Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation.

Background: Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases.

Results: We collect epigenetic and gene expression datasets from seven early time points during neural differentiation.

: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases.

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation.

Background: Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of regulatory programs this variation affects can shed light on the apparatuses of human diseases.

Results: We collected epigenetic and gene expression datasets from seven early time points during neural differentiation.

GWAS advancements to investigate disease associations and biological mechanisms.

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients.

Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches.

Objectives: Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, and craniofacial diseases (DOCs), especially periodontitis, can increase an individual's risk in developing complex traits including cardiovascular diseases (CVDs). In this study, we are focused on systematically investigating causality between periodontitis with CVDs with the application of artificial intelligence (AI), machine learning (ML) algorithms, and state-of-the-art bioinformatics approaches using RNA-seq-driven gene expression data of CVD patients.

IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.

Summary: In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine.

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical.

Changes in the biaxial viscoelastic response of the urinary bladder following spinal cord injury.

In order to gain a deeper understanding of bladder function, it is necessary to study the time-dependent response of the bladder wall. The present study evaluated and compared the viscoelastic behaviors of normal and spinal cord injured (SCI) rat bladder wall tissue using an established rat model and planar biaxial stress relaxation tests. Bladders from normal and spinalized (3 weeks) rats were subjected to biaxial stress (either 25 or 100 kPa in each loading direction) rapidly (in 50 ms) and subsequently allowed to relax at the constant stretch levels in modified Kreb's solution (in the absence of calcium; with no smooth muscle tone) for 10,000 s.

Intravesical capsaicin for the treatment of interstitial cystitis: a pilot study.

OBJECTIVES: To study the safety and efficacy of intravesically administered capsaicin, a C-fiber afferent neurotoxin, in patients with interstitial cystitis (IC). METHODS: A pilot study of intravesical capsaicin therapy was performed on 5 female patients diagnosed with IC using NIDDK criteria. Patients were evaluated with cystoscopy and CMG on initial presentation.