J. Geraci

发表

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Stephen C. Strother, Moyez Dharsee, Luciano Minuzzi, 2016, BMC Psychiatry.

Resting-State Cortico-Thalamic-Striatal Connectivity Predicts Response to Dorsomedial Prefrontal rTMS in Major Depressive Disorder

Joseph Geraci, Alastair Flint, Peter Giacobbe, 2013, Neuropsychopharmacology.

Anhedonia and Reward-Circuit Connectivity Distinguish Nonresponders from Responders to Dorsomedial Prefrontal Repetitive Transcranial Magnetic Stimulation in Major Depression

Sidney H. Kennedy, Joseph Geraci, Jonathan Downar, 2014, Biological Psychiatry.

Anhedonia and Reward -

Nathan Bakker, S. Kennedy, P. Giacobbe, 2018 .

Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression

Joseph Geraci, John Strauss, Pamela Wilansky, 2017, Evidence Based Journals.

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials.

W. Horan, J. Geraci, F. Butlen-Ducuing, 2022, Innovations in clinical neuroscience.

Using Machine Intelligence to Uncover Alzheimer’s Disease Progression Heterogeneity

R. Au, J. Geraci, Bessi Qorri, 2020, Exploration of Medicine.

EMT transcription factors snail and slug directly contribute to cisplatin resistance in ovarian cancer

Paulo A. S. Nuin, H. Feilotter, M. Dharsee, 2012, BMC Cancer.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

Anne-Christin Hauschild, Joseph Geraci, Jane A Foster, 2018, Journal of psychiatric research.

A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein–protein interaction analysis reveals novel genes and polymorphisms important for cancer research

Igor Jurisica, Joseph Geraci, I. Jurisica, 2009, International journal of cancer.

Small Patient Datasets Reveal Genetic Drivers of Non-Small Cell Lung Cancer Subtypes using a Novel Machine Learning Approach

L. Pani, J. Geraci, Bessi Qorri, 2021, medRxiv.

Quantum and classical machine learning for the classification of non-small-cell lung cancer patients

Siddhant Jain, Joseph Geraci, Jalal Ziauddin, 2020, SN Applied Sciences.

Algorithms for systematic identification of small subgraphs.

Igor Jurisica, Joseph Geraci, Geoffrey Liu, 2012, Methods in molecular biology.

Neurocognitive Predictors of Response in Treatment Resistant Depression to Subcallosal Cingulate Gyrus Deep Brain Stimulation

A. Lozano, H. Mayberg, S. Kennedy, 2017, Front. Hum. Neurosci..

Identification of rTMS responders vs. non-responders for MDD via a network theory approach on fMRIs

J. Geraci, 2013 .

Dorsomedial prefrontal rTMS for treatment-refractory major depression: safety, tolerability, efficacy, and predictors of clinical response

S. Kennedy, P. Giacobbe, M. McAndrews, 2012 .

Predictors of response to adjunctive osmotic-release methylphenidate or placebo in patients with major depressive disorder: effects of apathy/anhedonia and fatigue.

S. Kennedy, J. Geraci, A. Ravindran, 2014, Journal of clinical psychopharmacology.

Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation

L. Pani, J. Geraci, Bessi Qorri, 2023, Exploration of Medicine.

Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems

Moyez Dharsee, Joseph Geraci, Kenneth R. Evans, 2014, Bioinform..