Data Mining in Bioinformatics Series: Advanced Information and Knowledge Processing by Wang, J.T.L.; Zaki, M.J.; Toivonen, H.; Shasha, D.E. (Eds.) 1st Edition., 2005, XI, Springer.

• Witten's and Frank's textbook slides - Chapter 2.

The main objective of this lecture is to become familiar with the Weka system.

1. WekaTextbook slides provided above.

2. Work with the Weka System

   1. Get familiar with the sample datasets provided with the Weka system.

   2. Open the contact lenses dataset contact-lenses.arff with a word editor. The file consists of:

      • An OPTIONAL header with general information about the dataset. Each line in this header starts with "%" to denote that these are commented lines.

      • The name of the dataset

          @relation contact-lenses
                                   

      • The name and types of the attributes in the dataset

          @attribute age                  {young, pre-presbyopic, presbyopic}
          @attribute spectacle-prescrip   {myope, hypermetrope}
          @attribute astigmatism          {no, yes}
          @attribute tear-prod-rate       {reduced, normal}
          @attribute contact-lenses       {soft, hard, none}
                                   

        These attributes are the "column names" of the tabular data that comes after.

      • The dataset instances

          @data
        
          young,myope,no,reduced,none
          young,myope,no,normal,soft
          young,myope,yes,reduced,none
          young,myope,yes,normal,hard
           ...
        
                           

3. Run the Weka system. Choose the "Explorer" option in the main window.

4. Load the contact lenses dataset into the Weka System using "Open File".

5. Apply the following classification techniques (under "Classify") to this dataset

   • ZeroR: 0 predicting attributes are used to construct a classification rule

   • OneR: 1 predicting attribute is used to construct classification rules

   • Id3: Decision Trees

6. Go back to the "Preprocess" tab and modify the input dataset using the following preprocessing filters. Access the filters by clicking on the "Choose" button.

   • Remove This filter removes one or more attributes from the current dataset.
     1. Select "Remove" from "weka->filters->unsupervised->attribute"
     2. Click on "Remove" to access the Weka window with the parameters for this filter
     3. Click on "More" to learn more about this filter
     4. Use say "1-2" in the "AttributeIndices"
     5. Click "OK"
     6. Click "Apply" on the main Weka Window
     7. Describe how the dataset changed

   • Resample This filter selects a random sample from the data instances in the current dataset.
     1. Select "Resample" from "weka->filters->unsupervised->instance"
     2. Click on "Resample" to access the Weka window with the parameters for this filter
     3. Click on "More" to learn more about this filter
     4. Use say "20.0" in the "SampleSizePercent" to select a subsample of 20% of the current instances.
     5. Click "OK"
     6. Click "Apply" on the main Weka Window
     7. Describe how the dataset changed

7. If time permits, discuss how to convert a dataset to ARFF (attribute relation file format).

• diabetes.dat
• k_means_clustering_example_2D.m
• k_means_clustering_example_diabetes.m

• diabetes.dat
• hierarchical_clustering_example_2D.m
• hierarchical_clustering_example_diabetes.m

• See Bayesian Models
• EM Clustering using Matlab and Weka

• Probability and Bayesian Learning: Slides 125-130;
• Gibbs Classifier: slide 144;
• Naive Bayes: Slides 145-156;
• Bayesian Nets: Slides 158-167;
• Expectation Maximization (EM): Slides 168-174.

• Expectation Maximization and Gibbs Sampling

• diabetes_no_attribute_names.dat
• naive_bayes_example_diabetis.m

1. Gene Finding and HMMs

1. Ydo Wexler & Dan Geiger's Markov Chain Tutorial.
2. Hidden Markov Models (HMMs) Tutorial.

1. Prof. Ruiz's example of Viterbi's, Forward, and Backward algorithms
2. hmmgenerate_fair_loaded_coins_HMMs_tutorial_example.m using Matlab
3. hmmgenerate_pepsi_coke_HMMs_tutorial_example.m using Matlab

1. HMM Introduction
2. Computing Likelihood
3. HMM Training
4. HMM Applications

1. CpG islands, Markov Chains, HMMs
2. Forward and backward evaluation, HMM training

1. Markov models and Hidden Markov models
2. Estimating and using Hidden Markov models
3. Using HMMs for genefinding: GENSCAN and related models
4. Matlab Demos: Markov and HMM models

1. Introduction
2. Maximal Margin Classifier
3. The Kernel Trick

1. ** Introduction to Computational Learning Theory (COLT) Slides 10-23.
2. Suzi Fei's The "Kernel Trick" A Computational Biology Example using Support Vector Machines

1. Support vector machines and machine learning on documents

• Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze. "Introduction to Information Retrieval". Cambridge University Press. 2008.

• COMAD 2010 tutorial slides on IR and text mining in bioinformatics Part I and Part II.

• M.W. Berry, Z. Drmac, and E.R. Jessup, "Matrices, Vector Spaces, and Information Retrieval", SIAM Review, Vol.41, No.2, pp.335-362, 1999.

• My Bioinformatics and Computational Biology Resources and References.

• 2007 paper on progress in biomedical text mining
• 2005 survey of biomedical text mining
• Max Planck Tubingen slides on text mining for bioinformatics
• 2003 short paper on GENIA bio-text mining corpus