A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM Networks
Nowadays,with the increasing number of protein sequences all over the world,more and more people are paying their attention to predicting protein subcellular location.Since wet experiment is costly and time-consuming,the automatic computational methods are urgent.In this paper,we propose a variant model based on Long Short-Term Memory(LSTM)to predict protein subcellular location.In this model,we employ LSTM to capture long distance dependency features of the sequence data.Moreover,we adjust the loss function of the loss layer to solve multi-label classification problem.Experimental results demonstrate that,compared with the traditional machine learning methods,our method achieves the best performance in various evaluation metrics.
LSTM multi-label classification protein subcellular localization
Zhiying Gao Lijun Sun Zhihua Wei
Department of Computer Science and Technology,Tongji University,Shanghai,China Research Center of Big Data and Network Security,Tongji University,Shanghai200092,China Key Laboratory of Embedded System and Service Computing,Tongji University,Shanghai201804,China
国际会议
深圳
英文
248-252
2018-01-21(万方平台首次上网日期,不代表论文的发表时间)