AIMC Topic: Algorithms

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A New ECG Denoising Framework Using Generative Adversarial Network.

IEEE/ACM transactions on computational biology and bioinformatics
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks...

A Novel Drug Repositioning Approach Based on Collaborative Metric Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that rec...

An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data.

IEEE/ACM transactions on computational biology and bioinformatics
Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model)...

On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn't.

Genes
In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transfor...

Prediction of PCR amplification from primer and template sequences using recurrent neural network.

Scientific reports
We have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network...

Successful incorporation of single reviewer assessments during systematic review screening: development and validation of sensitivity and work-saved of an algorithm that considers exclusion criteria and count.

Systematic reviews
BACKGROUND: Accepted systematic review (SR) methodology requires citation screening by two reviewers to maximise retrieval of eligible studies. We hypothesized that records could be excluded by a single reviewer without loss of sensitivity in two con...

Machine learning-based investigation of the cancer protein secretory pathway.

PLoS computational biology
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is ofte...

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning.

Journal of biophotonics
Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parame...

Basic of machine learning and deep learning in imaging for medical physicists.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The manuscript aims at providing an overview of the published algorithms/automation tool for artificial intelligence applied to imaging for Healthcare. A PubMed search was performed using the query string to identify the proposed approaches (algorith...

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage.

Journal of the American College of Radiology : JACR
OBJECTIVE: To determine the institutional diagnostic accuracy of an artificial intelligence (AI) decision support systems (DSS), Aidoc, in diagnosing intracranial hemorrhage (ICH) on noncontrast head CTs and to assess the potential generalizability o...