Jullian, N., Tognetti, Y. & Afshar, M. Applications of Rule-Based Methods to Data Mining of Polypharmacology Data Sets. Data Min. Drug Discov. 57, 241–256 (2013).
Jullian, N., Jourdan, N. & Afshar, M. Hypothesis Generation for Scientific Discovery . Examples from the Use of KEM ® , a Rule-Based Method for Multi- Objective Analysis and Optimization. Towar. drugs Futur. key issues lead Find. lead Optim. (2008).
Afshar, M., Dartnell, C., Luzeaux, D., Sallantin, J. & Tognetti, Y. Aristotle’s square revisited to frame discovery science. J. Comput. 2, 54–66 (2007).
“Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables”, L Abtroun, P Bunouf, RM Gendreau and O Vitton, Clin. J. Pain, 32 (2016) 435–440.
“Hypothesis Generation for Scientific Discovery. Examples from the Use of KEM®, a Rule-Based Method for Multi-Objective Analysis and Optimization”, Nathalie Jullian, Nathalie Jourdan, Mohammad Afshar, Solvay Pharmaceuticals Conferences, 18 (2008), 75-80.
Dartnell, C., Martin, A., Hagège, H. & Sallantin, J. Human Discovery and Machine Learning. Int. J. Cogn. Informatics Nat. Intell. 2, 55–69 (2008).
Sallantin, J., Dartnell, C. & Afshar, M. A pragmatic logic of scientific discovery. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 4265 LNAI 231–242 (Springer Verlag, 2006).
Afshar, M., Lanoue, A. & Sallantin, J. Multiobjective/multicriteria optimization and decision support in drug discovery. in Comprehensive Medicinal Chemistry II vol. 4 767–774 (Elsevier, 2006).
Hampel, H. et al. A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer’s disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study. Alzheimer’s Dement. Transl. Res. Clin. Interv. 6, 1–15 (2020).
Abtroun, L., Bunouf, P., Gendreau, R. M. & Vitton, O. Is the efficacy of milnacipran in fibromyalgia predictable? A data-mining analysis of baseline and outcome variables. Clin. J. Pain 32, 435–440 (2016).
Macias, A. E. et al. Mortality among hospitalized dengue patients with comorbidities in Mexico, Brazil, and Colombia. Am. J. Trop. Med. Hyg. 105, 102–109 (2021).
Maciás, A. E. et al. Real-World Evidence of Dengue Burden on Hospitals in Mexico: Insights From the Automated Subsystem of Hospital Discharges (Saeh) Database. Rev. Investig. Clin. 71, 168–177 (2019).
Werneck, G. L. et al. Comorbidities increase in-hospital mortality in dengue patients in Brazil. Mem. Inst. Oswaldo Cruz 113, 1–5 (2018).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603–607 (2012).
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nat. 2012 4837391 483, 570–575 (2012).
Metz, J. T. et al. Navigating the kinome. Nat. Chem. Biol. 2011 74 7, 200–202 (2011).
Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nat. 2009 4627270 462, 175–181 (2009).
Karaman, M. W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 2008 261 26, 127–132 (2008).
Hampel, H. et al. Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer’s disease. 21, 177–191 (2019).
Shen, Q. et al. A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer. EBioMedicine 44, 322–333 (2019).
Mereiter, S. et al. The Thomsen-Friedenreich Antigen: A Highly Sensitive and Specific Predictor of Microsatellite Instability in Gastric Cancer. J. Clin. Med. 7, 256 (2018).
Dilly, S. J. et al. Clinical Pharmacokinetics of a Lipid-Based Formulation of Risperidone, VAL401: Analysis of a Single Dose in an Open-Label Trial of Late-Stage Cancer Patients. Eur. J. Drug Metab. Pharmacokinet. 44, 557–565 (2019).
Castro, M. P. et al. Utility of Serial Transcriptomic Analyses to Characterize the Resistome and to Refine Treatment Selection for Metastatic Colon Cancer: Case Report. Clin. Colorectal Cancer 20, 96–99 (2021).
Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019).
Bund, C. et al. An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients. Metabolomics 15, 1–11 (2019).
Ouss, L. et al. Behavior and interaction imaging at 9 months of age predict autism/intellectual disability in high-risk infants with West syndrome. Transl. Psychiatry 10, (2020).
Ouss, L. et al. Developmental trajectories of hand movements in typical infants and those at risk of developmental disorders: An observational study of kinematics during the first year of life. Front. Psychol. 9, 1–15 (2018).
Vatansever, S. et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Medicinal Research Reviews vol. 41 1427–1473 (2021).
Boniolo, F. et al. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin. Drug Discov. 16, 991–1007 (2021).
Brunese, L., Mercaldo, F., Reginelli, A. & Santone, A. An ensemble learning approach for brain cancer detection exploiting radiomic features. Comput. Methods Programs Biomed. 185, 105134 (2020).
Kim, C., Son, Y. & Youm, S. Chronic disease prediction using character-recurrent neural network in the presence of missing information. Appl. Sci. 9, 2170 (2019).
Vellas, B., Bain, L. J., Touchon, J. & Aisen, P. S. Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018. J. Prev. Alzheimer’s Dis. 6, 198–203 (2019).