Tutorials and Workshops
Introduction to Meta-Genomic Analysis (Summer 2011)
Dr. Alekseyenko taught this course as part of the 2011 Summer Institute in Statistics and Modeling in Infectious Diseases, June 13-29, 2011 at the University of Washington in Seattle. Please email Dr. Alekseyenko at Alexander.Alekseyenko@nyumc.org for possible course offerings in the future.
Introduction to Proteomics (Winter 2011)
Dr. Fenyo presented an overview of proteomics and mass spectrometry workflows, experimental design, and data analysis. He covered the following subjects in three lectures: (1) Protein identification (peptide mass fingerprinting, tandem mass spectrometry, database searching, spectrum library searching, de novo sequencing, significance testing); (2) Protein characterization (protein coverage, top-down proteomics, post-translational modifications, protein processing and degradation, protein complexes); (3) Protein quantitation (metabolic labeling - SILAC, chemical labeling, label-free quantitation, spectrum counting, stoichiometry, biomarker discovery and verification) Examples will be provided throughout the course on how the different approaches can be applied to investigate biological systems.
Slides/ Videos:
Proteomics Workshop I (Slides) (Video)
Proteomics Workshop II (Slides)
Proteomics Workshop III (Slides)
Tutorial in Support Vector Machines: Theory and Applications in Biomedicine. (Fall 2009, Fall 2008)
Dr. Aliferis and Dr. Statnikov gave this tutorial at the Annual AMIA Conference in 2008 and 2009. The contents of this tutorial are shown below.
Authors: Alexander Statnikov, Douglas Hardin, Isabelle Guyon, Constantin Aliferis. Materials about SVM Clustering were contributed by Nikita Lytkin.
Abstract:
This half-day tutorial will introduce support vector machines (SVMs) and their applications in biomedicine. SVMs are among the most important recent developments of machine learning and pattern recognition and have extensive applications in biomedicine and other fields. Unlike other approaches, these techniques are robust in data analysis with high variable-to-sample ratios and large number of irrelevant variables, they can learn efficiently very complex functions, and they employ powerful regularization principles to avoid overfitting.
A common obstacle in understanding and using SVMs is that they are mathematically challenging especially for biomedical researchers lacking extensive technical backgrounds. The tutorial is designed to enable all interested researchers grasp SVM fundamentals regardless of prior mathematical training. First, we will introduce basic principles behind SVMs in an intuitive manner. Then we will describe SVM-based algorithms for classification, regression, clustering and novelty detection, variable selection and dimensionality reduction. These algorithms are widely used and/or gaining popularity in biomedical applications. Throughout the tutorial we will provide case studies for each class of methods and give pointers to software implementations and additional literature. The knowledge gained in this tutorial will allow researchers to break the barriers of classical statistics and older pattern recognition and be able to conduct complex and high-dimensional analyses easily and efficiently.
Slides: Download pdf
Video (password-protected):
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Copyright notice: Copyright (c) of its authors. No reproduction is allowed without written permission.
Notice about upcoming books: The authors wrote a book based on the material in this tutorial. Volume 1 titled "A Gentle Introduction to Support Vector Machines in Biomedicine, Volume 1: Methods and Theory" is available for purchase from Amazon. The authors will publish Volume 2 in the Winter of 2012.
Special Faculty/Staff Tutorial on Local causal Discovery and Feature selection methods (Spring 2009)
Dr. Aliferis introduced to CHIBI faculty and staff, some fundamental algorithms and principles for causal graph-based local causal discovery and feature selection. The tutorial may be continued/expanded and opened to the general public given enough interest. Please contact Constantin Aleferis or Alexander Statnikov
Tutorial in Machine Learning for Biomedical Decision Support and Discovery (Fall 2004, Fall 2003)
Dr. Aliferis and colleague Dr. Tsamardinos gave this tutorial at AMIA 2003 and MedInfo 2004. The contents of this tutorial are shown below:
Authors: Constantin F. Aliferis and Ioannis Tsamardinos. Alexander Statnikov contributed supplementary information.
Abstract: The purpose of this tutorial is to (i) help participants develop a solid understanding of some of the most useful machine learning methods, (ii) give several examples of how these methods can be applied in practice, and (iii) provide resources for expanding the knowledge gained in the tutorial.
Slides:
- Tutorial overview and goals
- Importance of Machine Learning for discovery and decision support system construction
- A framework for inductive Machine Learning
- Generalization and overfitting
- Quick review of data preparation and model evaluation
- Bayesian classifiers
Presentation file II: Neural Networks
Presentation file III: Case Study on Predicting Breast Cancer Invasion with Artificial Neural Networks on the Basis of Mammographic Features
Presentation file IV: Support Vector Machines
Presentation file V: Quick Review of Additional families (K-Nearest Neighbors, Clustering, Decision Tree Induction, Genetic Algorithms)
Presentation file VI: Causal Discovery Methods Using Causal Probabilistic Networks
Presentation file VII: Feature selection
Presentation file VIII: Case studies
- Conclusions and wrap-up
- Resources for machine learning
- Questions & feedback
Copyright notice: Copyright (c) of its authors. No reproduction is allowed without written permission.
For recommendations for new tutorials please contact Dr. Alexander Statnikov: alexander.statnikov@med.nyu.edu.
