AIMC Topic: Machine Learning

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CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

Scientific reports
With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alteration...

Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.

Systematic reviews
BACKGROUND: We explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to (a) eliminate irrelevant records (automated simulation) and (b) complement the work o...

Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L-Regularization.

Computational intelligence and neuroscience
Though Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) via Convolutional Neural Networks (CNNs) has made huge progress toward deep learning, some key issues still remain unsolved due to the lack of sufficient samples and robust mode...

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

Computational and mathematical methods in medicine
Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality...

Machine learning for target discovery in drug development.

Current opinion in chemical biology
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursu...

An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a type of arrhythmia with high incidence. Automatic AF detection methods have been studied in previous works. However, a model cannot be used all the time without any improvement. And updating mod...

Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning.

Journal of affective disorders
BACKGROUND: Tailoring healthcare to patients' individual needs is a central goal of precision medicine. Combining smartphone-based interventions with machine learning approaches may help attaining this goal. The aim of our study was to explore the pr...

Triaging ophthalmology outpatient referrals with machine learning: A pilot study.

Clinical & experimental ophthalmology
IMPORTANCE: Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with t...

Machine learning techniques for protein function prediction.

Proteins
Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of ...

The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability.

The Journal of pharmacy and pharmacology
OBJECTIVES: The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (T ) and choice of diffusion cell on model quality and performance.