Shenghao Zhao, Xiaoyu Zhang, Ziyuan Zhao, Peisheng Qian, Weide Liu , Zeng Zeng, Bharadwaj Veeravalli, Lingyun Dai, Par Nordlund, Nayana Prabhu, Wai Leong Tam, and Xulei Yang “Hybrid Model Design For Protein Function Prediction,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024
Abstract:
Considering the significant role of protein function
probes in medicine development and health monitoring, we
design a hybrid model based on traditional and deep learning
methods to predict protein functions with desirable accuracy.
Our work aims to better utilize the protein sequence information in our hybrid prediction model. Firstly, we introduce the
high-efficiency sequence alignment tool DIAMOND to obtain
function prediction reference based on sequence homology since
“similar” proteins have similar protein functions. Secondly,
we adopt deep learning methods to extract features from
encoded protein sequences, then combine sequence features
with domain features and protein-protein interaction (PPI)
features in the deep neural network. Finally, we determine
the best weight parameter between prediction results from
DIAMOND and deep neural network. The experimental results
show our proposed hybrid model outperforms traditional and
state-of-the-art deep learning methods for protein function
prediction.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP22-2019-0003