ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification

被引:1
作者
Kandula, Maciej M. [1 ,2 ]
Aldoshin, Alexander D. [1 ]
Singh, Swati [1 ,3 ]
Kolaczyk, Eric D. [4 ]
Kreil, David P. [1 ]
机构
[1] Boku Univ Vienna, Inst Mol Biotechnol, Vienna, Austria
[2] Janssen Pharmaceut NV, Beerse, Belgium
[3] Indian Inst Technol Kanpur, Dept Biol Sci & Bioengn, Kanpur, India
[4] McGill Univ, Dept Math & Stat, Montreal, PQ, Canada
关键词
COMPARING CLUSTERINGS; CANCER; CLASSIFICATION; SURVIVAL; BREAST; MODEL; ACCUMULATION; PREDICTION; REVEALS; HEALTH;
D O I
10.1093/nar/gkac988
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.
引用
收藏
页码:E6 / E6
页数:14
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