S.N.U. EECS Jeong-Jin Lee Eui-Taik Na

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Presentation transcript:

S.N.U. EECS Jeong-Jin Lee Eui-Taik Na Locating exons S.N.U. EECS Jeong-Jin Lee Eui-Taik Na

Using Multilayer Neural Network Approach Part I. Using Multilayer Neural Network Approach

System Architecture Output Input Representation Learning Finding exon starting region and ending region Input Representation Orthogonal encoding Using windows [0, 0, 0, 0] ~ outside the sequences Learning Backpropagation

Parameters 1 Interpretations of inputs Case 1 - aligning the center - aligning the end Case 3, 4 - biased

Parameters 2 The size of window Number of units in the hidden layer

Experimental Results 1 Three datasets from UCSC Data set 1 Data set 2 Having 7 subsets Data set 1 3447 trainings, 251 evaluations Data set 2 3448 trainings, 250 evaluations Evaluation Score Sums of success hits’ percentages

Experimental Results 2 According to window type

Experimental Results 3 According to window size

Experimental Results 4 According to number of hidden units

Part 2 Using HMMs

Using HMMs The Tied Model The Wheel Model

Future considerations HMM + NN mixed models