AIMC Topic: Machine Learning

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A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

Computational and mathematical methods in medicine
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, ...

Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...

The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR, or high-risk plaque features?

European radiology
OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically s...

Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to address the inverse problem of converting pixel-wise multispectral reflectan...

CiRCus: A Framework to Enable Classification of Complex High-Throughput Experiments.

Journal of proteome research
Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework to gen...

Enhancer prediction with histone modification marks using a hybrid neural network model.

Methods (San Diego, Calif.)
Enhancer is a DNA sequence of a genome that controls transcription of downstream target genes. Enhancers are known to be associated with certain epigenetic signatures. Machine learning tools, such as CSI-ANN, ChromHMM, and RFECS, were developed for p...

Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

Molecular and cellular biochemistry
This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectr...

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

European journal of radiology
PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.

Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Journal of medical systems
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification f...

Machine learning meets partner matching: Predicting the future relationship quality based on personality traits.

PloS one
To what extent is it possible to use machine learning to predict the outcome of a relationship, based on the personality of both partners? In the present study, relationship satisfaction, conflicts, and separation (intents) of 192 partners four years...