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Journal articles on the topic 'Dose prediction'

Author: Grafiati

Published: 24 June 2021

Last updated: 29 July 2025

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1

Swartz, Conrad M. "Drug Dose Prediction With Flexible Test Doses." Journal of Clinical Pharmacology 31, no. 7 (1991): 662–67. http://dx.doi.org/10.1002/j.1552-4604.1991.tb03753.x.

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2

&NA;. "IV aminoglycoside dose prediction." Inpharma Weekly &NA;, no. 995 (1995): 18. http://dx.doi.org/10.2165/00128413-199509950-00043.

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3

Liu, Y., Z. Chen, Q. Zhou, et al. "A Feasibility Study of Dose Band Prediction in Radiotherapy: Predicting a Dose Spectrum." International Journal of Radiation Oncology*Biology*Physics 117, no. 2 (2023): e691. http://dx.doi.org/10.1016/j.ijrobp.2023.06.2164.

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4

Gizynska, M., D. Blatkiewicz, B. Czyzew, et al. "EP-1510: Cumulated dose prediction." Radiotherapy and Oncology 115 (April 2015): S822—S823. http://dx.doi.org/10.1016/s0167-8140(15)41502-6.

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Mitchel, R. E. J. "Radiation Risk Prediction and Genetics: The Influence of the TP53 Gene in vivo." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.007.

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Risk prediction and dose limits for human radiation exposure are based on the assumption that risk is proportional to total dose. However, there is concern about the appropriateness of those limits for people who may be genetically cancer prone. The TP53 gene product functions in regulatory pathways for DNA repair, cell cycle checkpoints and apoptosis, processes critical in determining ionizing radiation risk for both carcinogenesis and teratogenesis. Mice that are deficient in TP53 function are cancer prone. This review examines the influence of variations in TP53 gene activity on cancer and

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6

Polizzi, Mitchell, Robert W. Watkins, and William T. Watkins. "Data-Driven Dose-Volume Histogram Prediction." Advances in Radiation Oncology 7, no. 2 (2022): 100841. http://dx.doi.org/10.1016/j.adro.2021.100841.

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7

Holford, Nick H. G., Shu C. Ma, and Brian J. Anderson. "Prediction of morphine dose in humans." Pediatric Anesthesia 22, no. 3 (2011): 209–22. http://dx.doi.org/10.1111/j.1460-9592.2011.03782.x.

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8

MIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, no. 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.

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OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "RADIATION DOSE PREDICTION FOR DETACHED HOUSES." Journal of Environmental Engineering (Transactions of AIJ) 82, no. 735 (2017): 481–89. http://dx.doi.org/10.3130/aije.82.481.

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10

Lippi, Giuseppe, Gian Luca Salvagno, and Gian Cesare Guidi. "Genetic Factors for Warfarin Dose Prediction." Clinical Chemistry 53, no. 9 (2007): 1721–22. http://dx.doi.org/10.1373/clinchem.2007.092338.

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11

Kote, Alka S., and Dnyaneshwar V. Wadkar. "Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks." Engineering, Technology & Applied Science Research 9, no. 3 (2019): 4176–81. https://doi.org/10.5281/zenodo.3249101.

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Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for ch

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12

Marek, Elizabeth, Jeremiah D. Momper, Ronald N. Hines, et al. "Prediction of Warfarin Dose in Pediatric Patients: An Evaluation of the Predictive Performance of Several Models." Journal of Pediatric Pharmacology and Therapeutics 21, no. 3 (2016): 224–32. http://dx.doi.org/10.5863/1551-6776-21.3.224.

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OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing p

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13

Li, Zhen, Zhenyu Yang, Jiayu Lu, et al. "Deep learning-based dose map prediction for high-dose-rate brachytherapy." Physics in Medicine & Biology 68, no. 17 (2023): 175015. http://dx.doi.org/10.1088/1361-6560/acecd2.

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Abstract Background. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction techniques have been reported as promising solutions with high efficiency and accuracy. However, current dose prediction studies mainly target EBRT which are inappropriate for brachytherapy, the model designed specifically for brachytherapy has not yet well-established. Purpose. To predict dose distribution in brachytherapy using a novel Squeeze and Excitation Attention Net (SE_AN) model. Method. We hypothesized the tracks of 192I

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14

George, Alex, Bogdan R. Dinu, and Russell E. Ware. "Ndepth: Novel Dose Escalation to Predict Treatment with Hydroxyurea." Blood 126, no. 23 (2015): 3419. http://dx.doi.org/10.1182/blood.v126.23.3419.3419.

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Abstract Several clinical trials have demonstrated that hydroxyurea therapy offers significant benefits for infants, children, and adolescents with sickle cell anemia. Patients on hydroxyurea who achieve a stable maximum tolerated dose (MTD), defined by a target level of mild marrow suppression, have greater laboratory and clinical benefits than those maintained on a lower dose. A complicating factor in achieving MTD is the significant inter-patient variability in MTD, but no way currently to predict the MTD for individual patients. As such, MTD is commonly achieved by gradual dose escalation

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15

Madakasira, Sudhakar, and Prabhaker G. Khazanie. "Reliability of amitriptyline dose prediction based on single-dose plasma levels." Clinical Pharmacology and Therapeutics 37, no. 2 (1985): 145–49. http://dx.doi.org/10.1038/clpt.1985.26.

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16

Teuschler, Linda K., Richard C. Hertzberg, Anthony McDonald, Yusupha Mahtarr Sey, and Jane Ellen Simmons. "Evaluation of a Proportional Response Addition Approach to Mixture Risk Assessment and Predictive Toxicology Using Data on Four Trihalomethanes from the U.S. EPA’s Multiple-Purpose Design Study." Toxics 12, no. 4 (2024): 240. http://dx.doi.org/10.3390/toxics12040240.

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In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxic

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17

Yan, Aixia, Zhi Wang, Jiaxuan Li, and Meng Meng. "Human Oral Bioavailability Prediction of Four Kinds of Drugs." International Journal of Computational Models and Algorithms in Medicine 3, no. 4 (2012): 29–42. http://dx.doi.org/10.4018/ijcmam.2012100104.

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In the development of drugs intended for oral use, good drug absorption and appropriate drug delivery are very important. Now the predictions for drug absorption and oral bioavailability follow similar approach: calculate molecular descriptors for molecules and build the prediction models. This approach works well for the prediction of compounds which cross a cell membrane from a region of high concentration to one of low concentration, but it does not work very well for the prediction of oral bioavailability, which represents the percentage of an oral dose which is able to produce a pharmacol

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18

Hines, J. W., L. W. Townsend, and T. F. Nichols. "SPE dose prediction using locally weighted regression." Radiation Protection Dosimetry 116, no. 1-4 (2005): 131–34. http://dx.doi.org/10.1093/rpd/nci010.

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19

Hines, J. W., L. W. Townsend, and T. F. Nichols. "SPE dose prediction using locally weighted regression." Radiation Protection Dosimetry 116, no. 1-4 (2005): 232–35. http://dx.doi.org/10.1093/rpd/nci278.

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20

Tan, Hai Siong, Kuancheng Wang, and Rafe McBeth. "Deep evidential learning for radiotherapy dose prediction." Computers in Biology and Medicine 182 (November 2024): 109172. http://dx.doi.org/10.1016/j.compbiomed.2024.109172.

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21

Delasalles, Edouard, Rémi Vauclin, Elie Mengin, et al. "1377: Dose Prediction for Prostate Radiotherapy Planning." Radiotherapy and Oncology 194 (May 2024): S3560—S3562. http://dx.doi.org/10.1016/s0167-8140(24)01785-7.

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22

Skarpman Munter, Johanna, and Jens Sjölund. "Dose-volume histogram prediction using density estimation." Physics in Medicine and Biology 60, no. 17 (2015): 6923–36. http://dx.doi.org/10.1088/0031-9155/60/17/6923.

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23

OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "MONTE CARLO CALCULATION FOR RADIATION DOSE PREDICTION." Journal of Environmental Engineering (Transactions of AIJ) 81, no. 727 (2016): 835–43. http://dx.doi.org/10.3130/aije.81.835.

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24

Hu, Qiaozhi, Hualing Wang, and Ting Xu. "Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning." Journal of Clinical Medicine 12, no. 4 (2023): 1599. http://dx.doi.org/10.3390/jcm12041599.

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An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient charact

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25

Mortazavi, S. M. J., Fatemeh Aminiazad, Hossein Parsaei, and Mohammad Amin Mosleh-Shirazi. "AN ARTIFICIAL NEURAL NETWORK-BASED MODEL FOR PREDICTING ANNUAL DOSE IN HEALTHCARE WORKERS OCCUPATIONALLY EXPOSED TO DIFFERENT LEVELS OF IONIZING RADIATION." Radiation Protection Dosimetry 189, no. 1 (2020): 98–105. http://dx.doi.org/10.1093/rpd/ncaa018.

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Abstract We presented an artificial intelligence-based model to predict annual effective dose (AED) value of health workers. Potential factors affecting AED and the results of annual blood tests were collected from 91 radiation workers. Filter-based feature selection strategy revealed that the eight factors plate, red cell distribution width (RDW), educational degree, nonacademic course in radiation protection (hour), working hours per month, department and the number of procedures done per year and work in radiology department or not (0,1) were the most important predictors for AED. The predi

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26

Bai, Xue, Ze Liu, Jie Zhang, et al. "Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer." Science Progress 104, no. 3 (2021): 003685042110381. http://dx.doi.org/10.1177/00368504211038162.

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Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography image

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27

Neal, John S., and Lawrence W. Townsend. "Prediction of solar particle event proton doses using early dose rate measurements." Acta Astronautica 56, no. 9-12 (2005): 961–68. http://dx.doi.org/10.1016/j.actaastro.2005.01.023.

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28

Changshi, Liu. "Prediction of the bias currents induced by60Co via dose and dose rate." Radiation Effects and Defects in Solids 167, no. 4 (2012): 275–80. http://dx.doi.org/10.1080/10420150.2011.642870.

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29

BERG, R., S. KLASH, and M. GOSSMAN. "Surface dose prediction and verification for IMRT plans using line dose profiles." International Journal of Radiation OncologyBiologyPhysics 60 (September 2004): S590. http://dx.doi.org/10.1016/s0360-3016(04)01891-7.

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30

Berg, R., S. Klash, and M. Gossman. "Surface dose prediction and verification for IMRT plans using line dose profiles." International Journal of Radiation Oncology*Biology*Physics 60, no. 1 (2004): S590. http://dx.doi.org/10.1016/j.ijrobp.2004.07.587.

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31

Siccardi, Marco, Laura Dickinson, and Andrew Owen. "Validation of Computational Approaches for Antiretroviral Dose Optimization." Antimicrobial Agents and Chemotherapy 60, no. 6 (2016): 3838–39. http://dx.doi.org/10.1128/aac.00094-16.

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Strategies for reducing antiretroviral doses and drug costs can support global access, and numerous options are being investigated. Efavirenz pharmacokinetic simulation data generated with a bottom-up physiologically based model were successfully compared with data obtained from the ENCORE (Exercise and Nutritional Interventions for Cardiovascular Health) I clinical trial (efavirenz at 400 mg once per day versus 600 mg once per day). These findings represent a pivotal paradigm for the prediction of pharmacokinetics resulting from dose reductions. Validated computational models constitute a val

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32

George, Alex, Bogdan Dinu, Norma Estrada, et al. "Ndepth: A Randomized Controlled Trial of a Novel Dose-Prediction Equation to Determine Maximum Tolerated Dose for Hydroxyurea Therapy in Pediatric Patients with Sickle Cell Anemia." Blood 134, Supplement_1 (2019): 2267. http://dx.doi.org/10.1182/blood-2019-127414.

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Patients on hydroxyurea who achieve maximum tolerated dose (MTD), defined by a target level of mild myelosuppression, may have greater laboratory and clinical benefits than those maintained on a lower dose. MTD is currently determined by gradual dose escalation, a process that often takes six to twelve months. Using data from a previous cohort of pediatric patients escalated to MTD on hydroxyurea, we have developed an equation incorporating baseline serum creatinine, body mass index, and absolute reticulocyte count to predict individualized MTD for patients initiating therapy. The NDEPTH (Nove

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33

Poel, Robert, Amith J. Kamath, Jonas Willmann, et al. "Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring." Cancers 15, no. 17 (2023): 4226. http://dx.doi.org/10.3390/cancers15174226.

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External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 12

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34

Yun, Seong Su, Sundo Lee, Nam Ju Lee, et al. "Prediction of Extravasation before PET/CT Scan." Korean Journal of Nuclear Medicine Technology 28, no. 2 (2024): 97–104. https://doi.org/10.12972/kjnmt.20240013.

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Purpose: In this study, we investigated whether extravasation could be predicted by measuring the dose rate once at the injection site before PET/CT scanning. Materials and Methods: The dose rate at the injection site was measured for 412 patients, and the severity of extravasation was evaluated in the F-18 FDG PET/CT images. Then, extravasation activity and %ID were calculated with VOI analysis. Results: The dose rate at the injection site and %ID showed significant differences in the mild and moderate extravasation groups. Dose rates were 15.9±5.8 mR/hr, 18.7±9.9 mR/hr and 57.1±5.8 mR/hr in

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35

Schwartz, Michael, Katharina Sixel, Charlene Young, et al. "Prediction of Obliteration of Arteriovenous Malformations after Radiosurgery: the Obliteration Prediction Index." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 24, no. 2 (1997): 106–9. http://dx.doi.org/10.1017/s0317167100021417.

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ABSTRACT:Objective:To describe the response to single dose photon stereotactic radiosurgery of arteriovenous malformations (AVMs) so that the probability of success or failure of treatment may be predicted for the individual patient.Method:The obliteration prediction index (OPI) was calculated for AVMs by dividing the marginal dose of radiation in Gray (Gy) by the lesion diameter in centimetres in cohorts of 42 patients treated with the modified linear accelerator at Toronto-Sunnybrook Regional Cancer Centre and 394 patients treated with the gamma unit at the Royal Hallamshire Hospital, Sheffi

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36

OSCAR, THOMAS P. "Development and Validation of a Predictive Microbiology Model for Survival and Growth of Salmonella on Chicken Stored at 4 to 12°C†." Journal of Food Protection 74, no. 2 (2011): 279–84. http://dx.doi.org/10.4315/0362-028x.jfp-10-314.

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Salmonella spp. are a leading cause of foodborne illness. Mathematical models that predict Salmonella survival and growth on food from a low initial dose, in response to storage and handling conditions, are valuable tools for helping assess and manage this public health risk. The objective of this study was to develop and to validate the first predictive microbiology model for survival and growth of a low initial dose of Salmonella on chicken during refrigerated storage. Chicken skin was inoculated with a low initial dose (0.9 log) of a multiple antibiotic-resistant strain of Salmonella Typhim

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37

Tomek, Aleš, Tereza Šrámková, Vojtěch Kaplan, et al. "Pharmacogenetic algorithm for predicting daily dose of warfarin in Caucasian patients of Czech origin." Drug Metabolism and Drug Interactions 36, no. 2 (2020): 123–28. http://dx.doi.org/10.1515/dmpt-2020-0171.

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Abstract Objectives Warfarin use is limited by a low therapeutic index and significant interindividual variability of the daily dose. The most important factor predicting daily warfarin dose is individual genotype, polymorphisms of genes CYP2C9 (warfarin metabolism) and VKORC1 (sensitivity for warfarin). Algorithms using clinical and genetic variables could predict the daily dose before the initiation of therapy. The aim of this study was to develop and validate an algorithm for the prediction of warfarin daily dose in Czech patients. Methods Detailed clinical data of patients with known and s

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38

Eriksson, Niclas, and Mia Wadelius. "Prediction of warfarin dose: why, when and how?" Pharmacogenomics 13, no. 4 (2012): 429–40. http://dx.doi.org/10.2217/pgs.11.184.

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39

Caldwell, M. D., R. L. Berg, K. Q. Zhang, et al. "Evaluation of Genetic Factors for Warfarin Dose Prediction." Clinical Medicine & Research 5, no. 1 (2007): 8–16. http://dx.doi.org/10.3121/cmr.2007.724.

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40

Andreev, S. G., and Y. A. Eidelman. "Dose-response prediction for radiation-induced chromosomal instability." Radiation Protection Dosimetry 143, no. 2-4 (2010): 270–73. http://dx.doi.org/10.1093/rpd/ncq509.

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41

Sandgren, David J., Charles A. Salter, Ira H. Levine, James A. Ross, Patricia K. Lillis-Hearne, and William F. Blakely. "Biodosimetry Assessment Tool (BAT) Software—Dose Prediction Algorithms." Health Physics 99 (November 2010): S171—S183. http://dx.doi.org/10.1097/hp.0b013e3181f0fe6c.

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42

Biss, Tina, Anna-Karin Hamberg, Peter Avery, Mia Wadelius, and Farhad Kamali. "Warfarin dose prediction in children using pharmacogenetics information." British Journal of Haematology 159, no. 1 (2012): 106–9. http://dx.doi.org/10.1111/j.1365-2141.2012.09230.x.

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43

van Elmpt, W. J. C., S. M. J. J. G. Nijsten, B. J. Mijnheer, and A. W. H. Minken. "Experimental verification of a portal dose prediction model." Medical Physics 32, no. 9 (2005): 2805–18. http://dx.doi.org/10.1118/1.1987988.

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44

Abushawish, Mojahed, Arthur V. Galapon, Joaquín L. Herraiz, José M. Udías, and Paula Ibáñez. "1381: Deep learning-based dose prediction for INTRABEAM." Radiotherapy and Oncology 194 (May 2024): S4472—S4474. http://dx.doi.org/10.1016/s0167-8140(24)01789-4.

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Fiorino, C., G. Rizzo, S. Broggi, et al. "507 speaker IMAGE-BASED DOSE-VOLUME EFFECTS PREDICTION." Radiotherapy and Oncology 99 (May 2011): S205—S206. http://dx.doi.org/10.1016/s0167-8140(11)70629-6.

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46

Irannejad, Maziar, Iraj Abedi, Vida Darbaghi Lonbani, and Maryam Hassanvand. "Deep‐neural network approaches for predicting 3D dose distribution in intensity‐modulated radiotherapy of the brain tumors." Journal of Applied Clinical Medical Physics, November 7, 2023. http://dx.doi.org/10.1002/acm2.14197.

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AbstractPurposeThe aim of this study is to reduce treatment planning time by predicting the intensity‐modulated radiotherapy 3D dose distribution using deep learning for brain cancer patients. “For this purpose, two different approaches in dose prediction, i.e., first only planning target volume (PTV) and second PTV with organs at risk (OARs) as input of the U‐net model, are employed and their results are compared.”Methods and MaterialsThe data of 99 patients with glioma tumors referred for IMRT treatment were used so that the images of 90 patients were regarded as training datasets and the ot

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47

Sun, Zihan, Jiazhou Wang, Weigang Hu, et al. "An isodose‐constrained automatic treatment planning strategy using a multicriteria predicted dose rating." Medical Physics, April 4, 2025. https://doi.org/10.1002/mp.17795.

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AbstractBackgroundPrevious knowledge‐based planning studies have demonstrated the feasibility of predicting three‐dimensional photon dose distributions and subsequently generating treatment plans. The steepness of dose fall‐off represents a critical metric for clinical plan evaluation; however, dose fall‐off similarity is frequently overlooked in dose prediction tasks. Our study introduces a novel automatic treatment planning methodology that specifically focuses on dose fall‐off reconstruction for nasopharyngeal carcinoma (NPC).PurposeOur study aims to establish an innovative methodology for

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48

Lee, Heemoon, Hyun Joo Kim, Hyoung Woo Chang, Dong Jung Kim, Jonghoon Mo, and Ji-Eon Kim. "Development of a system to support warfarin dose decisions using deep neural networks." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-94305-2.

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AbstractThe first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1–4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospi

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49

Squires, Steven, Elaine F. Harkness, Alistair Mackenzie, D. Gareth Evans, Sacha J. Howell, and Susan M. Astley. "Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality." Biomedical Physics & Engineering Express, May 3, 2024. http://dx.doi.org/10.1088/2057-1976/ad470b.

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Abstract Purpose: To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions.&#xD;Methods: Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose an

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50

Huusari, Riikka, Tianduanyi Wang, Sandor Szedmak, Tero Aittokallio, and Juho Rousu. "Predicting drug combination response surfaces." npj Drug Discovery 2, no. 1 (2025). https://doi.org/10.1038/s44386-024-00004-z.

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Abstract Prediction of drug combination responses is a research question of growing importance for cancer and other complex diseases. Current machine learning approaches generally consider predicting either drug combination synergy summaries or single combination dose-response values, which fail to appropriately model the continuous nature of the underlying dose-response combination surface and can lead to inconsistencies when a synergy score or a dose-response matrix is reconstructed from separate predictions. We propose a novel prediction method, comboKR, that directly predicts the continuou

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