Academic Articles & Research Papers

Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.

Progressive suppression of regulated cell death defines terminal macrophage states in liver cirrhosis

Background Macrophages are central regulators of liver immunity and fibrosis; Recent single-cell studies have characterized macrophage heterogeneity in cirrhosis; however, how regulated cell death programs shape their functional transitions...

Identification of DDIT4 as a potential prognostic marker associated with chemotherapeutic and immunotherapeutic response in triple-negative breast cancer

Abstract Background Triple-negative breast cancer (TNBC) is the most heterogenous and aggressive subtype of breast cancer. Chemotherapy remains the standard treatment option for patients with TNBC owing to the unavailability of acceptable t...

Machine and deep learning methods for radiomics

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has...

Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry

Summary: Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite d...

Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly supervised clustering

ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR ...

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease lands...