Presentation is loading. Please wait.

Presentation is loading. Please wait.

Protein Fold Recognition as a Data Mining Coursework Project Badri Adhikari Department of Computer Science University of Missouri-Columbia.

Published byKelly Powers Modified over 8 years ago

Similar presentations

Presentation on theme: "Protein Fold Recognition as a Data Mining Coursework Project Badri Adhikari Department of Computer Science University of Missouri-Columbia."— Presentation transcript:

1 Protein Fold Recognition as a Data Mining Coursework Project Badri Adhikari Department of Computer Science University of Missouri-Columbia

2 Overview Problem Description Data Description and Specification Data Preprocessing Methodology Discussion of Results

3 The Problem Identify if two proteins belong to same fold [Binary Classification] Protein [Structure] Database Protein 1 Protein 2 Protein1-Protein20.005 Feature 1 0.065 Feature 2 0.79 Feature 3 Protein 3 Protein2-Protein30.034 0.152 Protein Pair Y Same Fold? N

4 The Problem Identify if two proteins belong to same fold [Binary Classification] Protein1-Protein20.005 Feature 1 0.065 Feature 2 0.79 Feature 3 Protein2-Protein30.034 0.152 Protein Pair Y Same Fold? N Protein fold recognition Customer 00010.005 Feature 1 0.065 Feature 2 0.79 Feature 3 Customer 00020.034 0.152 Customer Identification Y Potential Customer Y Customer 00030.005 0.0650.79N Recognizing Potential New Customers

5 Data Specification File size: 1.5G Examples count: 951600 Positive(+1) labels:7438 Negative(-1) labels: 944162 Number of features: 84

6 Data Description #119l-d119l 1alo-d1alo_1 -1 1:1.62 2:1.13 3:0.913193847041375 4:0.592859664812671 5:0.900250016135814 6:0.373223645145762 7:0.1888487926 #119l-d119l 1chka-d1chka +1 1:1.62 2:2.38 3:0.940068662681886 4:0.750255408177719 5:0.915009668297068 6:0.42843991595408 7:0.21728602097 #119l-d119l 1fcdc-d1fcdc1 -1 1:1.62 2:0.8 3:0.817167643633653 4:0.232414817929789 5:0.856456855019105 6:0.218979560580544 7:0.03189285886 #119l-d119l 1gal-d1gal_1 -1 1:1.62 2:3.22 3:0.917750359046243 4:0.658669958746871 5:0.901110932746401 6:0.415795355014216 7:0.1805356684 #119l-d119l 1gbs-d1gbs +1 1:1.62 2:1.85 3:0.933021070719294 4:0.703993132134445 5:0.911155732036236 6:0.358955863036017 7:0.1302872472 #119l-d119l 1iov-d2dln_2 -1 1:1.62 2:2.1 3:0.893588888139836 4:0.603748017846831 5:0.886157496493048 6:0.374372299060332 7:0.15693565952 #119l-d119l 1kte-d1kte -1 1:1.62 2:1.05 3:0.884958967781326 4:0.527597954487768 5:0.883935799166059 6:0.238710961583473 7:0.0332240052 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.900757073008603 6:0.294119634790149 7:0.0624425214 #119l-d119l 2baa-d2baa +1 1:1.62 2:2.43 3:0.856257831918934 4:0.450623745727366 5:0.869561412073741 6:0.378932373372537 7:0.1569895990 #119l-d119l 6lyt-d193l +1 1:1.62 2:1.29 3:0.904095075521014 4:0.602608563805903 5:0.893625220106668 6:0.337268512070013 7:0.1408736485 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.90075707 #119l-d119l 1sly-d1sly_2

7 Data Description #119l-d119l 1alo-d1alo_1 -1 1:1.62 2:1.13 3:0.913193847041375 4:0.592859664812671 5:0.900250016135814 6:0.373223645145762 7:0.1888487926 #119l-d119l 1chka-d1chka +1 1:1.62 2:2.38 3:0.940068662681886 4:0.750255408177719 5:0.915009668297068 6:0.42843991595408 7:0.21728602097 #119l-d119l 1fcdc-d1fcdc1 -1 1:1.62 2:0.8 3:0.817167643633653 4:0.232414817929789 5:0.856456855019105 6:0.218979560580544 7:0.03189285886 #119l-d119l 1gal-d1gal_1 -1 1:1.62 2:3.22 3:0.917750359046243 4:0.658669958746871 5:0.901110932746401 6:0.415795355014216 7:0.1805356684 #119l-d119l 1gbs-d1gbs +1 1:1.62 2:1.85 3:0.933021070719294 4:0.703993132134445 5:0.911155732036236 6:0.358955863036017 7:0.1302872472 #119l-d119l 1iov-d2dln_2 -1 1:1.62 2:2.1 3:0.893588888139836 4:0.603748017846831 5:0.886157496493048 6:0.374372299060332 7:0.15693565952 #119l-d119l 1kte-d1kte -1 1:1.62 2:1.05 3:0.884958967781326 4:0.527597954487768 5:0.883935799166059 6:0.238710961583473 7:0.0332240052 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.900757073008603 6:0.294119634790149 7:0.0624425214 #119l-d119l 2baa-d2baa +1 1:1.62 2:2.43 3:0.856257831918934 4:0.450623745727366 5:0.869561412073741 6:0.378932373372537 7:0.1569895990 #119l-d119l 6lyt-d193l +1 1:1.62 2:1.29 3:0.904095075521014 4:0.602608563805903 5:0.893625220106668 6:0.337268512070013 7:0.1408736485 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.90075707 #119l-d119l 1sly-d1sly_2 Protein query-target pair as example id

8 Data Description #119l-d119l 1alo-d1alo_1 -1 1:1.62 2:1.13 3:0.913193847041375 4:0.592859664812671 5:0.900250016135814 6:0.373223645145762 7:0.1888487926 #119l-d119l 1chka-d1chka +1 1:1.62 2:2.38 3:0.940068662681886 4:0.750255408177719 5:0.915009668297068 6:0.42843991595408 7:0.21728602097 #119l-d119l 1fcdc-d1fcdc1 -1 1:1.62 2:0.8 3:0.817167643633653 4:0.232414817929789 5:0.856456855019105 6:0.218979560580544 7:0.03189285886 #119l-d119l 1gal-d1gal_1 -1 1:1.62 2:3.22 3:0.917750359046243 4:0.658669958746871 5:0.901110932746401 6:0.415795355014216 7:0.1805356684 #119l-d119l 1gbs-d1gbs +1 1:1.62 2:1.85 3:0.933021070719294 4:0.703993132134445 5:0.911155732036236 6:0.358955863036017 7:0.1302872472 #119l-d119l 1iov-d2dln_2 -1 1:1.62 2:2.1 3:0.893588888139836 4:0.603748017846831 5:0.886157496493048 6:0.374372299060332 7:0.15693565952 #119l-d119l 1kte-d1kte -1 1:1.62 2:1.05 3:0.884958967781326 4:0.527597954487768 5:0.883935799166059 6:0.238710961583473 7:0.0332240052 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.900757073008603 6:0.294119634790149 7:0.0624425214 #119l-d119l 2baa-d2baa +1 1:1.62 2:2.43 3:0.856257831918934 4:0.450623745727366 5:0.869561412073741 6:0.378932373372537 7:0.1569895990 #119l-d119l 6lyt-d193l +1 1:1.62 2:1.29 3:0.904095075521014 4:0.602608563805903 5:0.893625220106668 6:0.337268512070013 7:0.1408736485 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.90075707 #119l-d119l 1sly-d1sly_2 Labels

9 Data Description #119l-d119l 1alo-d1alo_1 -1 1:1.62 2:1.13 3:0.913193847041375 4:0.592859664812671 5:0.900250016135814 6:0.373223645145762 7:0.1888487926 #119l-d119l 1chka-d1chka +1 1:1.62 2:2.38 3:0.940068662681886 4:0.750255408177719 5:0.915009668297068 6:0.42843991595408 7:0.21728602097 #119l-d119l 1fcdc-d1fcdc1 -1 1:1.62 2:0.8 3:0.817167643633653 4:0.232414817929789 5:0.856456855019105 6:0.218979560580544 7:0.03189285886 #119l-d119l 1gal-d1gal_1 -1 1:1.62 2:3.22 3:0.917750359046243 4:0.658669958746871 5:0.901110932746401 6:0.415795355014216 7:0.1805356684 #119l-d119l 1gbs-d1gbs +1 1:1.62 2:1.85 3:0.933021070719294 4:0.703993132134445 5:0.911155732036236 6:0.358955863036017 7:0.1302872472 #119l-d119l 1iov-d2dln_2 -1 1:1.62 2:2.1 3:0.893588888139836 4:0.603748017846831 5:0.886157496493048 6:0.374372299060332 7:0.15693565952 #119l-d119l 1kte-d1kte -1 1:1.62 2:1.05 3:0.884958967781326 4:0.527597954487768 5:0.883935799166059 6:0.238710961583473 7:0.0332240052 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.900757073008603 6:0.294119634790149 7:0.0624425214 #119l-d119l 2baa-d2baa +1 1:1.62 2:2.43 3:0.856257831918934 4:0.450623745727366 5:0.869561412073741 6:0.378932373372537 7:0.1569895990 #119l-d119l 6lyt-d193l +1 1:1.62 2:1.29 3:0.904095075521014 4:0.602608563805903 5:0.893625220106668 6:0.337268512070013 7:0.1408736485 #119l-d119l 1sly-d1sly_2 +1 1:1.62 2:1.68 3:0.914572798274142 4:0.607628373774305 5:0.90075707 #119l-d119l 1sly-d1sly_2 Feature values for each examples

10 Preprocessing Task 1: Group related data rows Problem : All The records are not independent of each other. Solution: Group records with same query template together, so that they are together either in the test data set or training data set. #119l-d119l 1alo-d1alo_1 -1 1:1.62 2:1.13 3:0.913193847041375 4:0.592859664812671 5:0.900250016135814 6:0.373223645145762 7:0.1888487926 #119l-d119l 1chka-d1chka +1 1:1.62 2:2.38 3:0.940068662681886 4:0.750255408177719 5:0.915009668297068 6:0.42843991595408 7:0.21728602097 #1aab-d1aab 1cyx-d1cyx -1 1:0.83 2:1.58 3:0.771248274633033 4:0.362259086646671 5:0.824117832571076 6:0.248160387073783 #119l-d119l 1fcdc-d1fcdc1 -1 1:1.62 2:0.8 3:0.817167643633653 4:0.232414817929789 5:0.856456855019105 6:0.218979560580544 7:0.03189285886

11 Preprocessing Task 2: Balance the positive and negative data Problem: Dataset has just 0.78% positive examples All Examples All +ve examples -ve examples (equal to +ve) Balancing the number of positive and negative examples All Random Remaining -ve examples Used only for testing Balanced examples

12 Methodology SVM light as the mining tool SVM light is an implementation of Support Vector Machines (SVMs) in C $ svm_learn example1/train.dat example1/model $ svm_classify example1/test.dat example1/model example1/prediction

13 Methodology Process for deciding the Kernel Function Many different kernels: linear, polynomial, radial basis function, or user defined. Consider the RBF kernel K(x, y) = Parameters to consider: -mmemory size of cache for kernel evaluations - ggamma value for rbf kernel -ctrade-off between training error and margin Use cross-validation to find the best parameter C and ϒ Use the best parameter C and ϒ to train the whole training set

14 Determining gamma parameter: – Ran training and testing for 100 gamma values between 0 and 1 – Found gamma = 0.15 as the best value – Ran again to find more precise gamma for 120 values from 0 to 0.3 – Found best value of gamma as 0.1 Used default C value of 0 Methodology Parameter Determination

15 ROC curve For different values of threshold - average sensitivity and specificity was computed from values in each fold threshold Evaluation with 10-fold cross-validation For threshold = -1.02 ThresholdSensivitySpecificityFPRAccuracyPrecision -1.020.7860.7690.2310.7690.027 1-specificity sensitivity

16 References A machine learning information retrieval approach to protein fold recognition by Jianlin Cheng and Pierre Baldi A Practical Guide to Support Vector Classification by Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdfhttp://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf Cross-Validation by PAYAM REFAEILZADEH, LEI TANG, HUAN LIU available at http://www.public.asu.edu/~ltang9/papers/ency-cross-validation.pdf http://www.public.asu.edu/~ltang9/papers/ency-cross-validation.pdf Classroom slides at http://people.cs.missouri.edu/~chengji/datamining2012/Chapter4_Classification_Prediction. ppt http://people.cs.missouri.edu/~chengji/datamining2012/Chapter4_Classification_Prediction. ppt

17 Thank you for your time Questions and comments are welcome.

Download ppt "Protein Fold Recognition as a Data Mining Coursework Project Badri Adhikari Department of Computer Science University of Missouri-Columbia."

Similar presentations

(SubLoc) Support vector machine approach for protein subcelluar localization prediction (SubLoc) Kim Hye Jin Intelligent Multimedia Lab. 2001.09.07.

(SubLoc) Support vector machine approach for protein subcelluar localization prediction (SubLoc) Kim Hye Jin Intelligent Multimedia Lab
CSC321: Introduction to Neural Networks and Machine Learning Lecture 24: Non-linear Support Vector Machines Geoffrey Hinton.

CSC321: Introduction to Neural Networks and Machine Learning Lecture 24: Non-linear Support Vector Machines Geoffrey Hinton.
Sequential Minimal Optimization Advanced Machine Learning Course 2012 Fall Semester Tsinghua University.

Sequential Minimal Optimization Advanced Machine Learning Course 2012 Fall Semester Tsinghua University.
ECG Signal processing (2)

ECG Signal processing (2)
ONLINE ARABIC HANDWRITING RECOGNITION By George Kour Supervised by Dr. Raid Saabne.

ONLINE ARABIC HANDWRITING RECOGNITION By George Kour Supervised by Dr. Raid Saabne.
An Introduction of Support Vector Machine

An Introduction of Support Vector Machine
An Introduction of Support Vector Machine

An Introduction of Support Vector Machine
Search Engines Information Retrieval in Practice All slides ©Addison Wesley, 2008.

Search Engines Information Retrieval in Practice All slides ©Addison Wesley, 2008.
Machine learning continued Image source: https://www.coursera.org/course/mlhttps://www.coursera.org/course/ml.

Machine learning continued Image source:
Particle swarm optimization for parameter determination and feature selection of support vector machines Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen,

Particle swarm optimization for parameter determination and feature selection of support vector machines Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen,
Learning Visual Similarity Measures for Comparing Never Seen Objects Eric Nowak, Frédéric Jurie CVPR 2007.

Learning Visual Similarity Measures for Comparing Never Seen Objects Eric Nowak, Frédéric Jurie CVPR 2007.
Face Recognition & Biometric Systems Support Vector Machines (part 2)

Face Recognition & Biometric Systems Support Vector Machines (part 2)
Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.

Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.
Standard electrode arrays for recording EEG are placed on the surface of the brain. Detection of High Frequency Oscillations Using Support Vector Machines:

Standard electrode arrays for recording EEG are placed on the surface of the brain. Detection of High Frequency Oscillations Using Support Vector Machines:
Image classification Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?

Image classification Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?
SUPPORT VECTOR MACHINES PRESENTED BY MUTHAPPA. Introduction Support Vector Machines(SVMs) are supervised learning models with associated learning algorithms.

SUPPORT VECTOR MACHINES PRESENTED BY MUTHAPPA. Introduction Support Vector Machines(SVMs) are supervised learning models with associated learning algorithms.
Presentation in IJCNN 2004 Biased Support Vector Machine for Relevance Feedback in Image Retrieval Hoi, Chu-Hong Steven Department of Computer Science.

Presentation in IJCNN 2004 Biased Support Vector Machine for Relevance Feedback in Image Retrieval Hoi, Chu-Hong Steven Department of Computer Science.
5/30/2006EE 148, Spring 20061 Visual Categorization with Bags of Keypoints Gabriella Csurka Christopher R. Dance Lixin Fan Jutta Willamowski Cedric Bray.

5/30/2006EE 148, Spring Visual Categorization with Bags of Keypoints Gabriella Csurka Christopher R. Dance Lixin Fan Jutta Willamowski Cedric Bray.
What is Learning All about ?  Get knowledge of by study, experience, or being taught  Become aware by information or from observation  Commit to memory.

What is Learning All about ?  Get knowledge of by study, experience, or being taught  Become aware by information or from observation  Commit to memory.
Face Processing System Presented by: Harvest Jang Group meeting Fall 2002.

Face Processing System Presented by: Harvest Jang Group meeting Fall 2002.

Similar presentations

Ads by Google