An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.

Protein-peptide interactions are fundamental to numerous cellular processes and are linked to diseases like cancer when disrupted. Understanding these interactions is critical for both functional geno...

Germline-aware deep learning models and benchmarks for predicting antibody VH-VL pairing.

Variable heavy (VH) and variable light (VL) chain pairing is a critical determinant of antibody diversity, stability, and antigen-binding specificity. Identifying productive VH - VL combinations exper...

Artificial intelligence, machine learning, telemedicine, and digital transformation in nephrology and transplantation.

Timelygpt: extrapolatable transformer pre-training for long-term time-series forecasting in healthcare.

PURPOSE: Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing and Computer Vision domains. However, the development of PTMs on...

Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation.

Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this pri...

Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent.

Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalace...

Functional biomaterials and machine learning approaches for phenotyping heterogeneous tumor cells and extracellular vesicles.

Heterogeneity in cancer is known to be a contributor to the formation of metastatic lesions, poor prognosis, and ultimately undermines therapeutic efficacy. This same tumor heterogeneity is reflected ...

Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.

BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phen...

Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.

Age-related macular degeneration (AMD) is the primary reason for severe visual impairments, making early diagnosis critically important. This paper provides a comprehensive review of the methods used ...

Deep Learning for Automated Measures of SUV and Molecular Tumor Volume in [

Metastatic castration-resistant prostate cancer has a high rate of mortality with a limited number of effective treatments after hormone therapy. Radiopharmaceutical therapy with [

An efficient deep learning-based strategy to screen inhibitors for GluN1/GluN3A receptor.

The GluN1/GluN3A receptor, a unique excitatory glycine receptor recently identified in the central nervous system, challenges traditional perspectives of N-methyl-D-aspartate (NMDA) receptor diversity...

AI-guided Cas9 engineering provides an effective strategy to enhance base editing.

Precise genome editing is crucial for functional studies and therapies. Base editors, while powerful, require optimization for efficiency. Meanwhile, emerging protein design methods and protein langua...

Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.

The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferative neoplasms (MPN). However, morphological assessments of bone marrow trephine (BMT) sections by hem...

AI in radiology: Powerful, promising… but alarmingly hackable.

Machine Learning-Based Predictive Modeling Maximizes the Efficacy of mTOR/p53 Co-Targeting Therapy Against AML.

Although mTOR signaling plays a key role in acute myeloid leukemia (AML), mTOR inhibitors have shown limited efficacy against AML in clinical trials. In this study, we found that the anti-leukemic eff...

Early detection of Alzheimer's disease: From multiplex assays and imaging to point of care devices and AI-based functional monitoring.

The advent of disease-modifying treatments and risk-reduction strategies in the clinic have increased the demand for biomarkers for early disease detection, prediction of clinical course of disease, i...

Discovery of novel GluN1/GluN3A NMDA receptor inhibitors using a deep learning-based method.

Ligand-based drug discovery methods typically utilize pharmacophore similarities among molecules to screen for potential active compounds. Among these, scaffold hopping is a widely used ligand-based l...

Identifying Metabolic Dysfunction-Associated Steatotic Liver Disease Using Natural Language Processing in a US National Cohort.

INTRODUCTION: Although metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD with increased alcohol intake (MetALD) are identified as clinical entities; tools to identify patients...

An Efficient Machine Learning Pipeline for Distinguishing Cancer and Non-Cancer Patients in Systemic Lupus Erythematosus.

BACKGROUND: The objective is to develop a machine learning model that is capable of effectively distinguishing between cancer and non-cancer patients among systemic lupus erythematosus (SLE). METHODS:...

Computational genome engineering through AI-CRISPR-precision medicine integration in modern therapeutics.

The convergence of precision medicine strategies, CRISPR gene editing technologies, and artificial intelligence (AI) is causing a revolutionary change in the pharmaceutical industry in recent times. L...