Prescriptions

Classification of 3D shoe prints using the PointNet architecture: proof of concept investigation of binary classification of nike and adidas outsoles.

Shoe prints are one of the most common types of evidence found at crime scenes, second only to fingerprints. However, studies involving modern approaches such as machine learning and deep learning for the detection and analysis of shoe prints are quite limited in this field. With advancements in technology, positive results have recently emerged for the detection of 2D shoe prints. However, few st...

Learning clustering-friendly representations via partial information discrimination and cross-level interaction.

Despite significant advances in the deep clustering research, there remain three critical limitations to most of the existing approaches. First, they often derive the clustering result by associating some distribution-based loss to specific network layers, neglecting the potential benefits of leveraging the contrastive sample-wise relationships. Second, they frequently focus on representation lear...

Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.

This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine ...

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive tre...

Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques.

The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of ...

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often resu...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review a...

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects.

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their sign...

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essenti...

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction.

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entit...

Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.

UNLABELLED: Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. Th...

Artificial intelligence in nanotechnology for treatment of diseases.

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and...

Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature.

Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the primary cause of acute liver failure. Identifying th...

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inh...

Detecting the interaction between microparticles and biomass in biological wastewater treatment process with Deep Learning method.

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level m...

Leveraging artificial intelligence for better translation of fibre-based pharmaceutical systems into real-world benefits.

The increasing prominence of biologics in the pharmaceutical market requires more advanced delivery systems to deliver these delicate and complex drug...

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

BACKGROUND: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing dru...

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.

BACKGROUND: The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interactio...

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with...

iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has ...