Simple Summary The ongoing advancements in deep learning, notably its use in predicting
cancer survival through genomic data analysis, calls for an up-to-date review. This paper …

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on
morphological information from histology slides and molecular profiles from genomic data …

In recent years, due to the significant development of instrumental techniques, clinical
research has acquired many interesting tools and analytical platforms that can support the …

This study utilizes a rich UK data set of smart demand metering data, household
characteristics, and weather data to develop a demand forecasting methodology that …

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide
images (WSIs) are a critical challenge in computational pathology that requires modeling …

Diverse, high-dimensional modalities collected in large cohorts present new opportunities
for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi …

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research
through new terminology for discussing the often many possible approaches to the analysis …

Breast cancer subtypes Luminal A and Luminal B are classified by the expression of PAM50
genes and may benefit from different treatment strategies. Machine learning models based …

Board-certified anatomic pathologists spend years developing domain-specific visual skills
and heuristics, which allow them to efficiently (both temporally and mentally) detect and …

Scientific studies increasingly collect multiple modalities of data to investigate a
phenomenon from several perspectives. In integrative data analysis it is important to …