The PRoBE laboratory for Pattern Recognition from Biomedical Evidence has been studying genomic, proteomic, imaging, microbiome and metabolomics data for early detection and monitoring of diverse diseases including cancers of the lung, breast and esophagus, as well as adverse cardiovascular events.
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning r
Lifan's Abstract: Integrating multiple sources of biological data has great potential to provide a better understanding of how genes function together. This study adopted the multiplex framework to integrate different high-throughput data for functional module identification. Our results showed that, with appropriate clustering algorithms, the multiplex formulation achieved a better accuracy for functional module identification.
Yifan's Abstract: Somatic genome alterations (SGAs) are one of the major causes of cancer. Among all the genetic alterations observed in a cancer cell, only a small fraction, known as drivers, directly contribute to tumor growth. The set of drivers in each tumor preserves most of the tumor-specific oncogenic information.
Jenna's Abstract: Resting-state functional magnetic resonance images (rs-fMRI) are invaluable tools for evaluating the neurodevelopmental status of infants and neonates. Unfortunately, rs-fMRI sequences are highly susceptible to motion. Many post-acquisition motion mitigation techniques have been developed to attempt to remove the effects of motion from rs-fMRI sequences.
There has been a lot of hype recently regarding AI and Healthcare. Sanjay will present an introduction to a use-case based approach for AI in Healthcare -- he will cover the broad categories of Financial, Operational and Clinical use-cases with Security as an envelope use-case. The learning objectives are practical business oriented Machine Learning and Deep Learning with specific focus on the data and processes involved.
Imaging genomics is an emerging data science field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventative approaches.
Technology to collect and analyse data relating to human health and behaviour will increasingly become a part of everyday life. For the first time, human kind has the opportunity to “time-travel” but not in a mechanical device but through simulation that will allow representations of the past (e.g.