Multi-omics Data Integration and Network Inference for Biomarker Discovery in Glioma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Glioma is a family of brain tumors with three main types exhibiting different progressions, which lack effective therapeutic options and specific molecular biomarkers. In this work, we propose a pipeline for multi-omics integrated analysis aimed at identifying features that could impact the development of different gliomas, assigned according to the latest classification guidelines. We estimate networks of genes and proteins based on human data, via the graphical lasso, as a network-based step towards variable selection. The estimated glioma networks were compared to disclose molecular relations that can be important for the development of a certain tumor type. Our outcomes were validated both mathematically, and through principal component analysis to determine if the selected subset of variables carries enough biological information to distinguish the three glioma types in a reduced dimensional subspace. The results highlight an overall agreement in variable selection across the two omics. Features exclusively selected by each glioma type appear as more representative of the pathological condition, making them suitable as potential diagnostic biomarkers. The comparison between glioma-type networks and with known protein-protein interactions reveals the presence of molecular relations that could be associated to a pathological condition. The 59 features identified by our analysis will be further considered to extend our work by integrating targeted biological evaluation.
Original languageEnglish
Title of host publicationProgress in Artificial Intelligence
Subtitle of host publication22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5–8, 2023, Proceedings, Part II
EditorsNuno Moniz, Zita Vale, José Cascalho, Catarina Silva, Raquel Sebastião
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-031-49011-8
ISBN (Print)978-3-031-49010-1
Publication statusPublished - Dec 2023
Event22nd EPIA Conference on Artificial Intelligence, EPIA 2023 - Faial Island, Portugal
Duration: 5 Sept 20238 Sept 2023

Publication series

NameLecture Notes in Computer Science
Volume14116 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd EPIA Conference on Artificial Intelligence, EPIA 2023
CityFaial Island


  • Glioma
  • Network distance
  • Network inference
  • Principal component analysis
  • Proteomics
  • Transcriptomics


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