Thomas Sauter

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Luxembourg

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Recent Grants

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Personalized risk stratification (iron overload): an integrative machine learning approach

Open Date: 2020-01-01

Close Date:

Grant: Close

Data-driven computational modelling and applications

Open Date: 2018-01-01

Close Date: 2025-01-01

Grant: Close

Systems medicine based patient stratification in cancer therapy

Open Date: 2018-01-01

Close Date:

Grant: Close

Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity

Open Date: 2017-01-01

Close Date: 2021-01-01

Grant: Close

Training in Cancer Biology: Focus on Tumour Escape Mechanisms

Open Date: 2016-01-01

Close Date: 2021-01-01

Articles (10)

Expanding the Disease Network of Glioblastoma Multiforme via Topological Analysis

Glioblastoma multiforme (GBM), a grade IV glioma, is a challenging disease for patients and clinicians, with an extremely poor prognosis. These tumours manifest a high molecular heterogeneity, with limited therapeutic options for patients. Since GBM is a rare disease, sufficient statistically strong evidence is often not available to explore the roles of lesser-known GBM proteins. We present a network-based approach using centrality measures to explore some key, topologically strategic proteins for the analysis of GBM. Since network-based analyses are sensitive to changes in network topology, we analysed nine different GBM networks, and show that small but well-curated networks consistently highlight a set of proteins, indicating their likely involvement in the disease. We propose 18 novel candidates which, based on differential expression, mutation analysis, and survival analysis, indicate that they may play a role in GBM progression. These should be investigated further for their functional roles in GBM, their clinical prognostic relevance, and their potential as therapeutic targets.

Year:

2023

Collaborators (3)

Daniela De Zio

Københavns Universitet

DENMARK

Daniel Kwaku Abankwa

-

LUXEMBOURG

Jun Pang

-

LUXEMBOURG
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