 Connection
Gregory Way to Machine Learning
This is a "connection" page, showing publications Gregory Way has written about Machine Learning.
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Connection Strength |
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1.855 |
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Chow YL, Singh S, Carpenter AE, Way GP. Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic. PLoS Comput Biol. 2022 02; 18(2):e1009888.
Score: 0.618
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Way GP, Sanchez-Vega F, La K, Armenia J, Chatila WK, Luna A, Sander C, Cherniack AD, Mina M, Ciriello G, Schultz N, Sanchez Y, Greene CS. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep. 2018 04 03; 23(1):172-180.e3.
Score: 0.472
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Way GP, Allaway RJ, Bouley SJ, Fadul CE, Sanchez Y, Greene CS. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma. BMC Genomics. 2017 02 06; 18(1):127.
Score: 0.436
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Way GP, Kost-Alimova M, Shibue T, Harrington WF, Gill S, Piccioni F, Becker T, Shafqat-Abbasi H, Hahn WC, Carpenter AE, Vazquez F, Singh S. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021 04 19; 32(9):995-1005.
Score: 0.144
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Way GP, Greene CS. Bayesian deep learning for single-cell analysis. Nat Methods. 2018 12; 15(12):1009-1010.
Score: 0.123
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Lin YT, Way GP, Barwick BG, Mariano MC, Marcoulis M, Ferguson ID, Driessen C, Boise LH, Greene CS, Wiita AP. Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma. Blood Adv. 2019 11 12; 3(21):3214-3227.
Score: 0.033
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Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS, Liu Y, Akbani R, Feng B, Donehower LA, Miller C, Shen Y, Karimi M, Chen H, Kim P, Jia P, Shinbrot E, Zhang S, Liu J, Hu H, Bailey MH, Yau C, Wolf D, Zhao Z, Weinstein JN, Li L, Ding L, Mills GB, Laird PW, Wheeler DA, Shmulevich I, Monnat RJ, Xiao Y, Wang C. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep. 2018 04 03; 23(1):239-254.e6.
Score: 0.029
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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