Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.

The use of prior knowledge in the machine learning framework has been considered a potential tool to handle the curse of dimensionality in genetic and genomics data. Although random forest (RF) represents a flexible non-parametric approach with several advantages, it can provide poor accuracy in high-dimensional settings, mainly in scenarios with small sample sizes. We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability, exemplifying its use for selecting and identifying relevant genes.

Oral manifestations in patients with coronavirus disease 2019 (COVID-19) identified using text mining: an observational study.

Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry.

Integrative analysis of gene and protein expression in atherosclerosis-related pathways modulated by periodontal pathogens. Systematic review.

Unlabelled: The mechanisms modulated by periodontal pathogens in atherosclerosis are not fully understood. Aim: to perform an integrative analysis of gene and protein expression modulated by periodontal pathogens in cells and animal models for atherosclerosis.

Methods: Cochrane, PRISMA and AMSTAR2 guidelines for systematic reviews were followed.

Presence of periodontal pathogenic bacteria in blood of patients with coronary artery disease.

It has been hypothesised that oral bacteria can migrate, through the blood, from the mouth to the arterial plaques, thus exacerbating atherosclerosis. This study compared bacteria present in the peripheral blood of individuals with and without coronary artery disease (CAD). RNA sequences obtained from blood were downloaded from GEO (GSE58150).

Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis.

Background: Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with patients having normal valves, using a knowledge-slanted random forest (RF).

Results: This study implemented a knowledge-slanted random forest (RF) using information extracted from a protein-protein interactions network to rank genes in order to modify their selection probability to draw the candidate split-variables.