Publications

Conference & Publications

  • Conference & Publications

ESMO 2023: What to Take Away. Several breakthroughs in advanced cancer detection.

ASCO 2023 : what you need to remember

American Society of Clinical Oncology (ASCO) 2023 annual meeting

Clinical Trials on Alzheimer's Disease Conference (CTAD) 2022

American Society of Clinical Oncology (ASCO) 2022 annual meeting

Alzheimer’s Association International Conference (AAIC) 2022

Clinical Trials on Alzheimer's Disease Conference (CTAD) 2020

American Society for Experimental Neurotherapeutics - ASENT 2019 Annual Meeting, 17 July, Maryland, USA

Clinical Trials on Alzheimer's Disease conference (CTAD) 2019, San Diego, USA

American Association for Cancer Research Annual Meeting (AACR) 2019, Atlanta, USA

Options X for the Control of Influenza, September, 2019, Singapore

Alzheimer's Association International Conference (AAIC), 2019, Los Angeles, USA

European Association for the Study of Diabetes (EASD), 2019, Barcelona, Spain

Research in Computational Molecular Biology, Computational Cancer Biology (RECOMB-CCB) 22nd 2018, Paris, France

European Society of Surgical Oncology (ESSO) 38th 2018, Budapest, Hungary

Alzheimer's Association International Conference (AAIC) 2018, Chicago, USA

Clinical Trials on Alzheimer's Disease conference (CTAD) 2017, Boston, Massachusetts

API (Asociacion Panamericana de Infectologia) - XVIII Congresso Panamericano de Infectologia, 2017, Panama

Anavex Life Sciences Investor Presentation, 12 Oct 2017

TAT 2017: 15th International Congress On Targeted Anticancer Therapies, 6-8 Mar 2017, Paris, France

ASTMH 2016: 65th Annual Meeting Of The American Society Of Tropical Medicine And Hygiene, 13-17 Nov 2016, Atlanta, USA

Institut Pasteur International Network Scientific Symposium, 29 Nov-2 Dec 2016, Paris, France

WIN 2014: Breakthrough Biomarker Investigations And Combined Therapeutic Approaches For Precision Cancer Medicine, 23-24 June 2014, Paris, France

EACR-23: 23rd Biennial Congress Of The European Association For Cancer Research. From Basic Research To Personalised Cancer Treatment, 5-8 July 2014, Munich, Germany

Australian Pain Society (APS) 2013, Canberra, Australia

48th Annual Meeting Of The EASL, April 2013, Amsterdam, The Netherlands

The Liver Meeting 2012, November 2012, Boston, Massachussets, USA

47th Annual Meeting Of The EASL, April 2012, Barcelona , Spain

19th International Symposium On Hepatitis C Virus And Related Viruses, October 2012, Venice, Italy

18th International Symposium On Hepatitis C Virus And Related Viruses, September 2011, Seattle, Washington USA

Digestive Disease Week, May 2010, New Orleans, LA

45th Annual Meeting Of The EASL, April 2010, Vienna, Austria

Solvay Pharmaceuticals Conferences 2008

Ariana Citations-Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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).

DOI: 10.4236/abb.2018.99028

Ariana Citations-Artificial intelligence in early drug discovery enabling precision medicine

Boniolo, F. et al. Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin. Drug Discov. 16, 991–1007 (2021).

DOI: 10.4236/abb.2018.99028

Ariana Citations-An ensemble learning approach for brain cancer detection exploiting radiomic features

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).

DOI: 10.4236/abb.2018.99028

Ariana Citations-Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information

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).

DOI: 10.4236/abb.2018.99028

Ariana Citations-Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018

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).

DOI: 10.4236/abb.2018.99028

Observational Studies-Behavior and interaction imaging at 9 months of age predict autism/intellectual disability in high-risk infants with West syndrome

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).

DOI: 10.4236/abb.2018.99028

Observational Studies-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

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).

DOI: 10.4236/abb.2018.99028

Genomic Patient Profiling-Utility of Serial Transcriptomic Analyses to Characterize the Resistome and to Refine Treatment Selection for Metastatic Colon Cancer: Case Report

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).

DOI: 10.4236/abb.2018.99028

Genomic Patient Profiling-Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial

Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019).

DOI: 10.4236/abb.2018.99028

Genomic Patient Profiling-An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients

Bund, C. et al. An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients. Metabolomics 15, 1–11 (2019).

DOI: 10.4236/abb.2018.99028

Pharmacokinetics

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).

DOI: 10.4236/abb.2018.99028

Blood Biomarkers- Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease

Hampel, H. et al. Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer’s disease. 21, 177–191 (2019).

DOI: 10.4236/abb.2018.99028

Blood Biomarkers-A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

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).

DOI: 10.4236/abb.2018.99028

Real World Evidence Using KEM- Mortality among Hospitalized Dengue Patients with Comorbidities in Mexico, Brazil, and Colombia

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).

DOI: 10.4236/abb.2018.99028

Blood Biomarkers- The Thomsen-Friedenreich Antigen: A Highly Sensitive and Specific Predictor of Microsatellite Instability in Gastric Cancer

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).

DOI: 10.4236/abb.2018.99028

Case Study – Ariana Pharma, uniquely positioned to identify strong signal in complex datasets.

Here is a striking example of how we found key information in a very small population and successfully validated it on a much larger scale. By analyzing a few cases in selected small cities in Brazil, we unearthed crucial associations between diagnoses and operations in Brazil as a whole.

DOI: 10.4236/abb.2018.99028

Real World Evidence Using KEM-Real-World Evidence of Dengue Burden on Hospitals in Mexico: Insights From the Automated Subsystem of Hospital Discharges (Saeh) Database

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).

DOI: 10.4236/abb.2018.99028

Real World Evidence Using KEM-Comorbidities increase in-hospital mortality in dengue patients in Brazil

Werneck, G. L. et al. Comorbidities increase in-hospital mortality in dengue patients in Brazil. Mem. Inst. Oswaldo Cruz 113, 1–5 (2018).

DOI: 10.4236/abb.2018.99028

Patient Stratification Using KEM- 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

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).

DOI: 10.4236/abb.2018.99028

Patient Stratification Using KEM-Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables

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).

DOI: 10.4236/abb.2018.99028

Systems Biology, Drug Repositioning-The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603–607 (2012).

DOI: 10.4236/abb.2018.99028

Systems Biology, Drug Repositioning-Systematic identification of genomic markers of drug sensitivity in cancer cells

Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nat. 2012 4837391 483, 570–575 (2012).

DOI: 10.4236/abb.2018.99028

Systems Biology, Drug Repositioning-Navigating the kinome

Metz, J. T. et al. Navigating the kinome. Nat. Chem. Biol. 2011 74 7, 200–202 (2011).

DOI: 10.4236/abb.2018.99028

Systems Biology, Drug Repositioning-Predicting new molecular targets for known drugs

Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nat. 2009 4627270 462, 175–181 (2009).

Systems Biology, Drug Repositioning-A quantitative analysis of kinase inhibitor selectivity

Karaman, M. W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 2008 261 26, 127–132 (2008).

DOI: 10.4236/abb.2018.99028

Patient Stratification Using KEM- 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

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).

DOI: 10.4236/abb.2018.99028

Patient Stratification Using KEM-Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables

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).

DOI: 10.4236/abb.2018.99028

Applications of Rule-Based Methods to Data Mining of Polypharmacology Data Sets

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).

DOI: 10.4236/abb.2018.99028

Hypothesis Generation for Scientific Discovery. Examples from the Use of KEM®, a Rule-Based Method for Multi-Objective Analysis and Optimization

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).

DOI: 10.4236/abb.2018.99028

Aristotle’s Square Revisited to Frame Discovery Science

Afshar, M., Dartnell, C., Luzeaux, D., Sallantin, J. & Tognetti, Y. Aristotle’s square revisited to frame discovery science. J. Comput. 2, 54–66 (2007).

DOI: 10.4236/abb.2018.99028

Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables

“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.

DOI: 10.4236/abb.2018.99028

Hypothesis Generation for Scientific Discovery. Examples from the Use of KEM®, a Rule-Based Method for Multi-Objective Analysis and Optimization

“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.

DOI: 10.4236/abb.2018.99028