James R. Green  PhD (Queen's), P.Eng., SMIEEE

Associate Professor

James Green - Research


Lab News

  • Fall 2018:Congratulations to graduate students Mohsen Sheikh Hassani, Amente Bekele, Roger Selzler, and Francois Charih for successfully defending their theses this term. Francois and Roger have been nominated for Senate Medals for their thesis research! Congratulations to Yasmina Souley Dosso and Kevin Dick for being awarded Queen Elizabeth II scholarships in 2018-2019!
  • Summer 2018:Yasmina Souley Dosso has won the "Women in Engineering Best Paper" award at MeMeA2018 for her paper entitled "Eulerian Magnification of Multi-Modal RGB-D Video for Heart Rate Estimation"! Congratulations Yasmina!
  • Spring 2018: Jim Green has been awarded a Faculty Graduate Mentoring Award. Shermeen Nizami and Jim Green have filed a US Patent (with IBM) on the subject of "Detecting Quality of Physiologic Data Using Contact Pressure Data for Alarm Generation". Congratulations to Calvin Jary and his project partner Katie Noah for winning 3rd place in the 2018 Data Science Poster Competition here at Carleton! Congratulations to Shermeen Nizami, Yasmin Souley Dosso, Joe Samuel, Naman Sethi, and Amente Bekele for winning First Place in the Life Sciences Research Day poster competition for their work entitled “Patient Monitoring in the NICU using Pressure Sensitive Mats and Video Analysis”.
  • Winter 2018: Jim Green has been awarded both a Research Achievement Award and a Teaching Achievement award for developing biomedical sensors and analytics for monitoring neonates during emergency transport to CHEO. Jim Green and Adrian Chan have been awarded a CIMVHR 2-year grant to develop unobtrusive real-time estimators of subject stress during PTSD treatments in a VR facility at the Ottawa Rehab Centre. PhD student Kevin Dick has been awarded the 2017-18 Koningstein Scholarship. Congrats Kevin! Mohamed Hozayen has been selected to represent Carleton University at the National Undergraduate Research Conference in Oklahoma this spring. Congrats Mohamed!
  • Fall 2017: Green Lab poster entitled "Patient Monitoring in the NICU using Pressure Sensitive Mats and Video Analysis" tied for Best Poster Award, among 75 exhibits at CASCON2017! PhD student Kevin Dick has been awarded an Queen Elizabeth II Graduate Scholarship in Science and Technology! Congrats Kevin! Jim Green and Francois Charih have been awarded a $50K OCE VIP-I/NSERC Engage grant with Clearwater Clinical to investigate the application of machine learning to the interpretation of audiograms.
  • Spring 2017: Several members of the lab participated in HackingHealth Ottawa 2017 Hackathon (see projects ideas). Amente's team won the "Showcase Showdown" and "Best Solution for Healthcare Collaboration Award", while Kevin, Yasmina, and Francois' team won the "IBM IoT Silver Prize" and the "CHEO Pilot Opportunity Prize". Well done! Shermeen Nizami has won the "Women in Engineering Best Paper" award at MeMeA2017 for her paper entitled "Comparing time and frequency domain estimation of neonatal respiratory rate using pressure-sensitive mats"! Congratulations Shermeen!
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CUBIC: Carleton University Biomedical Informatics Co-laboratory

At the Carleton University Biomedical Informatics Co-laboratory (CUBIC), we apply machine learning and data science to solve problems in biomedical informatics. Current projects requiring additional students include an exploration of the use of RGB-D video and pressure-sensitive mats for real-time patient monitoring in the NICU at CHEO (data collection ongoing), automated analysis of audiograms for telemedicine applications in under-served communities (with an industry partner), development of a wireless system to monitor neonates during emergency ground and air transport to the NICU at CHEO (collaboration already in place), and development of novel machine learning methods for analyzing protein structure, function, interaction, and chemical modification. Interested students should have strong software and communication skills proven through academic performance and/or industry experience. Hands-on experience with deep learning, artificial intelligence, web programming, and statistics is highly valued.

Species-specific Prediction of microRNA.
microRNA are short RNA molecules that play an important role in post-transcriptional gene regulation. Our collaborators are continuously sequencing new species and wish to identify novel microRNA within these new genomes. However, most widely-used microRNA prediction tools are only effective on human data. We have developed SMIRP, a framework for the creation of species-specific predictors of microRNA from genomic sequence. We have achieved up to 500% increases in sensitivity at precisions of up to 90% when compared with existing methods. SMIRP has been applied to study numerous genomes including turtles, slime moulds, and a snail. We are now developing methods to leverage transcriptomic RNA-Seq data as this continues to becomes more accessible to experimental researchers.

Artifact detection in real-time patient monitoring.
In a second application, we are part of a research initiative to conduct real-time patient monitoring in intensive care settings. Currently, enormous quantities of data are measured continuously from patients such as blood oxygen saturation, ECG, respiration rate, and blood pressure. However, typically only periodic readings are recorded by a health care worker at wide intervals since most hospitals lack the infrastructure required to store and analyze this data in real-time. Research has shown that analyzing patient data such as heart rate on a continuous basis can detect the onset of serious illness such as sepsis hours before symptoms become evident. While the infrastructure and algorithms are currently being developed and deployed in hospitals around the world, artifacts in the data continue to be problematic, leading to loss of data and potential mis-diagnosis. With our collaborators, we are developing a framework for real-time artifact detection that would enable the automated selection of optimal detection algorithms tailored for the specific clinical setting of each patient.

Protein structure prediction.
Much like the shape of a tool suggests its intended purpose, knowledge of a protein's structure can provide substantial insight into its function. Therefore, computational prediction of protein structure based solely on protein sequence data is a challenge of fundamental importance to biomedical research. An effective solution promises significant advances in computational drug discovery and an increased understanding of complex disease processes such as cancer. We have recently developed a novel approach to determining the 1D secondary structure of proteins from protein sequence data which makes use of Parallel Cascade Identification (PCI), a powerful method of nonlinear system identification. We are currently working towards extending this method to the prediction of full 3D tertiary structure prediction.

Post-translational modification.
While progress continues to be made on the prediction of structure from sequence, knowledge of a protein's structure may not be sufficient to discern its function. For example, most proteins undergo some form of post-translational modification (PTM) following initial synthesis which may have a profound impact on protein function. Our lab is therefore working to develop intelligent predictors of important PTM's such as sumoylation and phosphorylation. Iterative prediction of protein function and structure is a long term goal as well.

Information-driven mass spectrometry.
Tandem mass spectrometry (MS/MS) is an analytical technique for identifying proteins from an unknown mixture, and has become a cornerstone of modern proteomics. Currently, protein identification is relegated to take place offline, after the data collection phase, when it is too late to take corrective action in the case of an ambiguous identification. By sufficiently accelerating the data analysis, it becomes possible to close the feedback loop and achieve true information-driven data collection. Our research program aims to leverage advanced parallel processing approaches and architectures (GPU, multicore) to accelerate protein identification to enable real-time control of a MS/MS device. By simultaneously collecting and analyzing data on a tandem mass spectrometer, new forms of data analysis become possible including more effective identification of low abundance biomarkers.

Bioinformatics web services.
Please click here for a list of web services developed by our lab.

Areas of Research Interest

My research focus has been in the following areas:
  • Real-time patient monitoring, using pressure-sensitive mats, multi-modal video, and other sensors.
  • Machine intelligence, pattern classification, data mining
  • Bioinformatics, proteomics, and prediction of protein structure, function, interaction, and post-translational modification
  • Development of novel assistive technology and devices
Current projects include:
  • Neonatal patient monitoring using pressure sensitive mats (collaboration with IBM-CAS)
  • Classification of audiograms for tele-medicine applications (collaboration with Clearwater Clinical)
  • Real-time monitoring for vibration/acceleration, sound, air pressure, and temperature during emergency patient transport to the NICU (collaboration with the CHEO patient transport team)
  • Species-specific prediction of microRNA from genomic sequence or transcriptomics data
  • Prediction of protein-protein interactions from sequence
  • Development of various novel assistive devices for persons who are disabled and the elderly
  • Hardware acceleration of bioinformatics algorithms using GPGPU, including real-time mass spectrometry
  • Identification of post-translational modifications in proteins, including methylation, sumoylation, glycosylation, and hydroxylation.