AIMC Topic: Databases, Factual

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Deep neural network for food image classification and nutrient identification: A systematic review.

Reviews in endocrine & metabolic disorders
Technology impacts human life in both the aspects such as positive and negative, which helps in better communication and eliminating geographical boundaries. However, social media and mobile devices may lead to severe health conditions such as sleep ...

Automated assembly of molecular mechanisms at scale from text mining and curated databases.

Molecular systems biology
The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protei...

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Ecotoxicology and environmental safety
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic R...

ChatGPT: Is this version good for healthcare and research?

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: There have been advancements in artificial intelligence (AI) and deep learning in the past decade. Recently, OpenAI Inc. has launched a new chatbot, called ChatGPT that interacts in a conversational way and its dialogue format ma...

Content and quality of physical activity ontologies: a systematic review.

The international journal of behavioral nutrition and physical activity
INTRODUCTION: Ontologies are a formal way to represent knowledge in a particular field and have the potential to transform the field of health promotion and digital interventions. However, few researchers in physical activity (PA) are familiar with o...

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.

GigaScience
BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research...

Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication.

Neural networks : the official journal of the International Neural Network Society
BACKGROUND AND OBJECTIVE: Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post...

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.

Journal of biomedical semantics
BACKGROUND: Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many differ...

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Sensors (Basel, Switzerland)
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical ...