My last post explored a Genie use case built on a commercial data model in Databricks. This is the natural next step — moving from structured data into a full knowledge layer and using it to drive ...

Traditional machine learning in banking requires 3–4 months per use case: feature engineering, data labeling, model training, validation, deployment. Zero-shot inference on knowledge graphs eliminates ...

Objectives Non-alcoholic fatty liver disease (NAFLD) is a non-communicable disease with a rising prevalence worldwide and with large burden for patients and health systems. To date, the presence of ...

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and ...

Most people are familiar with data in the form of a spreadsheet, with labeled columns of different data types such as name, address, age, and so on. Databases work the same way, with each table laid ...

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and ...

ABSTRACT: With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with ...

Advances in Deep Learning Methodologies are greatly impacting the Artificial Intelligence community. With some great innovations and developments, a number of tasks are getting easier. Deep Learning ...

Different strains of the same prokaryotic species often show significant variation in gene content. Whether this variation is due to genetic drift or selection is not well understood. If the latter, ...

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their ...