Sinisa Colic, University of Toronto

Research

Read how they describe their research on their University of Toronto profile.

Applied Machine Learning

Latest papers

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers
Journal of Affective Disorders · 2024
Electrographic and pharmacological characterization of a progressive epilepsy phenotype in female MeCP2-deficient mice
Epilepsy Research · 2018
Prediction of antiepileptic drug treatment outcomes using machine learning
Journal of Neural Engineering · 2016 · first author

All 6 publications

6 publications.

01

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers

Ravan M, Noroozi A, Sanchez MM, Borden L, Alam N, Flor-Henry P, Colic S, Khodayari-Rostamabad A, Minuzzi L, Hasey G

Journal of Affective Disorders202425 citations

DOI: 10.1016/j.jad.2023.11.017

02

Electrographic and pharmacological characterization of a progressive epilepsy phenotype in female MeCP2-deficient mice

Robert G. Wither, Sinisa Colic, Berj L. Bardakjian, O. Carter Snead, Liang Zhang, James H. Eubanks

Epilepsy Research201823 citations

DOI: 10.1016/j.eplepsyres.2018.01.015 PMID: 29414525

03

Prediction of antiepileptic drug treatment outcomes using machine learning

Sinisa Colic, Robert G. Wither, Min Lang, Liang Zhang, James H. Eubanks, Berj L. Bardakjian

Journal of Neural Engineering201633 citationsFirst authorOpen Access

DOI: 10.1088/1741-2560/14/1/016002 PMID: 27900948

04

Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome

Sinisa Colic, Robert G. Wither, Liang Zhang, James H. Eubanks, Berj L. Bardakjian

Neural Networks201331 citationsFirst author

DOI: 10.1016/j.neunet.2013.05.002 PMID: 23727441

05

RESPONSIVE NEUROMODULATORS BASED ON ARTIFICIAL NEURAL NETWORKS USED TO CONTROL SEIZURE-LIKE EVENTS IN A COMPUTATIONAL MODEL OF EPILEPSY

Sinisa Colic, Osbert C. Zalay, Berj L. Bardakjian

International Journal of Neural Systems201124 citationsFirst author

DOI: 10.1142/s0129065711002894 PMID: 21956930

06

EEG analysis for estimation of duration and inter-event intervals of seizure-like events recorded in vivo from mice

S. Colic, Robert G. Wither, James H. Eubanks, Liang Zhang, Berj L. Bardakjian

20114 citationsFirst author

DOI: 10.1109/iembs.2011.6090710 PMID: 22254866

Frequent collaborators

Psychiatry & Behavioural Neurosciences
Department of Mechanical & Industrial Engineering
Edward S. Rogers Sr. Department of Electrical and Computer Engineering
Department of Surgery
Department of Medicine

Co-authors at University of Toronto, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.

Berj Bardakjian

Edward S. Rogers Sr. Department of Electrical and Computer Engineering

5 shared papers, latest 2018

Liang Zhang

Department of Medicine

4 shared papers, latest 2018

James Eubanks

Department of Surgery

4 shared papers, latest 2018

Luciano Minuzzi

Psychiatry & Behavioural Neurosciences

1 shared papers, latest 2024

Gary Marcel Hasey

Psychiatry & Behavioural Neurosciences

1 shared papers, latest 2024

Write to this professor

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Subject: Prospective PhD student: Applied Machine Learning Dear Professor Colic, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers" (2024), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]