AIMC Topic: Algorithms

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ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from ther...

SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...

Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natura...

Potential for Process Improvement of Clinical Flow Cytometry by Incorporating Real-Time Automated Screening of Data to Expedite Addition of Antibody Panels.

American journal of clinical pathology
OBJECTIVES: We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.

A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Translational vision science & technology
PURPOSE: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.

Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions.

Texas Heart Institute journal
Artificial intelligence and machine learning are rapidly gaining popularity in every aspect of our daily lives, and cardiovascular medicine is no exception. Here, we provide physicians with an overview of the past, present, and future of artificial i...

Artificial Intelligence in Echocardiography.

Texas Heart Institute journal
Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex ...

Data-driven technique for disruption prediction in GOLEM tokamak using stacked ensembles with active learning.

The Review of scientific instruments
In a tokamak, disruption is defined as losing control over a confined plasma resulting in sudden extinction of the plasma current. Machine learning offers potent solutions to classify plasma discharges into disruptive and non-disruptive classes. Evol...

Neural network-based adaptive synchronization for second-order nonlinear multiagent systems with unknown disturbance.

Chaos (Woodbury, N.Y.)
This paper handles the distributed adaptive synchronization problem for a class of unknown second-order nonlinear multiagent systems subject to external disturbance. It is supposed to be an unknown one for the underlying external disorder. First, the...