Investigating and Assessing Diverse Strategies and Classification Techniques Applied in the Integration of Multi-Omics Data

İpekten, Funda; Koçhan, Necla; Ertürk Zararsız, Gözde; Doğan, Halef; Eldem, VAHAP; Zararsız, Gökmen

doi:10.1007/978-3-032-06649-7_6

Investigating and Assessing Diverse Strategies and Classification Techniques Applied in the Integration of Multi-Omics Data

İpekten F., Koçhan N., Ertürk Zararsız G., Doğan H. O., Eldem V., Zararsız G.

Big Data Analytics in Biostatistics and Bioinformatics, Yichuan Zhao,Ding-Geng Chen, Editör, Springer Nature Switzerland Ag, Zug, ss.123-145, 2026

Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
Basım Tarihi: 2026
Doi Numarası: 10.1007/978-3-032-06649-7_6
Yayınevi: Springer Nature Switzerland Ag
Basıldığı Şehir: Zug
Sayfa Sayıları: ss.123-145
Editörler: Yichuan Zhao,Ding-Geng Chen, Editör
İstanbul Üniversitesi Adresli: Evet

Özet

High-throughput technologies have recently attracted much attention in academia. These technologies enable gathering information from various biological datasets, including genomics, transcriptomics, proteomics, and metabolomics data. Integrating and analyzing various datasets will allow us to accurately diagnose the disease, deepen our understanding of biological processes, and develop advanced treatment approaches. Numerous integration techniques have been developed for this aim, enabling researchers to unravel the complex patterns and underlying mechanisms behind biological occurrences, including those associated with illnesses. This study aims to explore and employ various data integration methodologies and machine-learning techniques on multi-omics data to predict disease classes using real datasets.