Background One of the main current challenges in computational biology is to make sense
of the huge amounts of multidimensional experimental data that are being produced. For …

Background The availability of high-throughput omics datasets from large patient cohorts
has allowed the development of methods that aim at predicting patient clinical outcomes …

Multi-omics data are increasingly being gathered for investigations of complex diseases
such as cancer. However, high dimensionality, small sample size, and heterogeneity of …

Background High-throughput methodologies such as microarrays and next-generation
sequencing are routinely used in cancer research, generating complex data at different …

Background Neuroblastoma is one of the most common types of pediatric cancer. In current
neuroblastoma prognosis, patients can be stratified into high-and low-risk groups. Generally …

Multilayer networks allow interpreting the molecular basis of diseases, which is particularly
challenging in rare diseases where the number of cases is small compared with the size of …

Background More than 90% of neuroblastoma patients are cured in the low-risk group while
only less than 50% for those with high-risk disease can be cured. Since the high-risk …

Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular
profiling experiments have revealed that these tumors are extremely heterogeneous. This …

Neuroblastoma is a common cancer in children, affected by a number of genes that interact
with each other through intricate but coordinated networks. Traditional approaches can only …

Background Recent advances in genome technologies and the subsequent collection of
genomic information at various molecular resolutions hold promise to accelerate the …