Tomasz Arodz, Ph.D. 
Associate Professor | VCU College of Engineering
Engineering East Hall, Room E4252, Richmond, VA, UNITED STATES
Dr. Arodz's research focuses on machine learning and bioinformatics
Links (1)
Biography
Tomasz Arodz is an assistant professor in the Department of Computer Science at Virginia Commonwealth University. Dr. Arodz holds a Ph.D. in computer science from AGH University of Science and Technology in Krakow, Poland. He is a laureate of the Prime Minister of Poland Award for his Ph.D. dissertation. Dr. Arodz also holds a M.Sc. in biotechnology from Jagiellonian University in Krakow.
Industry Expertise (4)
Computer Software
Education/Learning
Health and Wellness
Writing and Editing
Areas of Expertise (7)
Machine Learning: Nonlinear Classification Methods That Incorporate Existing Knowledge Into Training
Systems Biology: Integration of Prior Biological Knowledge and Multiple Sources of Data for Pathway Discovery
Computational Biology: Analysis of Role of Protein Mutations in Evolution and Disease
Pattern Recognition and Machine Learning in Biomedicine
Protein Evolution
Complex Biological Networks
Bioinformatics
Accomplishments (2)
Prime Minister of Poland Award (professional)
2008-01-01
Laureate of the Award for Ph.D. Dissertation
Foundation for Polish Science (professional)
2006-01-01
Laureate of the Young Researcher Stipend
Education (3)
Jagiellonian University: M.S., Biotechnology 2009
AGH University of Science and Technology: Ph.D., Computer Science 2007
AGH University of Science and Technology: M.S., Computer Science 2003
Media Appearances (1)
To Relieve Holiday Stress, Techies Trot Out Artificial Intelligence
Style Weekly print
2017-12-19
By now the journey to 2018 can feel more like a crawl than a mad dash. There's pressure to entertain family, reconnect with old friends and take that special someone on a memorable date. But a Richmond startup says artificial intelligence can solve the indecision over where to go and what to do. While some experts caution against placing exaggerated faith in artificial intelligence, early adopters are hoping for a more perfect holiday experience. . . . Just remember, if you're struggling to plan a not-so-silent night, don't give up on your gut, says Tom Arodz, another VCU professor who studies machine learning. "AI may learn to never recommend a symphony to heavy-metal lovers," Arodz says. "But just like with human instinct, it is often difficult to say why any particular recommendation is made."
Selected Articles (3)
Ensemble of Linear Models for Predicting Drug Properties
Journal of Chemical Information Models2006 We propose a new classification method for the prediction of drug properties, called random feature subset boosting for linear discriminant analysis (LDA). The main novelty of this method is the ability to overcome the problems with constructing ensembles of linear discriminant models based on generalized eigenvectors of covariance matrices. Such linear models are popular in building classification-based structure-activity relationships. The introduction of ensembles of LDA models allows for an analysis of more complex problems than by using single LDA, for example, those involving multiple mechanisms of action. Using four data sets, we show experimentally that the method is competitive with other recently studied chemoinformatic methods, including support vector machines and models based on decision trees. We present an easy scheme for interpreting the model despite its apparent sophistication. We also outline theoretical evidence as to why, contrary to the conventional AdaBoost ensemble algorithm, this method is able to increase the accuracy of LDA models.
Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
Combinatorial Chemistry & High Throughput Screening2006 Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.
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