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  1. 1.   Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting
  2. Narykov, Oleksandr; Zhu, Yitan; Brettin, Thomas; Evrard,Yvonne; Partin, Alexander; Xia, Fangfang; Shukla, Maulik; Vasanthakumari, Priyanka; Doroshow, James H; Stevens, Rick L
  3. Briefings in Bioinformatics. 2025, Mar 04; 26(2):
  1. 2.   Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models
  2. Narykov, Oleksandr; Zhu, Yitan; Brettin, Thomas; Evrard,Yvonne; Partin, Alexander; Shukla, Maulik; Xia, Fangfang; Clyde, Austin; Vasanthakumari, Priyanka; Doroshow, James H; Stevens, Rick L
  3. Cancers. 2023, Dec 21; 16(1):
  1. 3.   Deep learning-based segmentation of multisite disease in ovarian cancer
  2. Buddenkotte, Thomas; Rundo, Leonardo; Woitek, Ramona; Escudero Sanchez, Lorena; Beer, Lucian; Crispin-Ortuzar, Mireia; Etmann, Christian; Mukherjee, Subhadip; Bura, Vlad; McCague, Cathal; Sahin, Hilal; Pintican, Roxana; Zerunian, Marta; Allajbeu, Iris; Singh, Naveena; Sahdev, Anju; Havrilesky, Laura; Cohn, David E; Bateman, Nicholas W; Conrads, Thomas P; Darcy, Kathleen M; Maxwell, G Larry; Freymann,John; Öktem, Ozan; Brenton, James D; Sala, Evis; Schönlieb, Carola-Bibiane
  3. European Radiology Experimental. 2023, Dec 07; 7(1): 77.
  1. 4.   Proteogenomic insights suggest druggable pathways in endometrial carcinoma
  2. Dou, Yongchao; Katsnelson, Lizabeth; Gritsenko, Marina A; Hu, Yingwei; Reva, Boris; Hong, Runyu; Wang, Yi-Ting; Kolodziejczak, Iga; Lu, Rita Jui-Hsien; Tsai, Chia-Feng; Bu, Wen; Liu, Wenke; Guo, Xiaofang; An, Eunkyung; Arend, Rebecca C; Bavarva, Jasmin; Chen, Lijun; Chu, Rosalie K; Czekanski, Andrzej; Davoli, Teresa; Demicco, Elizabeth G; DeLair, Deborah; Devereaux, Kelly; Dhanasekaran, Saravana M; Dottino, Peter; Dover, Bailee; Fillmore, Thomas L; Foxall, McKenzie; Hermann, Catherine E; Hiltke, Tara; Hostetter, Galen; Jedryka, Marcin; Jewell, Scott D; Johnson, Isabelle; Kahn, Andrea G; Ku, Amy T; Kumar-Sinha, Chandan; Kurzawa, Pawel; Lazar, Alexander J; Lazcano, Rossana; Lei, Jonathan T; Li, Yi; Liao, Yuxing; Lih, Tung-Shing M; Lin, Tai-Tu; Martignetti, John A; Masand, Ramya P; Matkowski, Rafal; McKerrow, Wilson; Mesri, Mehdi; Monroe, Matthew E; Moon, Jamie; Moore, Ronald J; Nestor, Michael D; Newton, Chelsea; Omelchenko, Tatiana; Omenn, Gilbert S; Payne, Samuel H; Petyuk, Vladislav A; Robles, Ana I; Rodriguez, Henry; Ruggles, Kelly V; Rykunov, Dmitry; Savage, Sara R; Schepmoes, Athena A; Shi, Tujin; Shi, Zhiao; Tan, Jimin; Taylor, Mason; Thiagarajan,Mathangi; Wang, Joshua M; Weitz, Karl K; Wen, Bo; Williams, C M; Wu, Yige; Wyczalkowski, Matthew A; Yi, Xinpei; Zhang, Xu; Zhao, Rui; Mutch, David; Chinnaiyan, Arul M; Smith, Richard D; Nesvizhskii, Alexey I; Wang, Pei; Wiznerowicz, Maciej; Ding, Li; Mani, D R; Zhang, Hui; Anderson, Matthew L; Rodland, Karin D; Zhang, Bing; Liu, Tao; Fenyö, David
  3. Cancer Cell. 2023, Sep 11; 41(9): 1586-1605.e15.
  1. 5.   AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus
  2. Patel,Chiragkumar; Mall, Raghvendra; Bensmail, Halima
  3. Journal of Infection and Public Health. 2023, Mar 15; 16(5): 799-807.
  1. 6.   Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models
  2. Reza, Syed M S; Chu, Winston T; Homayounieh, Fatemeh; Blain, Maxim; Firouzabadi, Fatemeh D; Anari, Pouria Y; Lee, Ji Hyun; Worwa, Gabriella; Finch, Courtney L; Kuhn, Jens H; Malayeri, Ashkan; Crozier,Ian; Wood, Bradford J; Feuerstein, Irwin M; Solomon,Jeffrey
  3. Academic Radiology. 2023, Feb 27;
  1. 7.   Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data
  2. Xu, Junlin; Xu, Jielin; Meng, Yajie; Lu, Changcheng; Cai, Lijun; Zeng, Xiangxiang; Nussinov,Ruth; Cheng, Feixiong
  3. Cell Reports Methods. 2023, Jan 23; 3(1): 100382.
  1. 8.   Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset
  2. Conrad, Ryan; Narayan,Kedar
  3. Cell Systems. 2023, Jan 18; 14(1): 58-71.e5.
  1. 9.   QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
  2. Mehta, Raghav; Filos, Angelos; Baid, Ujjwal; Sako, Chiharu; McKinley, Richard; Rebsamen, Michael; Dätwyler, Katrin; Meier, Raphael; Radojewski, Piotr; Murugesan, Gowtham Krishnan; Nalawade, Sahil; Ganesh, Chandan; Wagner, Ben; Yu, Fang F; Fei, Baowei; Madhuranthakam, Ananth J; Maldjian, Joseph A; Daza, Laura; Gómez, Catalina; Arbeláez, Pablo; Dai, Chengliang; Wang, Shuo; Reynaud, Hadrien; Mo, Yuanhan; Angelini, Elsa; Guo, Yike; Bai, Wenjia; Banerjee, Subhashis; Pei, Linmin; Ak, Murat; Rosas-González, Sarahi; Zemmoura, Ilyess; Tauber, Clovis; Vu, Minh H; Nyholm, Tufve; Löfstedt, Tommy; Ballestar, Laura Mora; Vilaplana, Veronica; McHugh, Hugh; Maso Talou, Gonzalo; Wang, Alan; Patel, Jay; Chang, Ken; Hoebel, Katharina; Gidwani, Mishka; Arun, Nishanth; Gupta, Sharut; Aggarwal, Mehak; Singh, Praveer; Gerstner, Elizabeth R; Kalpathy-Cramer, Jayashree; Boutry, Nicolas; Huard, Alexis; Vidyaratne, Lasitha; Rahman, Md Monibor; Iftekharuddin, Khan M; Chazalon, Joseph; Puybareau, Elodie; Tochon, Guillaume; Ma, Jun; Cabezas, Mariano; Llado, Xavier; Oliver, Arnau; Valencia, Liliana; Valverde, Sergi; Amian, Mehdi; Soltaninejad, Mohammadreza; Myronenko, Andriy; Hatamizadeh, Ali; Feng, Xue; Dou, Quan; Tustison, Nicholas; Meyer, Craig; Shah, Nisarg A; Talbar, Sanjay; Weber, Marc-André; Mahajan, Abhishek; Jakab, Andras; Wiest, Roland; Fathallah-Shaykh, Hassan M; Nazeri, Arash; Milchenko, Mikhail; Marcus, Daniel; Kotrotsou, Aikaterini; Colen, Rivka; Freymann,John; Kirby,Justin; Davatzikos, Christos; Menze, Bjoern; Bakas, Spyridon; Gal, Yarin; Arbel, Tal
  3. The journal of Machine Learning for Biomedical Imaging. 2022, Aug; 2022
  1. 10.   QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
  2. Mehta, Raghav; Filos, Angelos; Baid, Ujjwal; Sako, Chiharu; McKinley, Richard; Rebsamen, Michael; Dätwyler, Katrin; Meier, Raphael; Radojewski, Piotr; Murugesan, Gowtham Krishnan; Nalawade, Sahil; Ganesh, Chandan; Wagner, Ben; Yu, Fang F; Fei, Baowei; Madhuranthakam, Ananth J; Maldjian, Joseph A; Daza, Laura; Gómez, Catalina; Arbeláez, Pablo; Dai, Chengliang; Wang, Shuo; Reynaud, Hadrien; Mo, Yuanhan; Angelini, Elsa; Guo, Yike; Bai, Wenjia; Banerjee, Subhashis; Pei, Linmin; Ak, Murat; Rosas-González, Sarahi; Zemmoura, Ilyess; Tauber, Clovis; Vu, Minh H; Nyholm, Tufve; Löfstedt, Tommy; Ballestar, Laura Mora; Vilaplana, Veronica; McHugh, Hugh; Maso Talou, Gonzalo; Wang, Alan; Patel, Jay; Chang, Ken; Hoebel, Katharina; Gidwani, Mishka; Arun, Nishanth; Gupta, Sharut; Aggarwal, Mehak; Singh, Praveer; Gerstner, Elizabeth R; Kalpathy-Cramer, Jayashree; Boutry, Nicolas; Huard, Alexis; Vidyaratne, Lasitha; Rahman, Md Monibor; Iftekharuddin, Khan M; Chazalon, Joseph; Puybareau, Elodie; Tochon, Guillaume; Ma, Jun; Cabezas, Mariano; Llado, Xavier; Oliver, Arnau; Valencia, Liliana; Valverde, Sergi; Amian, Mehdi; Soltaninejad, Mohammadreza; Myronenko, Andriy; Hatamizadeh, Ali; Feng, Xue; Dou, Quan; Tustison, Nicholas; Meyer, Craig; Shah, Nisarg A; Talbar, Sanjay; Weber, Marc-André; Mahajan, Abhishek; Jakab, Andras; Wiest, Roland; Fathallah-Shaykh, Hassan M; Nazeri, Arash; Milchenko, Mikhail; Marcus, Daniel; Kotrotsou, Aikaterini; Colen, Rivka; Freymann,John; Kirby,Justin; Davatzikos, Christos; Menze, Bjoern; Bakas, Spyridon; Gal, Yarin; Arbel, Tal
  3. The Journal of Machine Learning for Biomedical Imaging. 2022, Aug; 2022
  1. 11.   BETA: a comprehensive benchmark for computational drug-target prediction
  2. Zong, Nansu; Li,Ning; Wen, Andrew; Ngo, Victoria; Yu, Yue; Huang, Ming; Chowdhury, Shaika; Jiang, Chao; Fu, Sunyang; Weinshilboum, Richard; Jiang, Guoqian; Hunter, Lawrence; Liu, Hongfang
  3. Briefings in Bioinformatics. 2022, Jun 02;
  1. 12.   A cross-study analysis of drug response prediction in cancer cell lines
  2. Xia, Fangfang; Allen, Jonathan; Balaprakash, Prasanna; Brettin, Thomas; Garcia-Cardona, Cristina; Clyde, Austin; Cohn, Judith; Doroshow,Jim; Duan, Xiaotian; Dubinkina, Veronika; Evrard,Yvonne; Fan, Ya Ju; Gans, Jason; He, Stewart; Lu,Pinyi; Maslov, Sergei; Partin, Alexander; Shukla, Maulik; Stahlberg,Eric; Wozniak, Justin M; Yoo, Hyunseung; Zaki,George; Zhu, Yitan; Stevens, Rick
  3. Briefings in Bioinformatics. 2021, Sep 14;
  1. 13.   Federated learning improves site performance in multicenter deep learning without data sharing
  2. Sarma, Karthik; Harmon,Stephanie; Sanford, Thomas; Roth, Holger R.; Xu, Ziyue; Tetreault, Jesse; Xu, Daguang; Flores, Mona G.; Raman, Alex G.; Kulkarni, Rushikesh; Wood, Bradford J.; Choyke, Peter L.; Priester, Alan M.; Marks, Leonard S.; Raman, Steven S.; Enzmann, Dieter; Turkbey, Baris; Speier, William; Arnold, Corey W.
  3. Journal of the American Medical Informatics Association : JAMIA. 2021, Jun 12; 28(6): 1259-1264.
  1. 14.   Learning curves for drug response prediction in cancer cell lines
  2. Partin, Alexander; Brettin, Thomas; Evrard,Yvonne; Zhu, Yitan; Yoo, Hyunseung; Xia, Fangfang; Jiang, Songhao; Clyde, Austin; Shukla, Maulik; Fonstein, Michael; Doroshow, James H.; Stevens, Rick L.
  3. BMC bioinformatics. 2021, May 17; 22(1):
  1. 15.   Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019
  2. Li, Zhang; Zhang, Jiehua; Tan, Tao; Teng, Xichao; Sun, Xiaoliang; Zhao, Hong; Liu, Lihong; Xiao, Yang; Lee, Byungjae; Li, Yilong; Zhang, Qianni; Sun, Shujiao; Zheng, Yushan; Yan, Junyu; Li, Ni; Hong, Yiyu; Ko, Junsu; Jung,Hyun; Liu,Yanling; Chen, Yu-cheng; Wang, Ching-wei; Yurovskiy, Vladimir; Maevskikh, Pavel; Khanagha, Vahid; Jiang, Yi; Yu, Li; Liu, Zhihong; Li, Daiqiang; Schueffler, Peter J.; Yu, Qifeng; Chen, Hui; Tang, Yuling; Litjens, Geert
  3. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. 2021, Feb; 25(2): 429-440.
  1. 16.   A Deep Learning Pipeline for Nucleus Segmentation
  2. Zaki,George; Gudla, Prabhakar R; Lee, Kyunghun; Kim, Justin; Ozbun, Laurent; Shachar, Sigal; Gadkari, Manasi; Sun, Jing; Fraser, Iain D C; Franco, Luis M; Misteli, Tom; Pegoraro, Gianluca
  3. CYTOMETRY PART A. 2020, NOV 19;
  1. 17.   Deep Learning for Automated Liver Segmentation to Aid in the Study of Infectious Diseases in Nonhuman Primates
  2. Reza, Syed M S; Bradley, Dara; Aiosa, Nina; Castro, Marcelo; Lee, Ji Hyun; Lee, Byeong-Yeul; Bennett, Richard S; Hensley, Lisa E; Cong, Yu; Johnson, Reed; Hammoud, Dima; Feuerstein, Irwin; Solomon,Jeffrey
  3. Academic radiology. 2020, Nov; 28(Supplement 1): pii: S1076-6332(20)30504-3.
  1. 18.   Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
  2. Zhang, Ling; Wang, Xiaosong; Yang, Dong; Sanford, Thomas; Harmon,Stephanie; Turkbey, Baris; Wood, Bradford J.; Roth, Holger; Myronenko, Andriy; Xu, Daguang; Xu, Ziyue
  3. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2020, JUL; 39(7): 2531-2540.
  1. 19.   Predicting RNA SHAPE scores with deep learning
  2. Bliss, Noah; Bindewald,Eckart; Shapiro,Bruce
  3. RNA biology. 2020, JUN 1; 1-7.
  1. 20.   Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study
  2. Sanford, Thomas; Harmon,Stephanie; Turkbey, Evrim B; Kesani, Deepak; Tuncer, Sena; Madariaga, Manuel; Yang, Chris; Sackett, Jonathan; Mehralivand, Sherif; Yan, Pingkun; Xu, Sheng; Wood, Bradford J; Merino, Maria J; Pinto, Peter A; Choyke, Peter L; Turkbey, Baris
  3. Journal of magnetic resonance imaging : JMRI. 2020, Jun 01;
  1. 21.   Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
  2. Turner, Oliver C; Aeffner, Famke; Bangari, Dinesh S; High, Wanda; Knight, Brian; Forest, Tom; Cossic, Brieuc; Himmel, Lauren E; Rudmann, Daniel G; Bawa, Bhupinder; Muthuswamy, Anantharaman; Aina, Olulanu H; Edmondson,Elijah; Saravanan, Chandrassegar; Brown, Danielle L; Sing, Tobias; Sebastian, Manu M
  3. Toxicologic pathology. 2019, Oct 23; 192623319881401.
  1. 22.   AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing
  2. Bhattacharya, Tanmoy; Brettin, Thomas; Doroshow, James H.; Evrard,Yvonne; Greenspan, Emily J.; Gryshuk, Amy L.; Hoang, Thuc T.; Lauzon, Carolyn B. Vea; Nissley,Dwight; Penberthy, Lynne; Stahlberg,Eric; Stevens, Rick; Streitz, Fred; Tourassi, Georgia; Xia, Fangfang; Zaki,George
  3. Frontiers in oncology. 2019, Oct 2; 9
  1. 23.   Predicting tumor cell line response to drug pairs with deep learning
  2. Xia, Fangfang; Shukla, Maulik; Brettin, Thomas; Garcia-Cardona, Cristina; Cohn, Judith; Allen, Jonathan E.; Maslov, Sergei; Holbeck, Susan L.; Doroshow, Jim; Evrard, Yvonne; Stahlberg, Eric; Stevens, Rick L.
  3. BMC bioinformatics. 2018, Dec 21; 19(Supp 18):
  1. 24.   Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images
  2. Jung,Hyun; Suloway,Christian; Miao,Tianyi; Edmondson,Elijah; Morcock,David; Deleage,Claire; Liu,Yanling; Collins,Jack; Lisle, Curtis
  3. 2018 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR). 2018, OCT 09-11;
  1. 25.   TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks
  2. Jones,Sara; Beyers,Matt; Shukla, Maulik; Xia, Fangfang; Brettin, Thomas; Stevens, Rick; Weil, M Ryan; Ranganathan Ganakammal,Satishkumar
  3. Cancer Informatics. 2022 21: 11769351221139491.
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