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Predictive Analysis of Gene Expression Data from Human SAGE Libraries Alexessander Alves* Nikolay Zagoruiko + Oleg Okun § Olga Kutnenko + Irina Borisova.

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Presentation on theme: "Predictive Analysis of Gene Expression Data from Human SAGE Libraries Alexessander Alves* Nikolay Zagoruiko + Oleg Okun § Olga Kutnenko + Irina Borisova."— Presentation transcript:

1 Predictive Analysis of Gene Expression Data from Human SAGE Libraries Alexessander Alves* Nikolay Zagoruiko + Oleg Okun § Olga Kutnenko + Irina Borisova + * University of Porto, PORTUGAL + Russian Academy of Sciences RUSSIA § University of Oulu FINLAND

2 Outline 1. Goals 2. Background 3. SAGE Data 4. Gene Expression Data 5. Feature Selection 6. GRAD 7. Experiments 8. Conclusions

3 Goal Predictive Analysis: Feature Selection Methods in Bioinformatics and Machine Learning Cancer Classification

4 Background Genes code proteins and other larger biomolecules Genes are expressed in a two steps process (Central Dogma of Biology) Several technologies measure transcription: SAGE, Micro array… Central Dogma of Biology Gene Expression Process 1- Transcribed into an RNA Sequence 2- Translated into a protein Molla et al, 2003

5 SAGE DATA Advantages: Compare samples between different organs and patients. (No normalisation required) Collects complete gene expression profile of a cell/tissue without prior knowledge of the mRNA to be profiled

6 SAGE DATA Drawbacks: Very Expensive to Collect Data using the SAGE method Very Few Examples (consequence)

7 GENE EXPRESSION DATA Challenges posed to Machine Learning Number of Genes Dramatically Exceeds Examples!!! Curse of Dimensionality (not enough density to estimate accuratelly the model) Over-fitting (higher probability of finding casual relationships among data attributes)

8 Remove Irrelevant and Redundant Genes Methods: Wrapper Fit classifier to a subset of data and use classification accuracy to drive the search for relevant genes (e.g. C4.5 accuracy ) Filtering Use a function to assess the goodness of a subset of genes (e.g. euclidean distance, entropy, correlation, etc...) Problem Complexity O(2 n )... n, number of genes Smaller dataset n=822. O(2 n )  2.8x10 246  Intractable using a simple exaustive search Feature Selection

9 Gene Selection In Bioinformatics Filtering is usually prefered because is computationally less expensive Several works on classification select genes with: Wilcoxon test, t-test Additionally, also remove genes with low entropy, variability, or absolute expression level. Cons Redundancy Interdependency unaware

10 Our Proposals Study Bioinformatics Filtering Techniques Compare with Machine Learning Algorithms Avoid Redundancy Consider Interdependency and low expressed genes Introduce a new Filtering Algorithm GRAD

11 GRAD Search Strategy Search Strategy 1. Use Exaustive Search on the formation of informative groups of attributes (“granules”) 2. Use AdDel for choosing subsets of granules AdDel: A combination of forward sequential search (FSS) and backward sequential search (BSS) Number of attributes to include on a subset is estimated by algorithm

12 and are the distances to closest neighbors, one from each class GRAD Algorithm Algorithm P0: x1,x2,…,xn – initial set of features Formation of granules: Ordering by individual relevance G1: x7, x33, x12,…,xn All pairs by exhaustive search G2: x3x8, x15x88,…,xi xj All triplets by exhaustive search G3: x75x1x35, x11x49x55,…, xi xj xk Top level most relevant granules using AdDel G= … AdDel

13 Experiments Comparison 1. GRAD 2. Wrapper C4.5 3. Original Dataset 4. Filtering – Wilcoxon Test, low entropy, variability, and very low absolute expression level Classifiers 1. C4.5 2. SVM 3. RBF 4. NN-MLP Data Small Dataset: 74x822

14 Data Characterization Not all organs have samples of both classes Unbalanced number of cases: 50 Cancer Samples 24 Normal Samples Most data is relativelly low expressed Mean quite far from median: Potentially due to outliers

15 Data Characterization average vs standard deviation average vs range Both range and standard deviation have roughly linear relationship with gene expression level average

16 Experimental Results Predictive Accuracy GRAD WRAPPER Original Filtering 86% 82% 79% 78% GRAD is significantly better than using the original or the filtered dataset Wrapper approach is not

17 GRAD Results Importance of considering dependence Distance Function: 10best by GRAD P=100 % 10 most individually informative P=75,7 %

18 GRAD Results Scatter Plot of GRAD Attributes Interdependency relationship between two non differentially expressed genes selected with GRAD Two differentially expressed genes selected with GRAD.

19 GRAD Results Examples ordered by the value of the Distance Function In the future it can allow to estimate the degree of risk, to make early diagnostics and to supervise a course of treatment

20 Induced Classifiers C4.5 Induced on GRAD attributesC4.5 Induced using a Wrapper Approach

21 Conclusions 1. Coping with redundancy and dependency between attributes is very important. 2. Algorithm GRAD represents effective means to select a subset of attributes from very big initial set. 3. The submitted results have only illustrative character. 4. We are open for cooperation with those who have interest on the biological interpretation of results

22 Questions …

23 In increasing n the relevance grows, then growth stops and begins its decrease due to addition less informative, rustling attributes. The maximum of the curve of quality allows to specify optimum quantity of attributes. Only algorithms of AdDel family has such property. GRAD

24 Gene Selection In Bioinformatics Redundancy: some genes are highly correlated. (probably belonging to the same biological pathways) Curse of Dimensionality Interdependency: A few interdependent genes may carry together more significant information than a subset of independent genes. Loss of relevant information to discriminate among classes All this have a negative impact on predictive accuracy!!

25 Feature Selection Wrapper Considers the classifier while searching best subset Accuracy Improves May overfit due to small sample sizes and huge dimensionality Computationally more expensive Filtering: Potentially less accurate Faster: Does not requires the induction of a predictor Commonly prefered approach in bioinformatics

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