Suzana de Siqueira Santos

Suzana de Siqueira Santos
Fundação Getulio Vargas | FGV · School of Applied Mathematics "EMAp"

PhD

About

13
Publications
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163
Citations

Publications

Publications (13)
Article
Full-text available
Despite the development of specific therapies against severe acute respiratory coronavirus 2 (SARS-CoV-2), the continuous investigation of the mechanism of action of clinically approved drugs could provide new information on the druggable steps of virus–host interaction. For example, chloroquine (CQ)/hydroxychloroquine (HCQ) lacks in vitro activity...
Article
Full-text available
We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, b...
Article
Random graph models are used to describe the complex structure of real-world networks in diverse fields of knowledge. Studying their behaviour and fitting properties are still critical challenges that, in general, require model-specific techniques. An important line of research is to develop generic methods able to fit and select the best model amo...
Preprint
Full-text available
Repositioning of clinical approved drugs could represent the fastest way to identify therapeutic options during public health emergencies, the majority of drugs explored for repurposing as antivirals for 2019 coronavirus disease (COVID-19) have failed to demonstrate clinical benefit. Without specific antivirals, the severe acute respiratory syndrom...
Article
Full-text available
Background Glioblastoma is the most frequent and high-grade adult malignant central nervous system tumor. The prognosis is still poor despite the use of combined therapy involving maximal surgical resection, radiotherapy, and chemotherapy. Metabolic reprogramming currently is recognized as one of the hallmarks of cancer. Glutamine metabolism throug...
Preprint
Full-text available
Random graph models are used to describe the complex structure of real-world networks in diverse fields of knowledge. Studying their behavior and fitting properties are still critical challenges, that in general, require model specific techniques. An important line of research is to develop generic methods able to fit and select the best model amon...
Article
The modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graph...
Article
Full-text available
The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control vs. disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks si...
Chapter
The graph spectrum, defined as the set of eigenvalues of its adjacency matrix, is tightly associated with the graph structure. In this chapter, first, we use the graph spectrum to define the concept of graph spectral entropy, which measures the amount of uncertainty/randomness associated with the graph model. Based on it, we describe statistical me...
Article
Recent studies have suggested abnormal brain network organization in subjects with Autism Spectrum Disorders (ASD). Here we applied spectral clustering algorithm, diverse centrality measures (betweenness (BC), clustering (CC), eigenvector (EC), and degree (DC)), and also the network entropy (NE) to identify brain sub-systems associated with ASD. We...
Article
Full-text available
Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correl...
Article
One major task in molecular biology is to understand the dependency among genes to model gene regulatory networks. Pearson’s correlation is the most common method used to measure dependence between gene expression signals, but it works well only when data are linearly associated. For other types of association, such as non-linear or non-functional...

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