Epilepsi ile ilgili GWAS veri kümesinde alt ağ arama programlarının değerlendirmesi

Öz

Aktif alt ağ tespiti, bir protein-protein etkileşim ağında hastalıkla ilgili genlerin birbirine bağlı bir grup genini bulmayı amaçlamaktadır. Son yıllarda bu problem için çeşitli algoritmalar geliştirilmiştir. Bu çalışmada, hastalığa özgü alt ağ tanımlama programlarının analizleri epilepsi veri seti kullanılarak değerlendirilmiştir. Aynı koşullar altında ve aynı veri seti ile 9 farklı program çalıştırılmış ve bu programların Greedy algoritması, Genetik algoritma, Simüle Tavlama Algoritması, MCC (Maximal Clique Centrality) algoritması, MCODE (Molecular Complex Detection) algoritması ve PEWCC (Protein Complex) Ağırlıklı Kümeleme Katsayısı) algoritması sonuçları gösterilmiştir. Her programın en yüksek puan alan 5 modülü, kat zenginleştirme analizi ve normalleştirilmiş karşılıklı bilgi kullanılarak karşılaştırılmıştır. Aynı zamanda tanımlanan alt ağlar, hipergeometrik test kullanılarak fonksiyonel olarak zenginleştirilmiş ve hastalıkla ilişkili biyolojik yollar belirlenmeye çalışılmıştır. Ayrıca programların çalışma süreleri ve özellikleri karşılaştırmalı olarak değerlendirilmiştir.

Anahtar Kelimeler

• Protein-Protein etkileşim ağları
• Aktif alt-ağ araması
• Foksiyonel zenginleştirme analizi
• Kat zenginleştirme
• Normalleştirilmiş karşılıklı bilgi

Evaluation of Sub-Network search programs in epilepsy-related GWAS dataset

Abstract

The active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.

Keywords

• Protein-Protein interaction networks
• Active subnetwork search
• Functional enrichment analysis
• Fold enrichment
• Normalized mutual information

Kaynakça

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