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

Showing 131 to 140 of 183 articles

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

Methods (San Diego, Calif.)
MOTIVATION: Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addit...

Radiomics in neuro-oncology: Basics, workflow, and applications.

Methods (San Diego, Calif.)
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

Methods (San Diego, Calif.)
The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for...

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Methods (San Diego, Calif.)
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extra...

CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning.

Methods (San Diego, Calif.)
The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used a...

Encoder-decoder CNN models for automatic tracking of tongue contours in real-time ultrasound data.

Methods (San Diego, Calif.)
One application of medical ultrasound imaging is to visualize and characterize human tongue shape and motion in real-time to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it...

Supervised mixture of experts models for population health.

Methods (San Diego, Calif.)
We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk fa...

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

Methods (San Diego, Calif.)
At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a correspond...

A novel graph attention adversarial network for predicting disease-related associations.

Methods (San Diego, Calif.)
Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human...