Cancer Mutations Scores & RStudio Tune-up
Details
Talk 1: Classification and Statistical Analysis of Cancer Mutations Scores
By: Yemi Odeyemi (Ph.D. Candidate in Data Science at Chapman University)
The talk will describe part of Yemi's doctoral work on building a statistical and predictive model to classify driver-passenger mutations. A Logit model is used with 10-fold cross-validation. The data was preprocessed to impute missing values using the rule-of-thumb approach, removal of redundant features and feature scaling. Feature selection was determined using a stepwise approach based on AIC. The objective was to determine the optimal class boundary for the probability for discretization. The models were evaluated with Receiver Operator Characteristics - Area under the curve (ROC-AUC) which is based on sensitivity and specificity.
Talk 2: Tune up your RStudio Experience
By: Ryan Benz (SoCal Bioinformatics, Inc.)
In this talk, Ryan will discuss some of the ways you can tune-up your RStudio experience to make it easier to work with and to stream line commonly performed tasks to support your coding and analysis work.
Agenda:
6:30-7:00 Welcome and Networking
7:00-8:00
- Introduction
- Talk 1: Classification and Statistical Analysis of Cancer Mutations Scores
- Talk 2: Tune up your RStudio Experience
8:00-8:30 Clean-up and networking
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