Inferring interaction networks from multi-omics data
A major goal in systems biology is a comprehensive description of the entirety of all complex
interactions between different types of biomolecules—also referred to as the interactome …
interactions between different types of biomolecules—also referred to as the interactome …
Computational approaches for network-based integrative multi-omics analysis
Advances in omics technologies allow for holistic studies into biological systems. These
studies rely on integrative data analysis techniques to obtain a comprehensive view of the …
studies rely on integrative data analysis techniques to obtain a comprehensive view of the …
Integrated BATF transcriptional network regulates suppressive intratumoral regulatory T cells
Human regulatory T cells (Tregs) are crucial regulators of tissue repair, autoimmune
diseases, and cancer. However, it is challenging to inhibit the suppressive function of Tregs …
diseases, and cancer. However, it is challenging to inhibit the suppressive function of Tregs …
Host-response subphenotypes offer prognostic enrichment in patients with or at risk for acute respiratory distress syndrome
Objectives: Classification of patients with acute respiratory distress syndrome into hyper-and
hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective …
hypoinflammatory subphenotypes using plasma biomarkers may facilitate more effective …
Gaussian and Mixed Graphical Models as (multi-) omics data analysis tools
M Altenbuchinger, A Weihs, J Quackenbush… - … et Biophysica Acta (BBA …, 2020 - Elsevier
Abstract Gaussian Graphical Models (GGMs) are tools to infer dependencies between
biological variables. Popular applications are the reconstruction of gene, protein, and …
biological variables. Popular applications are the reconstruction of gene, protein, and …
Lipidomic signatures align with inflammatory patterns and outcomes in critical illness
Alterations in lipid metabolism have the potential to be markers as well as drivers of
pathobiology of acute critical illness. Here, we took advantage of the temporal precision …
pathobiology of acute critical illness. Here, we took advantage of the temporal precision …
Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models
Introduction Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk
populations for early lung cancer diagnosis. However, 96% of individuals with detected …
populations for early lung cancer diagnosis. However, 96% of individuals with detected …
Current and future directions in network biology
Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …
sciences, is critical for deepening understanding of cellular functioning and disease. While …
Learning high-dimensional directed acyclic graphs with mixed data-types
In recent years, great strides have been made for causal structure learning in the high-
dimensional setting and in the mixed data-type setting when there are both discrete and …
dimensional setting and in the mixed data-type setting when there are both discrete and …
Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems
Background Learning the causal structure helps identify risk factors, disease mechanisms,
and candidate therapeutics for complex diseases. However, although complex biological …
and candidate therapeutics for complex diseases. However, although complex biological …