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  1. 1.   High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts
  2. Harmon,Stephanie; Patel, Palak G; Sanford, Thomas H; Caven, Isabelle; Iseman, Rachael; Vidotto, Thiago; Picanço, Clarissa; Squire, Jeremy A; Masoudi, Samira; Mehralivand, Sherif; Choyke, Peter L; Berman, David M; Turkbey, Baris; Jamaspishvili, Tamara
  3. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2020, Sep 03;
  1. 2.   PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images
  2. Armato, Samuel G; Huisman, Henkjan; Drukker, Karen; Hadjiiski, Lubomir; Kirby, Justin; Petrick, Nicholas; Redmond, George; Giger, Maryellen L; Cha, Kenny; Mamonov, Artem; Kalpathy-Cramer, Jayashree; Farahani, Keyvan
  3. Journal of medical imaging (Bellingham, Wash.). 2018, Oct; 5(4): 044501.
  1. 3.   Use of Patient-specific MRI-based Prostate Mold for Validation of Multiparametric MRI in Localization of Prostate Cancer
  2. Trivedi, H.; Turkbey, B.; Rastinehad, A. R.; Benjamin, C. J.; Bernardo, M.; Pohida, T.; Shah, V.; Merino, M. J.; Wood, B. J.; Linehan, W. M.; Venkatesan, A. M.; Choyke, P. L.; Pinto, P. A.
  3. Urology. 2012, Jan; 79(1): 233-239.
  1. 4.   Whole cell segmentation in solid tissue sections
  2. Baggett, D.; Nakaya, M. A.; McAuliffe, M.; Yamaguchi, T. P.; Lockett, S.
  3. Cytometry Part A. 2005, OCT; 67A(2): 137-143.
  1. 5.   Gene expression profiles of serous, endometrioid, and clear cell subtypes of ovarian and endometrial cancer
  2. Zorn, K. K.; Bonome, T.; Gangi, L.; Chandramouli, G. V. R.; Awtrey, C. S.; Gardner, G. J.; Barrett, J. C.; Boyd, J.; Birrer, M. J.
  3. Clinical Cancer Research. 2005, SEP 15; 11(18): 6422-6430.
  1. 6.   Global transcriptional programs reveal a carbon source foraging strategy by Escherichia coli
  2. Liu, M. Z.; Durfee, T.; Cabrera, J. E.; Zhao, K.; Jin, D. J.; Blattner, F. R.
  3. Journal of Biological Chemistry. 2005, APR 22; 280(16): 15921-15927.
  1. 7.   Quantitative three-dimensional microscopy approaches with applications in breast cancer biology including measurement of genomic instability
  2. Lockett, S.; de Solorzano, C. O.; Baggett, D.; Chin, K.
  3. Journal of Mammary Gland Biology and Neoplasia. 2004, OCT; 9(4): 383-391.
  1. 8.   Simultaneous topography and recognition Imaging using force microscopy
  2. Stroh, C. M.; Ebner, A.; Geretschlager, M.; Freudenthaler, G.; Kienberger, F.; Kamruzzahan, A. S. M.; Smith-Gil, S. J.; Gruber, H. J.; Hinterdorfer, P.
  3. Biophysical Journal. 2004, SEP; 87(3): 1981-1990.
  1. 9.   Automatic and quantitative measurement of protein-protein colocalization in live cells
  2. Costes, S. V.; Daelemans, D.; Cho, E. H.; Dobbin, Z.; Pavlakis, G.; Lockett, S.
  3. Biophysical Journal. 2004 86(6): 3993-4003.
  1. 10.   The distribution of RNA polymerase in Escherichia coli is dynamic and sensitive to environmental cues
  2. Cabrera, J. E.; Jin, D. J.
  3. Molecular Microbiology. 2003 50(5): 1493-1505.
  1. 11.   The microarray explorer tool for data mining of cDNA microarrays: application for the mammary gland
  2. Lemkin, P. F.; Thornwall, G. C.; Walton, K. D.; Hennighausen, L.
  3. Nucleic Acids Research. 2000 28(22): 4452-4459.
  1. 12.   Exploratory data analysis groupware for qualitative and quantitative electrophoretic gel analysis over the Internet-WebGel
  2. Lemkin, P. F.; Myrick, J. M.; Lakshmanan, Y.; Shue, M. J.; Patrick, J. L.; Hornbeck, P. V.; Thornwal, G. C.; Partin, A. W.
  3. Electrophoresis. 1999 20(18): 3492-3507.
  1. 13.   Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
  2. Partin, Alexander; Brettin, Thomas; Zhu, Yitan; Dolezal, James M; Kochanny, Sara; Pearson, Alexander T; Shukla, Maulik; Evrard,Yvonne; Doroshow, James H; Stevens, Rick L
  3. Frontiers in Medicine. 2023 10: 1058919.
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