Endocrinology

Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis.

High-throughput screening and computational models have been effective in predicting chemical interactions with estrogen and androgen receptors, but similar approaches for steroidogenesis remain limited. To address this gap, we developed general steroidogenesis modulation models using data from ∼1,800 chemicals screened in H295R human adrenocortical carcinoma cells. A random forest model was valid...

Classification of Diabetic Patients using a Network Representation of Their Metabolism.

Studies on Type 2 Diabetes Mellitus (T2DM) rely on specific metabolic networks to represent the intricate relationships between metabolites. Accurate classification requires analyzing network characteristics, such as distance graphs and topological similarities, and identifying features that effectively capture these aspects. This study focuses on deriving metabolic networks and applying graph emb...

Utilization of artificial intelligence in Men's Health: Opportunities for innovation and quality improvement.

Artificial intelligence (AI) is reshaping the landscape of men's health by enhancing diagnostic accuracy, personalizing treatment strategies, and impr...

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategi...

Aptamer-directed siRNA delivery systems for triple-negative breast cancer therapy.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by the absence of estrogen, progesterone, and HER2 recept...

Integrative Machine Learning Approach for Predicting Resistance to First-Generation Receptor Ligands in Acromegaly.

CONTEXT: Acromegaly, caused by excess growth hormone (GH) and insulin-like growth factor-1 (IGF-1) due to pituitary adenomas, often necessitates first...

Effect of the exposure to brominated flame retardants on hyperuricemia using interpretable machine learning algorithms based on the SHAP methodology.

BACKGROUND: Brominated flame retardants (BFRs) are classified as important endocrine disruptors and persistent organic pollutants; nevertheless, there...

Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.

UNLABELLED: The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Gluco...

Application of IRSA-BP neural network in diagnosing diabetes.

Within the healthcare sector, the application of machine learning is gaining prominence, notably enhancing the efficiency and precision of diagnostic ...

Personalized machine learning models for noninvasive hypoglycemia detection in people with type 1 diabetes using a smartwatch: Insights into feature importance during waking and sleeping times.

Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, cur...

Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction.

Precise forecasting of cancer outcomes is essential for medical professionals to assess the well-being of patients and develop customized therapeutic ...

Big data and cardiovascular risk-insights into obesity, diabetes, and coronary heart disease.

BACKGROUND: Cardiovascular diseases (CVD) remain a major global health burden. Obesity and type 2 diabetes mellitus (T2DM) are key modifiable risk fac...

Identification of hub genes involved in the pathogenesis of diabetic nephropathy: A multi-omics study integrating machine learning, mendelian randomization and mediation analysis.

BACKGROUND: Diabetic nephropathy (DN), affecting 30%-40% of diabetic patients, is the leading cause of end-stage renal disease worldwide. This study a...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence...

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existi...

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can significantly reduce quality of life. This study aimed t...

Integrating ocular and clinical features to enhance intravenous glucocorticoid response prediction in thyroid eye disease: a machine learning approach.

PURPOSES: Predicting intravenous glucocorticoid (IVGC) efficacy in thyroid eye disease (TED) is vital for personalized treatment and minimizing side e...

Machine learning-based QSAR and molecular modeling identify promising PTP1B modulators from Ocimum gratissimum for type 2 diabetes therapy.

Protein tyrosine phosphatase 1B (PTP1B) is a key negative regulator of insulin signaling and a promising therapeutic target for the treatment of type ...

Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.

BACKGROUND: Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised sign...

Association rule mining of the human gut microbiome.

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community...