AIMC Topic: Algorithms

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VAE-GANMDA: A microbe-drug association prediction model integrating variational autoencoders and generative adversarial networks.

Artificial intelligence in medicine
Traditional biological experimental methods typically require weeks or even months of experimentation, and the cost of each experiment can reach hundreds or even thousands of dollars, which is quite expensive and time-consuming. To address this, a mo...

A novel Swin transformer based framework for speech recognition for dysarthria.

Scientific reports
Dysarthria frequently occurs in individuals with disorders such as stroke, Parkinson's disease, cerebral palsy, and other neurological disorders. Well-timed detection and management of dysarthria in these patients is imperative for efficiently handli...

Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.

Biomedical physics & engineering express
Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progression subtypes (particularly between adenocarcinomaand invasive adenocarcinoma) on frozen sections....

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a decision tree. We tested if the suggested components of the actual classification (metabolic dysfun...

Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.

Anais da Academia Brasileira de Ciencias
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...

Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.

European radiology experimental
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.

Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly improves survival rates and reduces lethality. Innovative technologies are being developed for early de...

MMSol: Predicting Protein Solubility with an Antinoise Multimodal Deep Model.

Journal of chemical information and modeling
Protein solubility plays a critical role in determining its biological function, such as enabling proper protein delivery and ensuring that proteins remain soluble during cellular processes or therapeutic applications. Accurate prediction of protein ...

Annotated dataset of simulated voiding sound for urine flow estimation.

Scientific data
Sound-based uroflowmetry is a non-invasive test emerging as an alternative to standard uroflowmetry, estimating voiding characteristics from the sound generated by urine striking water in a toilet bowl. The lack of labeled flow sound datasets limits ...

Dataset resulting from the user study on comprehensibility of explainable AI algorithms.

Scientific data
This paper introduces a dataset that is the result of a user study on the comprehensibility of explainable artificial intelligence (XAI) algorithms. The study participants were recruited from 149 candidates to form three groups representing experts i...