Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 261-275.doi: 10.1007/s11390-021-0866-2

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

Synthetic Lethal Interactions Prediction Based on Multiple Similarity Measures Fusion

Lian-Lian Wu1,2,?, Yu-Qi Wen2,?, Xiao-Xi Yang2,3, Bo-Wei Yan2, Song He2,*, and Xiao-Chen Bo1,2,*        

  1. 1 Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China;
    2 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China;
    3 Experimental Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100850, China
  • Received:2020-08-03 Revised:2021-02-28 Online:2021-03-05 Published:2021-04-01
  • Contact: Song He, Xiao-Chen Bo;
  • About author:Lian-Lian Wu is a Master student in Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin. She majors in biomedical engineering. Her research interests include bioinformatics, machine learning and deep learning.

The synthetic lethality (SL) relationship arises when a combination of deficiencies in two genes leads to cell death, whereas a deficiency in either one of the two genes does not. The survival of the mutant tumor cells depends on the SL partners of the mutant gene, thereby the cancer cells could be selectively killed by inhibiting the SL partners of the oncogenic genes but normal cells could not. Therefore, there is an urgent need to develop more efficient computational methods of SL pairs identification for cancer targeted therapy. In this paper, we propose a new approach based on similarity fusion to predict SL pairs. Multiple types of gene similarity measures are integrated and k-nearest neighbors algorithm (k-NN) is applied to achieve the similarity-based classification task between gene pairs. As a similarity-based method, our method demonstrated excellent performance in multiple experiments. Besides the effectiveness of our method, the ease of use and expansibility can also make our method more widely used in practice.

Key words: synthetic lethality; similarity measures fusion; k-nearest neighbor; multi-dimensional data;

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