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Novel Scheme for Essential Proteins Identification Based on Improved Multicriteria Decision Making

基于改进多准则决策的关键蛋白质识别方案

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Abstract

Identifying essential proteins from protein-protein interaction networks is important for studies on biological evolution and new drug’s development. Most of the presented criteria for prioritizing essential proteins only focus on a certain attribute of the proteins in the network, which suffer from information loss. In order to overcome this problem, a relatively comprehensive and effective novel method for essential proteins identification based on improved multicriteria decision making (MCDM), called essential proteins identification-technique for order preference by similarity to ideal solution (EPI-TOPSIS), is proposed. First, considering different attributes of proteins, we propose three methods from different aspects to evaluate the significance of the proteins: gene-degree centrality (GDC) for gene expression sequence; subcellular-neighbor-degree centrality (SNDC) and subcellular-in-degree centrality (SIDC) for subcellular location information and protein complexes. Then, betweenness centrality (BC) and these three methods are considered together as the multiple criteria of the decision-making model. Analytic hierarchy process is used to evaluate the weights of each criterion, and the essential proteins are prioritized by an ideal solution of MCDM, i.e., TOPSIS. Experiments are conducted on YDIP, YMIPS, Krogan and BioGRID networks. The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifying the essential proteins through the performance measures.

摘要

从蛋白质相互作用网络中识别关键蛋白质对生物进化和新药物研制具有重要意义. 目前许多蛋白质关键性的评判标准只关注蛋白质的某个属性, 这会有信息丢失的问题. 针对这一问题, 本文提出一种基于改进多准则决策的更全面有效的关键蛋白质鉴定方法(EPI-TOPSIS). 首先, 考虑蛋白质的不同属性, 从三个不同的方面来评估蛋白质重要性: 基于表达序列的基因度中心性; 基于定位信息和蛋白质复合物的亚细胞-邻居度中心性与亚细胞-复合物入度中心性. 然后将介数中心性与这三种方法一起考虑作为多准则决策模型的属性准则, 采用层次分析法赋予各个准则权重, 通过多准则决策的逼近理想距离求解蛋白质关键性, 并对蛋白质进行优先级排序. 最后, 在YDIP、 YMIPS、 Krogan和BioGRID网络上进行实验, 结果表明EPI-TOPSIS性能优于对比算法.

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Pengli Lu  (卢鹏丽) & Yuntian Chen  (陈云天)

  2. China Mobile Communications Group Gansu Co., Ltd., Lanzhou, 730070, China

    Yonggang Liao  (廖永刚)

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  1. Pengli Lu  (卢鹏丽)
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  2. Yuntian Chen  (陈云天)
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  3. Yonggang Liao  (廖永刚)
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Correspondence to Pengli Lu  (卢鹏丽).

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Foundation item: the National Natural Science Foundation of China (Nos. 62162040 and 11861045)

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Lu, P., Chen, Y. & Liao, Y. Novel Scheme for Essential Proteins Identification Based on Improved Multicriteria Decision Making. J. Shanghai Jiaotong Univ. (Sci.) 28, 418–431 (2023). https://doi.org/10.1007/s12204-023-2584-0

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