A cloud‐based, multi‐modal, cognitive ophthalmic imaging platform for enhanced clinical trial design and personalized medicine in blinding eye disease
Age Related Macular Degeneration is the leading cause of irreversible blindness in Canada and the industrialized world, yet there are no treatments for the vast majority of patients. Led by Tracery Ophthalmics inc, and working with Translatum Medicus inc (TMi) and academic partners at the Robarts Research Institute, Western University, and the “High Risk Dry AMD Clinic” of St Michael’s Hospital, we will engage SOSCIP’s Cloud Analytics platform, including servers, software and human resources, to accelerate the search for new treatments.
Specifically, Tracery has developed a novel functional imaging method, “AMD Imaging” (AMDI) that has already generated unprecedented pictures of the retina (the film of the eye) that include both known and unknown “flavours” of disease (the phenotype). These complex images will be compared against an individual’s genetic makeup (their genotype) and their concurrent illnesses, medications, and lifestyle history (their epigenetics). Further, Tracery’s imaging will help identify particular patients that will benefit from TMi’s drug development program, and ultimately help doctors choose which treatment will work best. Over the course of two years, we will involve increasing numbers of medical experts and their patients to generate and amass AMDI images, evaluating them over time and against other modalities.
Ultimately, through the “I3” program, we will work with IBM to train Watson and the Medical Sieve to recognize and co‐analyse complex disease patterns in the context of the ever‐expanding scientific literature. In short, we will leverage cloud‐based computing, to integrate image‐based and structured data, genomics and large data analytic to unite global users. We anticipate that this approach will significantly accelerate drug development, providing personalized treatment for the right patient at the right time.
A dynamic and scalable data cleaning system for Watson analytics
Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. It is estimated that poor data quality cost organizations between 15% and 25% of their operating budget. Existing data cleaning solutions focus on identifying inconsistencies that do not conform to prescribed data formats assuming the data remains relatively static. As modern applications move towards more dynamic search analytics and visualization, new data quality solutions that support dynamic data cleaning are needed. An increasing number of data analysis tools, such as Watson Analytics, provide flexible data browsing and querying abilities. In order to ensure reliable, trusted and relevant data analysis, dynamic data cleaning solutions are required. In particular, current data quality tools fail to adapt to: (1) fast changing data and data quality rules (for example as new datasets are integrated); (2) new data governance rules that may be imposed for a particular industry; and (3) utilize industry specific terminology and concepts that can refine data quality recommendations for greater accuracy and relevance. In this project, we will develop a system for dynamic data cleaning that adapts to changing data and rules, and considers industry specific models for improved data quality.
Active learning for automatic generation of narratives from numeric financial and supply chain data
Large enterprises compile and analyze large amounts of data on a daily basis. Typically, the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand, automating some of the manual processing requires more involved AI techniques. In our project, we aim to solve one of these harder to automate tasks. In fact, analyzing textual data using NLP is one of the standardized methods of data processing in modern software tools. However, vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives. Since generation of text is a domain dependent and non-trivial task, automated generation of narratives requires novel research to be useful to an enterprise environment. In this project, we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in executive level companies. Upon successful completion of this project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher level data fusion skills.
Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality
Watershed modeling is widely used to better understand processes and help inform planning and watershed management decisions. Examples include identifying impacts associated with land use change; investigating outcomes of infrastructure development, predicting effects of climate change. The proposed project will see the evolution of a desktop based watershed modeling and decision support system to a web based tool that will allow greater access by decision makers and stakeholders. By this means we will advance the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.
The proposed software evolution will take advantage of high performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. The result is expected to be a dramatic reduction in simulation run times. Reduced run times will facilitate the use of automatic calibration routines used to conduct model setup, reducing costs. It will also enable rapid response if the simulation were to be re-run by a request through the web-based user interface. The designed web-based tool will be used by decision and policy makers in the watersheds that contribute to Lake Erie to understand the sources of pollution especially phosphorus which is a major contributor to Lake Erie eutrophication problems and develop policies in supporting a wide variety of watershed planning and ultimately help achieve the Federal and Ontario government commitments to reduce 40% phosphorus entering Lake Erie by 2025.
Advancing video categorization
Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble is collaborating with Dr. Vida Movahedi from Seneca’s School of Information and Communication Technology to develop a machine-learning algorithm that will automatically output highly probable categories for videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This video identification research project will be Vubble’s first step in understanding how to automate the identification of accurate video. The need for automation of videos curation is prevalent, as video is quickly becoming the world’s dominant form of media consumption, particularly for digital native younger audiences. Furthermore, the results of the applied research will aid Vubble in moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake news video created from archival footage.
An economics-aware autonomic management system for big data applications
Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.
The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.
Big cardiac data
We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.
Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.
Big data analytics for the maritime internet of things (IoT)
The Internet of Things (IoT) is an emerging phenomenon that enables ordinary devices to generate sensor data and interact with one another to improve daily life. The maritime world has not escaped to the influence of the IoT revolution. We are in the midst of a technological wave in which vessels are not the only ones carrying sensors (GPS or radar) anymore, but other maritime entities such as cranes, crates, boats, pickup trucks, etc. are being equipped with the same capabilities. This trend constitutes the backbone of the so-called Maritime Internet of Things (mIoT).
This project is about exploiting the tide of sensor data emitted by a myriad of maritime entities in order to improve both internal and collaborative processes of mIoT-related organizations; for instance, think of a Port Authority adjusting its berthing and unloading schedule upon receiving notice that a vessel has been delayed by harsh weather conditions. The challenge addressed by this research project is the generation of actionable intelligence for Decision Support using Big Data analytics. Actionable intelligence includes anomalies, alerts, threats, potential response generation, process refinement and other types of knowledge that improve the efficiency of a maritime-related organization and/or the manner in which it interacts with other similar organizations.
Cloud‐based data analytic platform for real‐world evidence generation
Randomized controlled trials (RCTs) are considered the gold standard for supportive clinical evidence, however RCTs rarely describe the effectiveness of an intervention in real life practice. As such, regulators, payers, and health‐care providers are turning towards real world data (RWD) to understand how well an intervention performs in clinical practice.
The best source of RWD is source data – that is, data that are collected at the interface of the patient and the health care system, as well patient monitoring and self‐reported data captured directly within the patients’ home environment. Unfortunately, these data are stored in different and distinct systems that are not well integrated and in formats that do not allow for rapid analytic capabilities. The University of Waterloo in partnership with Roche Canada, are therefore proposing to develop the “CARE” (Clinical Analytics for Real‐World Evidence) platform.
The “CARE” platform will be a holistic cloud‐based data analytic solution featuring a large central repository for consolidating data obtained from disparate data systems (including data from electronic medical records, lab data, physician transcriptions, and patient monitoring devices and self‐reported surveys). The “CARE” platform will serve as a central hub for researchers that includes integrated and sophisticated data analytics, access control, security and study management tools in order to curate data for research and clinical purposes. This project begins the initial steps to address relevant research objectives in lung cancer by providing the tool and infrastructure required to process and analyze the enormous amount of scattered oncology data within an institution and across multiple institutions. Ultimately, this work will make it possible to mine currently siloed and/or unstructured data across the system and produce data‐driven insights in order to deliver the right care to the right patient at the right time through scientific innovation and research excellence.
Computational support for big data analytics, information extraction and visualization
The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:
IBM Platform:
- Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
- Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
- Design algorithms to determine which are suitable in such an environment.
- Identify commercialization venues in application domains.
- Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts
Continuous vital sign monitoring using intelligent bed sheet
Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.
Detailed computational fluid dynamics modeling of UV-AOPs photoreactors for micropollutants oxidation in water and wastewater
Micropollutants such as bisphenol-A and N-nitrosodimethylamine pose a significant threat to aquatic life, animals, and humans beings due to their persistent and potentially carcinogenic nature. While most conventional water treatment methods cannot remove these contaminants, ultraviolet-driven (UV) advanced oxidation processes (AOPs) are effective in degrading micropollutants. As UV-AOPs require electrical energy to enable the treatment, energy costs present a barrier to the widespread adoption of this technology. In this project, we focus on the optimization of UV-AOPs-based reactors to enhance their degradation performance while reducing their energy consumption. In this respect, we will develop a detailed numerical model that integrates hydraulics, optics and chemistry to investigate UV-AOP photoreactors in a comprehensive manner.
The resulting information will then be utilized to design the next-generation of UV-AOP photoreactors commercialized by Trojan Technologies. The design space will be explored by high-performance computer simulations of full-scale photoreactors rather than simplified or scaled-down models. This will be accomplished by leveraging opensource software, artificial-intelligence optimization techniques and the second-to-none parallel-computing capabilities offered by Blue Gene/Q. Once the optimization of UVAOPs-based reactors is complete, the advanced modeling results generated using Blue Gene/Q will be utilized in the development of a simplified model for sizing purposes. This will be accomplished through combined use of metamodeling techniques and cloud computing. In brief, the concept is to simplify the detailed model developed earlier so that it can be simulated using hand-held mobile devices, which will allow the company’s sales personnel to market the optimized reactors. Consequently, it will allow the company to increase its competitiveness on global scale as well as to increase the rate of adoption of advanced water treatment technologies by water utilities and end-users.
Developing real-time hyper-resolution simulation capability for the HydroGeoSphere (HGS) integrated groundwater – surface water modelling platform
Climate change will greatly impact the availability and quality of Earth’s water resources over the next century. The expected increase in mean temperature will have a severe impact on the water cycle, not only through changing precipitation patterns and amounts, but also through an increase in the severity and frequency of extreme events. Already, changing rainfall patterns and shifting temperatures are increasing the complexity of water management in the Grand River watershed and affecting the programs and operations of the Grand River Conservation Authority (GRCA).
Rigorous science-based forecasts to address how the surface and subsurface (groundwater) resources might be impacted by climate change will therefore necessarily demand the use of a computational platform that fully integrates the climate system with the surface/subsurface hydrological system in three dimensions. By establishing proactive science-based management policies now, such as water use quotas, limits on fertilizer and pesticide use, water treatment guidelines, flood control practices, etc., the future sustainability of the water resources can be protected, and perhaps even enhanced. The high-resolution regional climate simulations being performed by Prof. W.R. Peltier’s group will provide the data to drive our 3D integrated surface/subsurface hydrological model. We are also coordinated with the smart data collection activities being undertaken in the Southern Ontario Water Consortium (SOWC), of which IBM is a major partner.
Development of cardiac specific machine learning infrastructure
Analytics for Life, Inc. (A4L) is an early stage medical device company that specializes in the development of technologies to analyze patient physiological signals in order to evaluate cardiac performance, status and risk. A4L’s core competencies include identifying and developing mathematical features from physiological signals and assembling these features into clinically informative formulae using machine learning techniques. A4L has used third party machine learning tools (open source and licensed products) for the formula generation aspect of the product development cycle.
Specifically, A4L has used these tools to demonstrate the feasibility of computing left ventricular ejection fraction, cardiac ischemic burden and other cardiac performance/status parameters for simple to collect, non-invasive physiological signals (surface voltage gradients, SPO2, Impedance etc.). As a result of this experience, A4L have learned the benefits and insufficiencies of these tools for A4L’s specific purposes. A4L plans to file with the U.S. Food and Drug Administration (FDA) an application for approval of a physiological signal collection device and will soon afterwards be seeking market clearance for products assessing cardiac health emanating from the machine learning process. A4L believes it can build a machine learning tool specifically tailored for cardiac evaluation based on experience with the tools used to date.
This A4L-specific machine learning paradigm will search only relevant mathematical spaces, cutting down on time and CPU power needed to iterate to solutions and will allow for an assessment of a much wider array of potential solutions. Furthermore, this A4L-specific machine learning paradigm will provide a controlled and validated system that can be audited and evaluated by regulatory bodies, something that is not possible with the current machine learning tool(s). A4L proposes a hybridization of paradigms within a set mathematical space. This will create efficiency in the search, and therefore more searches can be performed in the same period of time. This will lead to more solutions being available for evaluation, resulting in more accurate and efficiently produced end solutions. If successful, this new paradigm will allow for simple, non-invasive, rapid and relatively inexpensive cardiac diagnostic capabilities, bringing tertiary care diagnostics to primary care settings and disrupting the current infrastructure and capital cost-centric model of diagnostic delivery.
Distributed and scalable search in enterprise databases
Google search, and other search engines such as Bing and Yahoo!, provide a convenient way to find Webpages that contain various keywords or are related to particular topics. For the purposes of searching, Webpages are essentially loosely structured paragraphs of text. However, much of the world’s high-quality enterprise data are structured into well defined tables containing sets of well-defined columns.
One consequence of structured database design is that information about a single entity may be scattered across many columns in many tables, and must be stitched together in a meaningful way when answering user queries. This turns out to be significantly more difficult than finding Webpages or text documents containing various keywords.
As Dr. Surajit Chadhuri (a Distinguished Scientist at Microsoft Research) recently argued in a keynote talk at the IEEE Data Engineering conference, search over structured databases has fallen behind search over unstructured data. In the proposed research, we will develop a powerful and intuitive search system, akin to Web keyword search, for structured enterprise data. Our system will empower nontechnical users to explore enterprise databases and turn big data into actionable insight, just as Google search has empowered society to explore the Web.
Distributed Deep Learning and Graph Analytics Using IBM Spectrum Computing Solutions
Deep learning is a popular machine learning technique and has been applied to many real-world problems, ranging from computer vision to natural language processing. In most cases deep learning outperformed previous work. However, training a deep neural network is very time-consuming, especially on big data. A popular solution is to distribute and parallel the training process across multiple machines. Indeed, the race is on to parallelize deep learning! Industry and academic research teams around the world are trying to make deep neural networks train as fast as possible on farms of GPU capable servers. We are working with our IBM partners to help develop advanced scheduling and messaging techniques for distributed deep learning. In addition, we will develop two real-world applications of distributed deep learning to demonstrate the efficiency and effectiveness of distributed deep learning. In one application, we address the video surveillance problem of tracking a moving target over a network of video cameras with partial or no overlaps in their coverage. We will use a deep learning approach to identify multiple pedestrians in each video frame, and a particle filter to track moving pedestrians. In the second application, we address the problem of fraud/intrusion detection. We will use graph-based detection that considers relationships between objects or individuals. Graph-based approaches are powerful because they do not operate on objects or individuals in isolation, but also consider their network information. We will emphasize on graph-based fraud detection methods that have a number of applications and potentially large impacts.
Genetic variation and structure-based drug polypharmacology: multiscale structural pharmacogenomics
Over the past 25 years, drug discovery efforts have been aimed at identifying small organic molecules that exert a strong effect on a single protein that corresponds to the biological target of a disease. Although designed to fulfill a single biological function, small molecule drugs can incidentally interact with hundreds of the ~20,000 known human gene products. These off-target interactions may lead to side effects, new therapeutic effects, or otherwise affect drug response. Mapping a drug to all of its human protein interaction partners is known as polypharmacology. Thus, it is advantageous to understand the polypharmacology of each drug in development prior to clinical testing in order to better anticipate off-target effects that could potentially cause toxicity. However, to address drug polypharmacology experimentally is prohibitively expensive in time and monetary cost. Predicting a drug’s polypharmacology is Cyclica’s central mandate. Our drug discovery platform is based on proteome-wide screening, and centered around PROBEx, a cloud-based software solution that simulates a drug’s interaction with 100,000’s of proteins on the basis of their molecular structures. PROBE x maps out the possible off-target protein partners and consequently pharmacology for any pharmaceutical or nutraceutical compounds, to service academic research institutes, hospitals, and pharmaceutical companies developing new disease therapies. Pharmacogenomics is the study of how genes affect a person’s response to drugs by combining pharmacology (the science of drugs), and genomics (the study of genes and their functions) to develop effective, safe medications that are tailored to a person’s genetic makeup.
This field, commonly termed “personalized medicine” has recently emerged and gained significant attention from many companies, including IBM Watson Life Sciences. To our knowledge however, information pertaining to the structural proteome and its relation to drug binding, has not yet been systematically leveraged to improve pharmacogenetic analyses. This technology gap represents a unique opportunity for Cyclica to contribute to personalized medicine, by working with SOSCIP and the IBM cloud analytics platform. Cyclica can integrate its drug polypharmacology predictions with clinical and genomic data to build models that can explain and predict variation in drug response between different individuals. Through Industrial Partner: Cyclica Inc. this program, we will create new tools that match patients to the pharmaceuticals best suited for their distinct genetic features. Ultimately, the effort will improve outcomes and limit the loss of valuable time associated with trial-and-error medicine.