AIMC Topic: Algorithms

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A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

Investigative ophthalmology & visual science
PURPOSE: To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH).

Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data.

IEEE journal of biomedical and health informatics
Diet and physical activity are known as important lifestyle factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used to measure physical activity or detect eating time. In many intervent...

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Neuroinformatics
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not inc...

SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning.

Proteomics
SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples, is presented here. SIMLR can be...

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug d...

Postoperative neonatal mortality prediction using superlearning.

The Journal of surgical research
BACKGROUND: The variable risks associated with neonatal surgery present a challenge to accurate mortality prediction. We aimed to apply superlearning, an ensemble machine learning method, to the prediction of 30-day neonatal postoperative mortality.

Machine learning and deep analytics for biocomputing: call for better explainability.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...

Annotating gene sets by mining large literature collections with protein networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining o...