Calendar

Mar
21
Fri
A Pipeline for High-Dimensional Time Course Gene Expression Data to Study Dynamic Network Responses to Viral Infections @ North Ross 638
Mar 21 @ 10:30 – 11:30
A Pipeline for High-Dimensional Time Course Gene Expression Data to Study Dynamic Network Responses to Viral Infections
Hulin Wu, Ph.D., Dean’s Professor
Department of Biostatistics and Computational Biology
Director, Center for Integrative Bioinformatics and Experimental Mathematics
University of Rochester School of Medicine and Dentistry
Email: Hulin_Wu@urmc.rochester.edu
A new pipeline for high-dimensional time course gene expression data is developed based on the concept of function data analysis (FDA) with a purpose to study dynamic network responses at gene level. The pipeline includes significant testing for dynamic response genes (DRGs), clustering gene response curves, constructing dynamic gene response networks using differential equation models, network feature analysis, dynamic system analysis, and biological annotations. Novel statistical methods and modeling approaches are developed for the pipeline, which include high-dimensional ODE model selection, parameter estimation, and dynamic system characteristic analysis. We illustrate the pipeline and the proposed methods using genome-wide time course gene expression data from mice and human subjects challenged by influenza viruses. Some interesting biological findings will be discussed.
Mar
24
Mon
Is Item-Level Non-Invariance Always Important? @ Norm Endler Room (BSB 164)
Mar 24 @ 10:15 – 11:15

Quantitative Methods Forum @ Norm Endler Room (BSB 164)

Mar 24 @ 10:15 AM – 11:15 AM

Speaker: Alyssa Counsell, York University
Department of Psychology

Title: Is Item-Level Non-Invariance Always Important?

Abstract: Differential Item Functioning (DIF) refers to measurement non-invariance across groups. In other words, DIF is present when individuals from two distinct groups with equivalent levels of the latent trait or ability demonstrate different response patterns. The implication is that group membership (instead of the latent trait) accounts for the difference in responding. There are several methods that test for DIF but I will use item response theory (IRT) in the current presentation. Specifically I will discuss DIF results that compare Canadian and German participants’ response patterns on each of the items of the General Self Efficacy Scale (Schwarzer & Jerusalem, 1995). The results demonstrate a practical concern for researchers. When DIF is present in some items, the implications for research are not always clear. In some instances the pattern of DIF may not consistently favour one group, and instead, item-level group differences may appear to cancel each other out if the total test information curve is examined. In psychology where groups are typically compared on test information rather than on an item-to-item basis, DIF may not represent a meaningful or important effect.

Mar
31
Mon
The Yuen-Welch and Generalized Linear Model Approaches for Analyzing Skewed and Heteroscedastic Data in Psychology and A brief survey of current statistical methods for meta-analyzing data produced by single-case experimental designs @ Norm Endler Room (BSB 164)
Mar 31 @ 10:15 – 11:15

Speaker: Victoria Ng and Joo Ann Lee, York University
Department of Psychology

Speaker: Victoria Ng

Title: The Yuen-Welch and Generalized Linear Model Approaches for Analyzing Skewed and Heteroscedastic Data in Psychology

Abstract: Many psychological studies are designed for testing whether there are group mean differences for some continuous outcome variable. However, the assumptions of normality and heteroscedasticity underlying traditional methods (i.e., ANOVA/OLS regression) are often violated. Two alternative methods are discussed: the Yuen-Welch with trimmed means, and the Generalized Linear Model (GLM). Given the many specifications that are possible in the GLM, selected studies on competing estimators from health outcomes literature are touched upon. With the premise that one would ideally choose the method that yields both adequate power and estimates that represent all relevant data (i.e., including distribution tails), I address the motivation for comparing the Yuen-Welch and the GLM by simulation and discuss potential implementations of such a study.

Speaker: Joo Ann Lee

Title: A brief survey of current statistical methods for
meta-analyzing data produced by single-case experimental designs

Abstract: Single-case experimental designs (SCEDs; also known as n-of-1 trials,
small-n designs, single-subject designs, and interrupted time-series
experimental designs, among others) are a set of experimental designs
that employ repeated data collection over time on a single unit of
interest such as an individual, a family, or an institution. SCEDs are
especially beneficial when the research areas studied have high
variability, or a low prevalence rate, because the unit serves as its
own control. More specifically, SCEDs explicitly focus on
within-individual variability. Unfortunately, a single SCED provides
very little, if any, information about between-individual variability.
This disadvantage however, can be remediated by meta-analyzing results
from separate SCEDs. Nonetheless, the meta-analytic methods of SCEDs
are just beginning to be developed. The presentation will begin with a
review of the type of data common to SCEDs, followed by illustrations
of current popular methods to analyze and meta-analyze SCED data, and
conclude with future research in the area.

Apr
30
Wed
SORA / BN / TABA Workshop
Apr 30 all day

SORA-TABA workshop will be held on Wednesday, April 30th, 2014

 University of Toronto
Health Sciences Building (the auditorium – HS610),

155 College Street, Toronto ON.

 

Recent Advanced in Deep Learning:

Learning Structured, Robust, and Multimodal Models

Building intelligent systems that are capable of extracting meaningful representations from high-dimensional data lies at the core of solving many Artificial Intelligence tasks, including visual object recognition, information retrieval, speech perception, and language understanding.

In this talk I will first introduce a broad class of hierarchical probabilistic models called Deep Boltzmann Machines (DBMs) and show that DBMs can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will then describe a new class of more complex models that combine Deep Boltzmann Machines with structured hierarchical Bayesian models and show how these models can learn a deep hierarchical structure for sharing knowledge across hundreds of visual categories, which allows accurate learning of novel visual concepts from few examples. Finally, I will introduce deep models that are capable of extracting a unified representation that fuses together multiple data modalities. I will show that on several tasks, including modelling images and text, video and sound, these models significantly improve upon many of the existing techniques.

Ruslan Salakhutdinov

Assistant Professor,
Department of Computer Science and
Department of Statistical Sciences
University of Toronto

May
24
Sat
SSC Student Conference @ University of Toronto
May 24 all day

SSC Student Conference in Toronto

Date: May 24, 2014
Location: University of Toronto
Link

May
25
Sun
SSC Annual Meeting
May 25 – May 28 all day

SSC Annual Meeting in Toronto

Date: May 25 to May 28, 2014
Location: University of Toronto
Link

May
28
Wed
TABA seminar @ Room 7-605, Princess Margaret Hospital
May 28 @ 18:00 – May 28 @ 20:30

Title:   Using Animal Instincts to Find Efficient Experimental Designs
Speaker: Weng Kee Wong, Dept of Biostatistics, UCLA

ABSTRACT
Using popular models from the biological and pharmaceutical sciences as examples, I demonstrate how particle swarm optimization (PSO) searches for different types of optimal experimental designs in dose response studies, including mini-max types of optimal designs where effective algorithms to find such designs have remained elusive until now.

Oct
20
Mon
TABA Seminar: Interrupted time series
Oct 20 @ 18:00 – Oct 20 @ 20:00

Topic: Interrupted time series
Presenter: Yan Chen, St. Michael’s Hospital; TARGet Kids project

Abstract: more details to follow

When: Monday October 20th, 2014
6-7 pm seminar
7 pm dinner (optional, but registration required)
Where: Room 696, UoT Health Sciences Building,
155 College Street <----------Note different location Cost: Seminar - free Dinner - $15 (students $5) To attend, please RSVP by or on Friday the 17th of October: * for seminar only, please email us by clicking here: mailto:taba.exec@gmail.com?subject=SeminarOnly * for seminar and dinner, please email us by clicking here: mailto:taba.exec@gmail.com?subject=SeminarAndDinner If you plan to attend the dinner, we accept a mailed cheque, Interac email money transfer or cash at the event. If you have to cancel the dinner we request a 48 hour notice otherwise TABA has to pay for it. For Interac email money transfer (not PayPal): do the transfer via your bank to taba.exec@gmail.com and send a separate email via your own email program to us (taba.exec@gmail.com) giving the answer to the security question you used so that we can accept the transfer. For cheques: mail cheque, payable to TABA, to Lorinda Simms Eli Lilly Canada Inc. 3650 Danforth Ave Toronto, ON M1N 2E8

Mar
2
Mon
TABA seminar: Janet McDougall & Lorinda Simms @ Room 7-605, PMH
Mar 2 @ 18:00 – Mar 2 @ 20:30
Apr
21
Tue
TABA seminar
Apr 21 @ 18:00 – Apr 21 @ 21:00

Topic: Maternal and Child Health Gains in Afghanistan in the Post-Taliban Era: A Synthesis of Big Data  (Presenter: Nadia Akseer)