Probability-based collaborative filtering model for predicting gene–disease associations

BMC Medical Genomics - Tập 10 - Trang 45-53 - 2017
Xiangxiang Zeng1,2, Ningxiang Ding1, Alfonso Rodríguez-Patón2, Quan Zou3
1Department of Computer Science, School of Information Science and Technology, Xiamen University, Xiamen, China
2Department of Artificial Intelligence, Universidad Politcnica de Madrid (UPM), Madrid, Spain
3School of Computer Science and Technology, Tianjin University, Tianjin, China

Tóm tắt

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

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BMC Medical Genomics

Tập 10

45-53

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