 Connection
Co-Authors
This is a "connection" page, showing publications co-authored by Christopher Calderon and Theodore Randolph.
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Connection Strength |
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2.743 |
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Calderon CP, Daniels AL, Randolph TW. Deep Convolutional Neural Network Analysis of Flow Imaging Microscopy Data to Classify Subvisible Particles in Protein Formulations. J Pharm Sci. 2018 04; 107(4):999-1008.
Score: 0.590
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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 Feb; 114(2):1254-1263.
Score: 0.241
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Thite NG, Tuberty-Vaughan E, Wilcox P, Wallace N, Calderon CP, Randolph TW. Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning. J Pharm Sci. 2024 Aug; 113(8):2114-2127.
Score: 0.230
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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: 0.227
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Greenblott DN, Wood CV, Zhang J, Viza N, Chintala R, Calderon CP, Randolph TW. Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation. Biotechnol Bioeng. 2024 May; 121(5):1626-1641.
Score: 0.226
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Thite NG, Ghazvini S, Wallace N, Feldman N, Calderon CP, Randolph TW. Interfacial Adsorption Controls Particle Formation in Antibody Formulations Subjected to Extensional Flows and Hydrodynamic Shear. J Pharm Sci. 2023 Nov; 112(11):2766-2777.
Score: 0.217
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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: 0.206
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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: 0.202
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Calderon CP, Ripple DC, Srinivasan C, Ma Y, Carrier MJ, Randolph TW, O'Connor TF. Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy. Pharm Res. 2022 Feb; 39(2):263-279.
Score: 0.196
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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: 0.184
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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: 0.177
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Movafaghi S, Daniels AL, Kelly MD, Witeof AE, Calderon CP, Randolph TW, Goodwin AP. Hydrogel Coatings on Container Surfaces Reduce Protein Aggregation Caused by Mechanical Stress and Cavitation. ACS Appl Bio Mater. 2021 09 20; 4(9):6946-6953.
Score: 0.047
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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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