Synthetic Lethal Interactions Prediction Based on Multiple Similarity Measures Fusion

• 作者: Lian-Lian Wu Yu-Qi Wen Xiao-Xi Yang Bo-Wei Yan Song He Xiao-Chen Bo
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: Special Section on AI Big Data Analytics in Biology and Medicine
• 页码范围: 261-275
• 页数: 15页
• 语种: 英文

摘要

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.