Our Publications

Artificial Intelligence in Autism Spectrum Disorder Diagnosis: A Scoping Review of Face, Voice, and Text Analysis Methods

Background

Autism is a complex neurodevelopmental condition affecting social interaction and behavior. Traditional diagnostic methods, relying on observational techniques and interviews conducted by trained professionals, remain the gold standard for ASD diagnosis. However, these methods can be time-consuming and may be influenced by subjective factors. Recent advancements in artificial intelligence (AI) offer promising approaches to augment existing methods, potentially enhancing efficiency and providing additional objective data through facial, vocal, and textual analysis.

Objective

The objective of this study was to conduct a comprehensive review of artificial intelligence applications in autism spectrum disorder (ASD) diagnosis, specifically focusing on facial, vocal, and textual analysis methods.

Methods

A comprehensive search was conducted in PubMed, Web of Science, Scopus, and Google Scholar. The findings were reported in accordance with the PRISMA checklist. Data were collated and summarized, and results were reported qualitatively, adopting a narrative synthesis approach.

Results

In facial image analysis, deep learning algorithms demonstrated high accuracy in identifying autism-related …

X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for CHD Diagnosis

Coronary Heart Disease (CHD) is becoming a leading cause of death worldwide. To assess coronary artery narrowing or stenosis, doctors use coronary angiography, which is considered the gold-standard method. Interventional cardiologists rely on angiography to decide on the best course of treatment for CHD, such as revascularization with bypass surgery, coronary stents, or medication. However, angiography has some issues, including operator bias, inter-observer variability, and poor reproducibility. The automated interpretation of coronary angiography is yet to be developed, and these tasks can only be performed by highly specialized physicians. Developing automated angiogram interpretation and coronary artery stenosis estimation using Artificial Intelligence (AI) approaches requires a large dataset of X-ray angiography images that include clinical information. We have collected 231 X-ray images of heart vessels, along with the necessary angiographic variables, including the SYNTAX score, to support the advancement of research on CHD-related machine learning and data mining algorithms. We hope that this dataset will ultimately contribute to advances in clinical diagnosis of CHD.

A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches

Rice fields are a major producer of methane, a strong greenhouse gas. However, identifying genetic variation in methane emissions among rice varieties remains challenging. This study applied association rule mining to detect key rice root morphological and anatomical traits influencing methane emissions, validated using a support vector machine. We report models which accurately classified high and low methane-emitting varieties with 98% (morphological) and 94% (anatomical) accuracy. These models effectively distinguished methane emission categories based on intrinsic trait patterns. Machine learning analysis highlighted the top 10 morphological and anatomical traits associated with methane emission levels. High methane-emitting varieties were characterized by lower middle root porosity, base root porosity, average root porosity, root diameter (RDia), and higher S-type lateral root length. Conversely, low methane-emitting varieties exhibited lower root number, tiller number, root dry weight, leaf number, and higher RDia. Anatomically, high methane-emitting varieties showed reduced lacunae number, total stele area, mean metaxylem size, metaxylem number, and metaxylem vessel area. Low methane-emitting varieties, in contrast, had higher percent aerenchyma, total stele area, ratio of total cortical area to root cross-section area, ratio of stele to root cross-section area, and aerenchyma area. The results suggest that the rhizosphere oxygenation …

Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data

Objective

Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.

Methods

Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.

Results

In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP al…

An update on glycerophosphodiester phosphodiesterases; from bacteria to human

The hydrolysis of deacylated glycerophospholipids into sn-glycerol 3-phosphate and alcohol is facilitated by evolutionarily conserved proteins known as glycerophosphodiester phosphodiesterases (GDPDs). These proteins are crucial for the pathogenicity of bacteria and for bioremediation processes aimed at degrading organophosphorus esters that pose a hazard to both humans and the environment. Additionally, GDPDs are enzymes that respond to multiple nutrients and could potentially serve as candidate genes for addressing deficiencies in zinc, iron, potassium, and especially phosphate in important plants like rice. In mammals, glycerophosphodiesterases (GDEs) play a role in regulating osmolytes, facilitating the biosynthesis of anandamine, contributing to the development of skeletal muscle, promoting the differentiation of neurons and osteoblasts, and influencing pathological states. Due to their capacity to enhance a plant's ability to tolerate various nutrient deficiencies and their potential as pharmaceutical targets in humans, GDPDs have received increased attention in recent times. This review provides an overview of the functions of GDPD families as vital and resilient enzymes that regulate various pathways in bacteria, plants, and humans.