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Computing Concepts for Bioinformatics http://amadeus.biosci.arizona.edu/~nirav ,[object Object],[object Object],[object Object],[object Object]
Systems Biology:Confluence of omics Systems Biology Genomics Functional Genomics Meta- bolomics Proteomics Pharmaco- genomics Modelling Clinical Pathways
The players: Statistics Machine Learning Databases Data Visualization Data Mining and  Knowledge Discovery
Useful Websites: ,[object Object],[object Object],[object Object],[object Object]
Statistics, Machine Learning and Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Problems Suitable for Data-Mining  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Discovery Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Many Names of Data Mining  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Currently: Data Mining and Knowledge Discovery  are used interchangeably  Piatetsky-Shapiro
Major Data Mining Tasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Piatetsky-Shapiro
Finding patterns ,[object Object],[object Object],[object Object],[object Object],[object Object]
Machine learning techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Classification Learn a method for predicting the instance class from pre-labeled (classified)  instances Many approaches: Regression,  Decision Trees, Bayesian, Neural Networks,  ...  Given a set of points from classes  what is the class of new point  ?
Classification: Linear Regression ,[object Object],[object Object],[object Object],[object Object]
Classification: Decision Trees X Y if X > 5 then blue else if Y > 3 then blue else if X > 2 then green else blue 5 2 3
Classification: Neural Nets ,[object Object],[object Object],[object Object]
The weather problem Given past data, Can you come up with the rules for  Play/Not Play ? What is the game? no true 91 71 rainy yes false 75 81 overcast yes true 90 72 overcast yes true 70 75 sunny yes false 80 75 rainy yes false 70 69 sunny no false 95 72 sunny yes true 65 64 overcast no true 70 65 rainy yes false 80 68 rainy yes false 96 70 rainy yes false 86 83 overcast no true 90 80 sunny no false 85 85 sunny Play Windy Humidity Temperature Outlook
The weather problem ,[object Object],witten&eibe … … … … … Yes False Normal Mild Rainy Yes False High Hot  Overcast  No True High  Hot  Sunny No False High Hot Sunny Play Windy Humidity Temperature Outlook If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes If none of the above then play = yes
Weather data with mixed attributes ,[object Object],witten&eibe … … … … … Yes False 80 75 Rainy Yes False 86 83 Overcast  No True 90 80 Sunny No False 85 85 Sunny Play Windy Humidity Temperature Outlook If outlook = sunny and humidity > 83 then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity < 85 then play = yes If none of the above then play = yes
The contact lenses data witten&eibe None Reduced Yes Hypermetrope Pre-presbyopic  None Normal Yes Hypermetrope Pre-presbyopic None Reduced No Myope Presbyopic None Normal No Myope Presbyopic None Reduced Yes Myope Presbyopic Hard Normal Yes Myope Presbyopic None Reduced No Hypermetrope Presbyopic Soft Normal No Hypermetrope Presbyopic None Reduced Yes Hypermetrope Presbyopic None Normal Yes Hypermetrope Presbyopic Soft Normal No Hypermetrope Pre-presbyopic None Reduced No Hypermetrope Pre-presbyopic Hard Normal Yes Myope Pre-presbyopic None Reduced Yes Myope Pre-presbyopic Soft Normal No Myope Pre-presbyopic None Reduced No Myope Pre-presbyopic hard Normal Yes Hypermetrope Young None Reduced Yes Hypermetrope Young Soft Normal No Hypermetrope Young None Reduced No Hypermetrope Young Hard Normal Yes Myope Young None Reduced Yes Myope Young  Soft Normal No Myope Young None Reduced No Myope Young Recommended lenses Tear production rate Astigmatism Spectacle prescription Age
A complete and correct rule set witten&eibe If tear production rate = reduced then recommendation = none If age = young and astigmatic = no and tear production rate = normal then recommendation = soft If age = pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft If age = presbyopic and spectacle prescription = myope and astigmatic = no  then recommendation = none If spectacle prescription = hypermetrope and astigmatic = no and tear production rate = normal then recommendation = soft If spectacle prescription = myope and astigmatic = yes and tear production rate = normal then recommendation = hard If age young and astigmatic = yes  and tear production rate = normal then recommendation = hard If age = pre-presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none If age = presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none
A decision tree for this problem witten&eibe
Classifying iris flowers witten&eibe … … … Iris virginica 1.9 5.1 2.7 5.8 102 101 52 51 2 1 Iris virginica 2.5 6.0 3.3 6.3 Iris versicolor 1.5 4.5 3.2 6.4 Iris versicolor 1.4 4.7 3.2 7.0 Iris setosa 0.2 1.4 3.0 4.9 Iris setosa 0.2 1.4 3.5 5.1 Type Petal width Petal length Sepal width Sepal length If petal length < 2.45 then  Iris setosa If sepal width < 2.10 then  Iris versicolor ...
[object Object],[object Object],Predicting CPU performance witten&eibe 0 0 32 128 CHMAX 0 0 8 16 CHMIN Channels Performance Cache (Kb) Main memory (Kb) Cycle time (ns) 45 0 4000 1000 480 209 67 32 8000 512 480 208 … 269 32 32000 8000 29 2 198 256 6000 256 125 1 PRP CACH MMAX MMIN MYCT PRP = -55.9 + 0.0489 MYCT + 0.0153 MMIN + 0.0056 MMAX + 0.6410 CACH - 0.2700 CHMIN + 1.480 CHMAX
Soybean classification witten&eibe Diaporthe stem canker 19 Diagnosis Normal 3 Condition Roots … Yes 2 Stem lodging Abnormal 2 Condition Stem … ? 3 Leaf spot size Abnormal 2 Condition Leaves ? 5 Fruit spots Normal 4 Condition of fruit pods Fruit … Absent 2 Mold growth Normal 2 Condition Seed … Above normal 3 Precipitation July 7 Time of occurrence Environment Sample value Number of values Attribute
The role of domain knowledge ,[object Object],witten&eibe If leaf condition is normal and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown then diagnosis is rhizoctonia root rot If leaf malformation is absent and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown then diagnosis is rhizoctonia root rot
Learning as search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Enumerating the concept space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
The version space ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],witten&eibe
Machine Learning with WEKA
WEKA: the bird Copyright: Martin Kramer (mkramer@wxs.nl)
WEKA: the software ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WEKA: versions ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],WEKA only deals with “flat” files Flat file in ARFF format
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],WEKA only deals with “flat” files numeric attribute nominal attribute
 
 
Explorer: pre-processing the data ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
 
 
 
 
Explorer: building “classifiers” ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Final Project ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Final Project ,[object Object],[object Object],[object Object]
Final Project: Due dates ,[object Object],[object Object],[object Object]
MySQL exercise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Gratitude ,[object Object],[object Object],[object Object],[object Object]

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Part XIV

  • 1.
  • 2. Systems Biology:Confluence of omics Systems Biology Genomics Functional Genomics Meta- bolomics Proteomics Pharmaco- genomics Modelling Clinical Pathways
  • 3. The players: Statistics Machine Learning Databases Data Visualization Data Mining and Knowledge Discovery
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Classification Learn a method for predicting the instance class from pre-labeled (classified) instances Many approaches: Regression, Decision Trees, Bayesian, Neural Networks, ... Given a set of points from classes what is the class of new point ?
  • 13.
  • 14. Classification: Decision Trees X Y if X > 5 then blue else if Y > 3 then blue else if X > 2 then green else blue 5 2 3
  • 15.
  • 16. The weather problem Given past data, Can you come up with the rules for Play/Not Play ? What is the game? no true 91 71 rainy yes false 75 81 overcast yes true 90 72 overcast yes true 70 75 sunny yes false 80 75 rainy yes false 70 69 sunny no false 95 72 sunny yes true 65 64 overcast no true 70 65 rainy yes false 80 68 rainy yes false 96 70 rainy yes false 86 83 overcast no true 90 80 sunny no false 85 85 sunny Play Windy Humidity Temperature Outlook
  • 17.
  • 18.
  • 19. The contact lenses data witten&eibe None Reduced Yes Hypermetrope Pre-presbyopic None Normal Yes Hypermetrope Pre-presbyopic None Reduced No Myope Presbyopic None Normal No Myope Presbyopic None Reduced Yes Myope Presbyopic Hard Normal Yes Myope Presbyopic None Reduced No Hypermetrope Presbyopic Soft Normal No Hypermetrope Presbyopic None Reduced Yes Hypermetrope Presbyopic None Normal Yes Hypermetrope Presbyopic Soft Normal No Hypermetrope Pre-presbyopic None Reduced No Hypermetrope Pre-presbyopic Hard Normal Yes Myope Pre-presbyopic None Reduced Yes Myope Pre-presbyopic Soft Normal No Myope Pre-presbyopic None Reduced No Myope Pre-presbyopic hard Normal Yes Hypermetrope Young None Reduced Yes Hypermetrope Young Soft Normal No Hypermetrope Young None Reduced No Hypermetrope Young Hard Normal Yes Myope Young None Reduced Yes Myope Young Soft Normal No Myope Young None Reduced No Myope Young Recommended lenses Tear production rate Astigmatism Spectacle prescription Age
  • 20. A complete and correct rule set witten&eibe If tear production rate = reduced then recommendation = none If age = young and astigmatic = no and tear production rate = normal then recommendation = soft If age = pre-presbyopic and astigmatic = no and tear production rate = normal then recommendation = soft If age = presbyopic and spectacle prescription = myope and astigmatic = no then recommendation = none If spectacle prescription = hypermetrope and astigmatic = no and tear production rate = normal then recommendation = soft If spectacle prescription = myope and astigmatic = yes and tear production rate = normal then recommendation = hard If age young and astigmatic = yes and tear production rate = normal then recommendation = hard If age = pre-presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none If age = presbyopic and spectacle prescription = hypermetrope and astigmatic = yes then recommendation = none
  • 21. A decision tree for this problem witten&eibe
  • 22. Classifying iris flowers witten&eibe … … … Iris virginica 1.9 5.1 2.7 5.8 102 101 52 51 2 1 Iris virginica 2.5 6.0 3.3 6.3 Iris versicolor 1.5 4.5 3.2 6.4 Iris versicolor 1.4 4.7 3.2 7.0 Iris setosa 0.2 1.4 3.0 4.9 Iris setosa 0.2 1.4 3.5 5.1 Type Petal width Petal length Sepal width Sepal length If petal length < 2.45 then Iris setosa If sepal width < 2.10 then Iris versicolor ...
  • 23.
  • 24. Soybean classification witten&eibe Diaporthe stem canker 19 Diagnosis Normal 3 Condition Roots … Yes 2 Stem lodging Abnormal 2 Condition Stem … ? 3 Leaf spot size Abnormal 2 Condition Leaves ? 5 Fruit spots Normal 4 Condition of fruit pods Fruit … Absent 2 Mold growth Normal 2 Condition Seed … Above normal 3 Precipitation July 7 Time of occurrence Environment Sample value Number of values Attribute
  • 25.
  • 26.
  • 27.
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  • 30. WEKA: the bird Copyright: Martin Kramer (mkramer@wxs.nl)
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