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Abstract Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable...

Abstract Objectives To develop and evaluate a deep learning model for diagnosing untreated rheumatoid arthritis (RA) using digital camera images of bilateral dorsal hands, benchmarking its performance against the widely-...

Abstract Background This study aims to explore spatial heterogeneity within tumors, establish and validate an interpretable machine learning model combining habitat radiomics and deep learning, and investigate its predic...

Abstract Introduction This study aimed to develop a deep learning-based system to automatically assess patient positioning compliance in chest radiographs, focusing on cervical spine visibility, scapular overlap, and cos...

Abstract Background Leishmaniasis is a vector-borne parasitic disease caused by Leishmania protozoa. The disease manifests in several clinical presentations including cutaneous, mucocutaneous, and visceral leishmaniasis....

Abstract Background N6-methyladenosine ( $$m^{6}A$$ ) plays a crucial role in enriching RNA functional and genetic information. While deep learning has advanced $$m^{6}A$$ site identification, current state-of-the-art me...

Abstract Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous featu...

Abstract Background Knee osteoarthritis (OA) is a leading global cause of disability, yet conventional tools lack sensitivity for early detection and precise prognostication. Artificial intelligence (AI) and machine lear...