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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.   Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease
  2. Qiu, Yunguang; Hou, Yuan; Gohel, Dhruv; Zhou, Yadi; Xu, Jielin; Bykova, Marina; Yang, Yuxin; Leverenz, James B; Pieper, Andrew A; Nussinov,Ruth; Caldwell, Jessica Z K; Brown, J Mark; Cheng, Feixiong
  3. Cell Reports. 2024, Apr 21; 43(5): 114128.
  1. 3.   A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening
  2. Vasanthakumari, Priyanka; Zhu, Yitan; Brettin, Thomas; Partin, Alexander; Shukla, Maulik; Xia, Fangfang; Narykov, Oleksandr; Weil,Michael; Stevens, Rick L
  3. Cancers. 2024, Jan 26; 16(3):
  1. 4.   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. 5.   Machine Learning Informs RNA-Binding Chemical Space
  2. Yazdani, Kamyar; Jordan, Deondre; Yang,Mo; Fullenkamp,Chris; Schneekloth,Jay; Calabrese,Dave; Boer, Robert E; Hilimire, Thomas A; Allen, Timothy E H; Khan, Rabia T
  3. Angewandte Chemie (International ed. in English). 2022, Dec 30;
  1. 6.   Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles
  2. Chandler, Morgan; Jain, Sankalp; Halman, Justin; Hong, Enping; Dobrovolskaia,Marina; Zakharov, Alexey V; Afonin, Kirill A
  3. Small (Weinheim an der Bergstrasse, Germany). 2022, Oct 10; e2204941.
  1. 7.   Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation
  2. Stahlberg,Eric; Abdel-Rahman, Mohamed; Aguilar, Boris; Asadpoure, Alireza; Beckman, Robert A; Borkon,Lynn; Bryan, Jeffrey N; Cebulla, Colleen M; Chang, Young Hwan; Chatterjee, Ansu; Deng, Jun; Dolatshahi, Sepideh; Gevaert, Olivier; Greenspan, Emily J; Hao, Wenrui; Hernandez-Boussard, Tina; Jackson, Pamela R; Kuijjer, Marieke; Lee, Adrian; Macklin, Paul; Madhavan, Subha; McCoy, Matthew D; Mohammad Mirzaei, Navid; Razzaghi, Talayeh; Rocha, Heber L; Shahriyari, Leili; Shmulevich, Ilya; Stover, Daniel G; Sun, Yi; Syeda-Mahmood, Tanveer; Wang, Jinhua; Wang, Qi; Zervantonakis, Ioannis
  3. Frontiers in Digital Health. 2022, Oct 6; 4: 1007784.
  1. 8.   Theoretical classification of exchange geometries from the perspective of NMR relaxation dispersion
  2. Chao,Fa-An; Zhang,Yue; Byrd,Robert
  3. Journal of magnetic resonance (San Diego, Calif. : 1997). 2021, Jul; 328
  1. 9.   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. 10.   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. 11.   Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions
  2. Liu, Ge; Carter, Brandon; Bricken, Trenton; Jain, Siddhartha; Viard,Mathias; Carrington,Mary; Gifford, David K
  3. Cell systems. 2020, AUG 26; 11(2): 131-144.e6.
  1. 12.   Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery
  2. Hinkson, Izumi V; Madej,Benjamin; Stahlberg,Eric
  3. Frontiers in pharmacology. 2020, Jun 30; 11: 770.
  1. 13.   Computational network biology: Data, models, and applications
  2. Liu, Chuang; Ma, Yifang; Zhao, Jing; Nussinov,Ruth; Zhang, Yi-Cheng; Cheng, Feixiong; Zhang, Zi-Ke
  3. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS. 2020, MAR 3; 846: 1-66.
  1. 14.   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. 15.   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. 16.   Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges
  2. Elhalawani, Hesham; Lin, Timothy A.; Volpe, Stefania; Mohamed, Abdallah S. R.; White, Aubrey L.; Zafereo, James; Wong, Andrew J.; Berends, Joel E.; AboHashem, Shady; Williams, Bowman; Aymard, Jeremy M.; Kanwar, Aasheesh; Perni, Subha; Rock, Crosby D.; Cooksey, Luke; Campbell, Shauna; Yang, Pei; Ger, Rachel B.; Cardenas, Carlos E.; Fave, Xenia J.; Sansone, Carlo; Piantadosi, Gabriele; Marrone, Stefano; Liu, Rongjie; Huang, Chao; Yu, Kaixian; Li, Tengfei; Yu, Yang; Zhang, Youyi; Zhu, Hongtu; Morris, Jeffrey S.; Baladandayuthapani, Veerabhadran; Shumway, John W.; Ghosh, Alakonanda; Poehlmann, Andrei; Phoulady, Hady A.; Goyal, Vibhas; Canahuate, Guadalupe; Marai, G. Elisabeta; Vock, David; Lai, Stephen Y.; Mackin, Dennis S.; Court, Laurence E.; Freymann, John; Farahani, Keyvan; Kaplathy-Cramer, Jayashree; Fuller, Clifton D.
  3. Frontiers in Oncology. 2018, Aug 17; 8: 294.
  1. 17.   RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers
  2. Bindewald, E.; Shapiro, B. A.
  3. Rna-a Publication of the Rna Society. 2006 12(3): 342-352.
  1. 18.   A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer
  2. Di Natale, Francesco; Bhatia, Harsh; Carpenter, Timothy S.; Neale, Chris; Kokkila-Schumacher, Sara; Oppelstrup, Tomas; Stanton, Liam; Zhang, Xiaohua; Sundram, Shiv; Scogland, Thomas R. W.; Dharuman, Gautham; Surh, Michael P.; Yang, Yue; Misale, Claudia; Schneidenbach, Lars; Costa, Carlos; Kim, Changhoan; D'Amora, Bruce; Gnanakaran, Sandrasegaram; Nissley,Dwight; Streitz, Fred; Lightstone, Felice C.; Bremer, Peer-Timo; Glosli, James N.; Ingolfsson, Helgi I.
  3. 2019; .
  1. 19.   cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine
  2. Long, Jiaxin; Ganakammal, Satishkumar Ranganathan; Jones,Sara; Kothandaraman, Harish; Dhawan, Deepika; Ogas, Joe; Knapp, Deborah W; Beyers, Matthew; Lanman, Nadia A
  3. Frontiers in Oncology. 2023 13: 1216892.
  1. 20.   Improved detection of low-frequency within-host variants from deep sequencing: A case study with human papillomavirus
  2. Mishra,Sambit; Nelson, Chase W; Zhu, Bin; Pinheiro, Maisa; Lee,Hyo Jung; Dean, Michael; Burdett,Laurie; Yeager,Meredith; Mirabello, Lisa
  3. Virus Evolution. 2024 10(1): veae013.
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