboembolism A Datta, MK Matlock, N Le Dang, T Moulin, KF Woeltje, EL Yanik, ... IEEE Journal of Biomedical and Health Informatics 25 (6), 2204-2214, 2020 | 34 | 2020 |
Effective tag mechanisms for evolving coordination M Matlock, S Sen Proceedings of the 6th international joint conference on Autonomous agents …, 2007 | 34 | 2007 |
The metabolic rainbow: deep learning phase I metabolism in five colors NL Dang, MK Matlock, TB Hughes, SJ Swamidass Journal of chemical information and modeling 60 (3), 1146-1164, 2020 | 33 | 2020 |
Learning a local-variable model of aromatic and conjugated systems MK Matlock, NL Dang, SJ Swamidass ACS Central Science 4 (1), 52-62, 2018 | 28 | 2018 |
Scaffold network generator: a tool for mining molecular structures MK Matlock, JM Zaretzki, SJ Swamidass Bioinformatics 29 (20), 2655-2656, 2013 | 26 | 2013 |
Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam DA Barnette, MA Schleiff, LR Osborn, N Flynn, M Matlock, SJ Swamidass, ... Toxicology 440, 152478, 2020 | 19 | 2020 |
Deep learning long-range information in undirected graphs with wave networks MK Matlock, A Datta, N Le Dang, K Jiang, SJ Swamidass 2019 International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 18 | 2019 |
Combined analysis of phenotypic and target-based screening in assay networks SJ Swamidass, CN Schillebeeckx, M Matlock, MR Hurle, P Agarwal Journal of Biomolecular Screening 19 (5), 782-790, 2014 | 15 | 2014 |
Sharing chemical relationships does not reveal structures M Matlock, SJ Swamidass Journal of Chemical Information and Modeling 54 (1), 37-48, 2014 | 9 | 2014 |
Deep learning coordinate-free quantum chemistry MK Matlock, M Hoffman, NL Dang, DL Folmsbee, LA Langkamp, ... The Journal of Physical Chemistry A 125 (40), 8978-8986, 2021 | 8 | 2021 |
Securely measuring the overlap between private datasets with cryptosets SJ Swamidass, M Matlock, L Rozenblit PloS one 10 (2), e0117898, 2015 | 8 | 2015 |