 Connection
Theodore Randolph to Machine Learning
This is a "connection" page, showing publications Theodore Randolph has written about Machine Learning.
|
|
| |
Connection Strength |
|
 |
|
 |
| |
2.396 |
|
|
|
-
Greenblott DN, Calderon CP, Randolph TW. Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis. J Pharm Sci. 2025 02; 114(2):1254-1263.
Score: 0.574
-
Greenblott DN, Johann F, Snell JR, Gieseler H, Calderon CP, Randolph TW. Features in Backgrounds of Microscopy Images Introduce Biases in Machine Learning Analyses. J Pharm Sci. 2024 05; 113(5):1177-1189.
Score: 0.541
-
Thite NG, Ghazvini S, Wallace N, Feldman N, Calderon CP, Randolph TW. Machine Learning Analysis Provides Insight into Mechanisms of Protein Particle Formation Inside Containers During Mechanical Agitation. J Pharm Sci. 2022 10; 111(10):2730-2744.
Score: 0.482
-
Daniels AL, Calderon CP, Randolph TW. Machine learning and statistical analyses for extracting and characterizing "fingerprints" of antibody aggregation at container interfaces from flow microscopy images. Biotechnol Bioeng. 2020 11; 117(11):3322-3335.
Score: 0.421
-
Wang Y, Hanford A, Boroumand M, Kalonia C, Leissa J, Shah M, Pham T, Randolph T, Prajapati I. Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis. MAbs. 2025 12; 17(1):2501629.
Score: 0.147
-
Greenblott DN, Zhang J, Calderon CP, Randolph TW. Machine learning approaches to root cause analysis, characterization, and monitoring of subvisible particles in monoclonal antibody formulations. Biotechnol Bioeng. 2022 12; 119(12):3596-3611.
Score: 0.122
-
Witeof AE, Daniels AL, Rea LT, Movafaghi S, Kurtz K, Davis M, Eveland RW, Calderon CP, Randolph TW. Machine Learning and Accelerated Stress Approaches to Differentiate Potential Causes of Aggregation in Polyclonal Antibody Formulations During Shipping. J Pharm Sci. 2021 07; 110(7):2743-2752.
Score: 0.110
|
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.
|