High-throughput genomic and proteomic technologies are widely used in cancer research to
build better predictive models of diagnosis, prognosis and therapy, to identify and …

Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

Translating data to knowledge and actionable insights is the Holy Grail for many scientific
fields, including biology. The unprecedented massive and heterogeneous data have created …

Background In this study, we evaluated the ability of radiomic textural analysis of
intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast …

Objective: To investigate whether machine learning analysis of multiparametric MR
radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. Study …

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs
remains one of the key challenges in cancer research. Despite its relevance, no efficient …

Background Radiomics is aimed at image-based tumor phenotyping, enabling application
within clinical-decision-support-systems to improve diagnostic accuracy and allow for …

We developed algorithms to identify pregnant women with suicidal behavior using
information extracted from clinical notes by natural language processing (NLP) in electronic …

Identification of carcinogenic activity is the primary goal of the 2-year bioassay. The expense
of these studies limits the number of chemicals that can be studied and therefore chemicals …

Recent advances in proteomics allow the accurate measurement of abundances for
thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for …