machine learning

The latest group of winning technologies has a little something for everyone—from scientists at the lab bench to those in the clinic and even the classroom.

In this webinar, Linghua Wang and Jeremy Goecks will talk about technology that enables new approaches for a better understanding of tumors on a cellular, spatial, and environmental level.

Researchers used artificial intelligence in large genomics studies to fill in gaps in patient information and improve predictions, but new research uncovers false positives and misleading correlations.

AI-driven technologies can help scientists accomplish efficient research workflows, from experimental design to data management and beyond.

An automated bioprinting and imaging platform allows researchers to examine heterogeneous responses to anticancer drugs within a tumor organoid population.

Researchers tackle the antibiotic resistance crisis with explainable neural networks and high throughput drug discovery.

Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

New technologies and archival tissue biopsy samples enable exploration of changes in the bone marrow as people age.

Properly optimizing data enhances AI training for accelerating novel biopharmaceutical discoveries. 

Researchers honed prediction models and datasets to unlock the potential of deep neural networks in biological applications while avoiding bias.

Good data management is the first step toward improving scientific outcomes with artificial intelligence and machine learning.

Researchers created a model that uses clinical testing data to locate the primary site of cancer cells with no known origin, likely improving survival.

Researchers integrate scRNA-seq, spatial transcriptomics, and histology imaging data to show that spatial cellular architecture predicts glioblastoma prognosis.

A new machine learning model promises fast prediction of drug-target interactions.

New advancements in automation, AI, connectivity, sustainability, and more propel research forward.

Scientists and clinicians increasingly use real world data (RWD) to make valuable discoveries that can be applied to the healthcare industry.

Machine learning can be harnessed to synthesize artificial light-bearing enzymes that actually work in cells.

The signature could one day be used to improve treatment planning for people with substance abuse disorders.

Discover how machine learning helps scientists accelerate their research.