Prescriptions

Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.

Falls among the elderly and especially those with NeuroDegenerative Disorders (NDD) reduces life expectancy. The purpose of this study is to explore the role of Machine Learning on Electronic Health Records (EHR) data for time-to-event survival analysis prediction of injuries, and role of sensitive attributes, e.g., Race, Ethnicity, Sex, in these models. We used multiple survival analysis methods ...

Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.

Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods have been developed to predict drug solubility based on chemical structures, no reliable tools currently exist for predicting the dissolution rate cons...

MTGNN: A Drug-Target-Disease Triplet Association Prediction Model Based on Multimodal Heterogeneous Graph Neural Networks and Direction-Aware Metapaths.

The forecasting of drug-target interactions (DTIs) is a crucial element in the domain of drug repositioning. Current methodologies, primarily based on...

Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro.

The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived c...

First impressions of a humanoid social robot with natural language capabilities.

Concurrent developments in robotic design and natural language processing (NLP) have enabled the production of humanoid chatbots that can operate in c...

Unveiling the Mechanism of Negative Poisson's Ratio in Phosphorus-like 2D MX Materials Driven by Geometric and Electronic Structures.

Two-dimensional black-phosphorus-like materials with a re-entrant structure have been reported exhibiting positive or negative Poisson's ratio (NPR). ...

HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.

BACKGROUND: Accurate identification of drug-drug interactions (DDIs) is critical in pharmacology, as DDIs can either enhance therapeutic efficacy or t...

Formative evaluation of the acceptance of HIV prevention Artificial Intelligence chatbots by Black gay, bisexual, and other men who have sex with men in the Southern United States: Focus group study.

Gay, bisexual, and other men who have sex with men (MSM) account for 60% of new HIV infections among Black Americans in the Southern United States (U....

Predicting drug-target interactions using machine learning with improved data balancing and feature engineering.

Drug-Target Interaction (DTI) prediction is a vital task in drug discovery, yet it faces significant challenges such as data imbalance and the complex...

Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique.

This study develops and evaluates advanced hybrid machine learning models-ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Re...

Investigating the effect of transformer encoder architecture to improve the reliability of classroom observation ratings on high-inference discourse.

This study investigates the effect of transformer encoder architecture on the classification accuracy of high-inference discourse elements in classroo...

Identifying a gene signature for age-related hearing loss through machine learning and revealing the effect of the CTSS on the mice cochlea.

Age-related hearing loss (ARHL) is one of the most common health conditions among the elderly population. This study used machine learning to screen f...

Evaluating the Impact of AI-Based Model-Informed Drug Development (MIDD): A Comparative Review.

Model-informed drug development (MIDD) methods play critical role to ensure development of efficacious, and safe individualized therapies. The applica...

Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation.

Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional me...

Personalized medication recommendations for Parkinson's disease patients using gated recurrent units and SHAP interpretability.

Managing Parkinson's disease (PD) through medication can be challenging due to varying symptoms and disease duration. This study aims to demonstrate t...

Integrative strategies in drug discovery: Harnessing genomics, deep learning, and computer-aided drug design.

The development of novel drugs increasingly relies on advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. ...

Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.

Drug combination therapy is promising for cancer treatment by reducing resistance and improving efficacy. Machine learning approaches to predicting dr...

Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism.

Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interac...

Menopausal hormone therapy and the female brain: Leveraging neuroimaging and prescription registry data from the UK Biobank cohort.

BACKGROUND: Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a compre...

Toward Faithful Neural Network Intrinsic Interpretation With Shapley Additive Self-Attribution.

Self-interpreting neural networks have attracted significant attention from the research community. Along this line, extensive works inherently share ...