Prediction of mutation effects using a deep temporal convolutional neural network = 딥 템포럴 컨볼루션 신경망을 이용한 돌연변이 영향 예측 연구

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 17
  • Download : 0
Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model based on a Temporal Convolutional Network architecture, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence. We show that this model is competitive with the variational autoencoder model when tested against a set of 42 deep mutational scan experiments. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared to the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process.
Advisors
Kim, Dongsupresearcher김동섭researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2020.2,[iv, 33 p. :]

Keywords

protein sequence analysis▼amutation effect▼adeep learning▼agenerative model▼atemporal convolutional network; 단백질 서열 분석▼a돌연변이 영향▼a딥러닝▼a생성 모델▼a템포럴 컨벌루션 신경망

URI
http://hdl.handle.net/10203/283833
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=909914&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0