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Zoom link.

This is our 3rd talk of the PIMS Data Science Seminar Series. PIMS requests all seminar participants to complete the demographics form online.

Abstract:
In this data explosion era, machine learning has accelerated research in biology and medicine at an unprecedent speed and in multiple dimensions. The increased data volume and capacity for data aggregation and analytics power, along with decreasing costs of genome sequencing has spurred the growth in bioinformatics and need for novel tools to integrate the highly heterogenous data from multiple sources and of varying types, and extract meaningful patterns. The big data analytics and AI tools have already created significant impact in many fields of life sciences including medicine. However, the data complexity and multi-dimensionality have led to technical challenges in developing and validating AI solutions that generalize to diverse populations and imped the progress in their implementation in clinical practice due to imbalance in data distribution across population demography and data sparsity. This leads to the unconscious biases in the generated models and algorithms. In this talk, the speaker will discuss applications of AI in biology and medical research, advances and major challenges.

Short bio of the speaker:
Dr. Youlian Pan is an international expert in integrative pattern recognition from big data in Life Sciences. He has authored and co-authored over 80 refereed articles and created significant applications of data mining, machine learning, AI and bioinformatics in genomics, transcriptomics and systems biology with various medical applications, such as cancers, infectious diseases and neurodegenerative diseases. He also has extensive research interest in plants’ pathogenesis and embryogenesis, their interaction with environment, and biological oceanography specifically in marine pollution. Dr. Pan is a Senior Research Scientist at the National Research Council Canada and an Adjunct Professor at the University of Victoria and Brock University. He received his PhD in Biology and Master of Computer Science from Dalhousie University. He has served at various capacities in editorial board of six international journals, such as Journal of Computations & Modeling, Open Medical Informatics, and Frontiers in Genetics, Microbiology and Plant Sciences; and various national and international grant evaluation panels such as Natural Sciences and Engineering Research Council (NSERC) of Canada, National Science Foundation (NSF) of US.

Zoom link.

PIMS requests all seminar participants to complete the demographics form online at https://ubc.ca1.qualtrics.com/jfe/form/SV_6QcNr2rQcIlQGyy

Abstract: In this talk, we will share a new evolutionary but ensemble method, that enable us to track different regimes of behavior and identify when the changes occurred. As opposed to traditional methods that relies on statistical change tracking to detect intrusions, we use the performance, and the predictive power of evolving models to detect when and which models can or cannot describe the observations anymore. By doing so, we obtain a much fine-grain, more adaptive outlier detection algorithm that can reliably model data while being robust to its variations. The algorithm can be viewed as an evolutionary algorithm with growth and new generation capabilities, but it is special in the sense that it includes ensemble of models with performance measures and age decay corrections to evolve and compare models. For some special cases, the algorithm can be related to Bayesian Change Point techniques.

Bio: Belaid Moa received the B.Sc. degree in electrical engineering from École Hassania des Travaux Publics, Casablanca, Morocco, the M.Eng. degree in electronics and signal processing from École Nationale Supérieure d'électronique, d'électrotechnique, d'informatique, d'hydraulique et des Télécommunications, Toulouse, France, the DEA Diploma degree in Telecommunications and Networks from the Institute National Polytechnique de Toulouse, Toulouse, and the Ph.D. degree in computer science from the University of Victoria. He is currently an adjunct faculty with ECE Dept., and Advanced Research Computing Specialist with the Digital Research Alliance of Canada/BCDRI /University Systems, at the University of Victoria. He has authored or co-authored many research articles and conference proceedings in various journals and multi-disciplinary research areas

PCIC is pleased to announce an upcoming talk on Wednesday, November 22nd, titled, Clustering for Climate Science Insights, as part of our Pacific Climate Seminar Series.

This talk will be delivered by Dr. John R.J. Thompson, an Assistant Professor at the University of British Columbia (Okanagan campus) whose areas of expertise are nonparametric and applied statistics and machine learning. This talk will be held between 3 p.m. and 4 p.m. Pacific Time, via Zoom meetings. For more on this talk, including registration information and an abstract, see the talk’s page on our site.

This talk is co-sponsored by Pacific Institute for the Mathematical Sciences (PIMS).

This seminar will be in person and available through Zoom.

PIMS requests all seminar participants to complete the demographics form online at https://ubc.ca1.qualtrics.com/jfe/form/SV_6QcNr2rQcIlQGyy

Abstract: In life history studies one often encounters situations where individuals in a population are eligible to enroll only if the response time does not exceed an associated censoring time, which leads to the so called left truncated lifetime data. While auxiliary information for the truncated individuals from the same or similar cohorts may be available, challenges arise due to the practical issue of accessibility of individual-level data and taking account of various sampling conditions for different cohorts. We propose a likelihood-based method for incorporating auxiliary data to eliminate the bias due to left-truncation and improve efficiency. Simulation results and an application to data from a longitudinal study of aging are given.

Abstract: Plant virus infection causes enormous economic loss, $350 billion worldwide in 2021. We focus on grapevine viruses, which caused Canadian grape growers’ annual loss of over $23 million. Once infected by a virus, the grapevine remains infected for life and must be replaced, since no therapeutic treatments are available. So, diagnosis of virus infection is critical for disease control. Collaborating with researchers from CFIA and other institutes, we are developing a genomic-sequencing-based diagnostic test to detect virus infections of grapevines. More information about this project can be found at https://www.bloomberg.com/press-releases/2020-10-27/-10m-in-funding-coming-to-bc-researchers-for-improved-grapevine-and-cannabis-management.

My role is to support the method/software development to decide if a sample is infected by any virus (from a list of ~1000 viruses) based on its genomic sequencing data. In this talk, I will briefly discuss our recent research results from this project.

Abstract: Neural spike trains are sequences of consecutive electrochemical waves generated by the nerve cells through which they communicate. These waves are localized in time hence called spikes. Statistical analysis of simultaneously recorded neural spike trains from an ensemble of neurons is a challenging problem from both statistical and computational points of view. I will discuss some biologically inspired multivariate point process models for such data, namely- Skellam Process with Resetting (SPR) and a generalization of it under a continuous-time latent factor model (LFM). To the best of our knowledge, this generalization of the SPR is the first continuous-time multivariate LFM for studying neuronal interactions and functional connectivity. Leveraging the computational efficacy of approximate Bayesian inference, we show that our model can handle larger neuronal ensembles compared to alternative approaches. Using experimental data from a classical conditioning study on the prefrontal cortex in rats, we shed light on our understanding of cue and outcome value encoding.

Abstract: I introduce a method for approximate marginal likelihood inference via adaptive Gaussian quadrature in mixed models with a single grouping factor. The core technical contributions are (a) an algorithm for computing the exact gradient of the approximate log marginal likelihood and (b) a useful parameterization of the multivariate Gaussian. The former leads to efficient quasi-Newton optimization of the marginal likelihood that is several times faster than established methods; the latter gives Wald confidence intervals for random effects variances that attain nominal coverage and low bias if enough quadrature points are used. The Laplace approximation is a special case of the method and is shown in simulations to perform exceptionally poorly for binary random slopes models, but this is mitigated by just adding more quadrature points.