Earticle

초록

영어

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one- hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

목차

Abstract
 1. Introduction
 2. Background
  2.1 Deoxyribonucleic Acid (DNA)
  2.2 Restricted Boltzmann Machine
  2.2 Deep Belief Network for Text
 3. Proposed Method
  3.1 Encoding
  3.2 Forming DBN by Training Stack of RBMs and Fine-tuning
 4. Experimental Result and Discussion
  4.1 Dataset
  4.2 General Setup
  4.3 Result and Discussion
 5. Conclusion
 6. Acknowledgement
 References

키워드

저자

• Prima Sanjaya [ Department of Ubiquitous IT, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea ]
• Dae-Ki Kang [ Department of Computer & Information Engineering, Dongseo University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380