Collaboration Hub

Researcher: Karim Mekhail seeks skills from another member
Institution: University of Toronto
Website: https://www.mekhaillab.com
Project aims: DNA mobility can contribute to genome stability. Although initially believed to be random, this mobility can exhibit non-random motions consistent with gaining access to repair-conducive factors. The current goal is to predict the motion of DNA and the outcome of repair on a single-cell level.
Desired skills in collaborators: Advanced knowledge of time series prediction models.

Researcher: Dr. Michelle Greiver
Organization: POPLAR (Primary care Ontario Practice-based Learning and Research Network)
Contact: dfcm.utopian@utoronto.ca
Website: UTOPIAN Data Safe Haven
Project aims: Collaboration between seven networks across Ontario that extract and manage primary care EMR data. Data sets provided in secure environment. To request access to data, please see https://www.dfcm.utoronto.ca/getting-utopian-support
Research team currently has these skills: Data managers, epidemiologists, data analysts, primary care clinician-scientists, primary care researchers
Desired skills in potential collaborators: Data science. We welcome applications for use of primary care EMR data. (Linkage with ICES currently underway.)
Learn more and see project collaborators: https://www.poplarnetwork.ca

Contact: Dr. Fa-Hsuan Lin
Organization: Sunnybrook Health Sciences Centre
Website: https://linbrainlab.org
Funding available: NSERC, MITACS
Project title and aims: Encoding and decoding the human brains under complex naturalistic stimuli
We are interested in measuring neuronal responses under complex and naturalistic stimuli, such as movie clips and musical pieces. The goals include the modeling of brain responses with sensory inputs (brain coding) and estimating the behavioural responses and sensory inputs based on brain activity (brain decoding). We hypothesize that relationships between brain activity, sensory input, and behavioural responses implicate the mental states, which are different between age groups, lifestyles, and disorders. We aim at classifying and predicting their subjective feelings, behaviours, and treatment responses based on neuroimaging measurements.
Research team currently has these skills: Our lab is specialized in neuroimaging data acquisition and analysis. The modality we used includes magnetic resonance imaging (MRI) and electroencephalography (EEG). We are specialized in recording MRI and EEG with high spatiotemporal resolution and sensitivity on healthy adults, schizophrenic patients, and teenagers suffering from attention deficit disorders.
Desired skills in potential collaborators: We are enthusiastic in collaborating with experts in data analysis. We are interested in learning data modeling methods capable of generating robust results with our sample size (tens to hundreds of participants) and analytic strategies that support adaptive models with evolving data samples.
Other project collaborators: We are collaborating with international industrial partners and major medical imaging vendors to provide infrastructure (AI computations) and technical development (parts and information for imaging hardware) supports.

Contact: Andrew Semihradsky <andrew.sedmihradsky@utoronto.ca>
Telephone number: 289-527-5608
Website: https://www.maxsbigride.com/
Project Aims: I hope to create an intelligent online platform that captures information about completed, ongoing and recently funded Duchenne research from online databases, journals and various other sources. This platform will utilize the power of artificial intelligence to find synergies between existing research projects and suggest connections between researchers with the aim of promoting communication, collaboration and promising new directions for research.
Desired skills in collaborators: At the moment I am just interested in talking through this project to see if it is possible — I do not know enough about AI to determine its feasibility or potential.

Contact: Adam Kepecs (Tel: 647-812-6678)
Organization: IntelAGENT
Project aims: This project aims to use Machine Learning to parse a clinician's unstructured free-text patient encounter note in their Electronic Medical Record (EMR) system to identify billable OHIP services. This will alleviate administrative overhead and help physicians spend more time in clinical practice.
Desired skills: Expertise in Natural Language Processing models for unstructured clinical notes.
Other collaborators in this project: Prof. Frank Rudzicz

T-CAIREM is seeking an experienced medical illustrator/animator to communicate AI in medicine research findings through multimedia/interactive platforms.

Start Date: As soon as possible; duration will be approximately five months.
Estimated time commitment: six to seven hours per week.
Compensation: $60/hour
To Apply: Email T-CAIREM Centre Administrator, Zoryana Salo, zoryana.salo@utoronto.ca and include a link to your online portfolio.

Researcher: Yalini Senathirajah
Institution: University of Toronto
Project description: Interested in collaborations with those with a mature AI recommendation algorithm that could be integrated into the EHR (our research is on user-controlled platforms for EHR redesign)
Project aims: Composable approaches to EHR design may have benefits for human-computer interaction, cognitive support, efficiency, workflow, and other benefits. It also may make it easier to incorporate AI-based recommendations at the point-of-care. We are interested in possibly piloting this approach with those with appropriate tested algorithms seeking to incorporate them into the EHR.
Desired skills in collaborators: Those who have developed AI-based algorithms aimed at clinical care, at an appropriate stage of development/testing.

Researcher: Sophia Huang
Institution: University of Toronto
Lab: https://bozec-lab.com
Project title: Multimodal histopathology image analysis using machine learning for early cancer diagnosis
Project description: The first part of the project aims to create a machine-learning algorithm for early oral cancer diagnosis by combining different histological images used in clinical diagnosis. 
     The second project involves 3D image analysis of collagen structural properties obtained by atomic force microscope and creating a machine learning approach to link the collagen nanoscale biophysical property with the collagen-related disease.
Desired skills in collaborators: Expertise in convolutional neural network, supervised and unsupervised machine learning, 3D image analysis.