Prescriptions

A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.

Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligence (AI) offers a promising opportunity to address this challenge by enabling the design of hit-like anti-cancer molecules conditioned on complex genom...

Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.

Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional neural networks are used in radiology research for prediction but can be corrupted through adversarial attacks. This study investigates the effect of an adversarial attack, through poisoned data. To improve generalizability, we create a generic ResNet ...

Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework.

Effective student performance evaluation is essential for improving education, especially in higher and technical schools. Data mining helps solve edu...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of n...

Deep learning based knowledge tracing in intelligent tutoring systems.

The emergence of online education, e.g., intelligent tutoring system (ITS), complements or partially replaces conventional offline education, especial...

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves.

Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant di...

AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios.

Drug-induced liver injury (DILI) is a significant concern with prescription medications and supplements. Accordingly, it is crucial to develop tools a...

TGEL-transformer: Fusing educational theories with deep learning for interpretable student performance prediction.

With the integration of educational technology and artificial intelligence, personalized learning has become increasingly important. However, traditio...

Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT.

E-commerce is a vital component of the world economy, providing people with a simple and convenient method for shopping and enabling businesses to exp...

Should We Use Behavioural Predictions in Organ Allocation?

Medical predictions, for example, concerning a patient's likelihood of survival, can be used to efficiently allocate scarce resources. Predictions of ...

Utility of machine learning algorithms in classification of progressive cognitive impairment in Alzheimer's disease: A retrospective cohort based on China.

BackgroundDistinct risk factors influence Alzheimer's disease (AD) stage stratification, yet effective tools for early diagnosis and prognosis remain ...

Harnessing computational technologies to facilitate antibody-drug conjugate development.

Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known ch...

Artificial intelligence chatbots as a source of virtual social support: Implications for loneliness and anxiety management.

Loneliness, social isolation, and anxiety affect millions of people across the world. Communication technologies, including artificial intelligence (A...

In silico & in vitro approaches suggest osteoclastogenesis induction underlying fractures in Entrectinib-treated children.

Entrectinib, a potent, CNS-active, TRK and ROS1 inhibitor is associated with occurrence of bone fractures, particularly in pediatric patients. We emba...

Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.

While static risk models may identify key driving risk factors, the dynamic nature of risk requires up-to-date risk information to guide treatment dec...

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 ...

Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.

OBJECTIVES: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescri...

A Comprehensive Framework for the Prediction of Intra-Operative Hypotension.

In this paper, the problem of triggering early warning for intra-operative hypotension (IOH) is addressed. Recent studies on the Hypotension Predictio...

RNA-ligand interaction scoring via data perturbation and augmentation modeling.

Despite recent advances in RNA-targeting drug discovery, the development of data-driven deep learning models remains challenging owing to limited vali...

Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.

We introduce an artificial intelligence model to personalize treatment in major depression, which was deployed in the Artificial Intelligence in Depre...