 Connection
David Lynch to Machine Learning
This is a "connection" page, showing publications David Lynch has written about Machine Learning.
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Connection Strength |
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0.722 |
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Castaldi PJ, Boueiz A, Yun J, Estepar RSJ, Ross JC, Washko G, Cho MH, Hersh CP, Kinney GL, Young KA, Regan EA, Lynch DA, Criner GJ, Dy JG, Rennard SI, Casaburi R, Make BJ, Crapo J, Silverman EK, Hokanson JE. Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study. Chest. 2020 05; 157(5):1147-1157.
Score: 0.133
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Uthoff J, Stephens MJ, Newell JD, Hoffman EA, Larson J, Koehn N, De Stefano FA, Lusk CM, Wenzlaff AS, Watza D, Neslund-Dudas C, Carr LL, Lynch DA, Schwartz AG, Sieren JC. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys. 2019 Jul; 46(7):3207-3216.
Score: 0.128
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Raghu G, Flaherty KR, Lederer DJ, Lynch DA, Colby TV, Myers JL, Groshong SD, Larsen BT, Chung JH, Steele MP, Benzaquen S, Calero K, Case AH, Criner GJ, Nathan SD, Rai NS, Ramaswamy M, Hagmeyer L, Davis JR, Gauhar UA, Pankratz DG, Choi Y, Huang J, Walsh PS, Neville H, Lofaro LR, Barth NM, Kennedy GC, Brown KK, Martinez FJ. Use of a molecular classifier to identify usual interstitial pneumonia in conventional transbronchial lung biopsy samples: a prospective validation study. Lancet Respir Med. 2019 06; 7(6):487-496.
Score: 0.127
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Pankratz DG, Choi Y, Imtiaz U, Fedorowicz GM, Anderson JD, Colby TV, Myers JL, Lynch DA, Brown KK, Flaherty KR, Steele MP, Groshong SD, Raghu G, Barth NM, Walsh PS, Huang J, Kennedy GC, Martinez FJ. Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning. Ann Am Thorac Soc. 2017 Nov; 14(11):1646-1654.
Score: 0.115
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Kim SY, Diggans J, Pankratz D, Huang J, Pagan M, Sindy N, Tom E, Anderson J, Choi Y, Lynch DA, Steele MP, Flaherty KR, Brown KK, Farah H, Bukstein MJ, Pardo A, Selman M, Wolters PJ, Nathan SD, Colby TV, Myers JL, Katzenstein AL, Raghu G, Kennedy GC. Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data. Lancet Respir Med. 2015 Jun; 3(6):473-82.
Score: 0.097
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Beswick DM, Humphries SM, Balkissoon CD, Vladar EK, Ramakrishnan VR, Lynch DA, Taylor-Cousar JL. Machine learning evaluates improvement in sinus computed tomography opacification with CFTR modulator therapy. Int Forum Allergy Rhinol. 2021 05; 11(5):953-954.
Score: 0.035
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Choi Y, Liu TT, Pankratz DG, Colby TV, Barth NM, Lynch DA, Walsh PS, Raghu G, Kennedy GC, Huang J. Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions. BMC Genomics. 2018 May 09; 19(Suppl 2):101.
Score: 0.030
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Kennedy GC, Barth NM, Walsh PS, Huang J, Pankratz DG, Choi Y, Fedorowicz GM, Anderson JD, Raghu G, Martinez FJ, Colby TV, Lynch DA, Brown KK, Groshong SD, Myers JL, Flaherty KR, Steele MP. Reply: Improving Care for Patients with Interstitial Lung Disease, Using Machine Learning, Requires Transparency and Reproducibility. Ann Am Thorac Soc. 2017 12; 14(12):1864-1865.
Score: 0.029
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Cheplygina V, Pena IP, Pedersen JH, Lynch DA, Sorensen L, de Bruijne M. Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease. IEEE J Biomed Health Inform. 2018 09; 22(5):1486-1496.
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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