AI Medical Compendium Journal:
Methods (San Diego, Calif.)

Showing 121 to 130 of 183 articles

Prediction and interpretation of cancer survival using graph convolution neural networks.

Methods (San Diego, Calif.)
The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the ...

Potentials and caveats of AI in hybrid imaging.

Methods (San Diego, Calif.)
State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional in...

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.

Methods (San Diego, Calif.)
Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as ...

Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications.

Methods (San Diego, Calif.)
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression b...

Deep learning of brain magnetic resonance images: A brief review.

Methods (San Diego, Calif.)
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of c...

Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.

Methods (San Diego, Calif.)
Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Methods (San Diego, Calif.)
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment plan...

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.)
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...

GCN-BMP: Investigating graph representation learning for DDI prediction task.

Methods (San Diego, Calif.)
One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict...

Artificial intelligence and radiomics in pediatric molecular imaging.

Methods (San Diego, Calif.)
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving fie...