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Prescriptions

Latest AI and machine learning research in prescriptions for healthcare professionals.

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Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer.

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. ...

An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.

Finger dexterity, and finger individuation in particular, is crucial for human movement, and disrupt...

Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.

Age and aging are important predictors of health status, disease progression, drug kinetics, and eff...

Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and...

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment ...

SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer.

Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlight...

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms...

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting t...

Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

In the drug discovery process, accurate prediction of drug-target interactions is crucial to acceler...

LGS-PPIS: A Local-Global Structural Information Aggregation Framework for Predicting Protein-Protein Interaction Sites.

Exploring protein-protein interaction sites (PPIS) is of significance to elucidating the intrinsic m...

Leveraging machine learning to streamline the development of liposomal drug delivery systems.

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. L...

Graph-based machine learning model for weight prediction in protein-protein networks.

Proteins interact with each other in complex ways to perform significant biological functions. These...

Anchoring temporal convolutional networks for epileptic seizure prediction.

. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigat...

Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling.

The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-...

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

Structure-based machine learning algorithms have been utilized to predict the properties of protein-...

Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images.

BACKGROUND: The Food and Drug Administration mandates patient labeling materials like the Medication...

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