BACKGROUND: Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. A data-driven approach using machine learning (ML) offers advanced...
BACKGROUND: A major challenge in sentiment analysis on social media is the increasing prevalence of image-based content, which integrates text and visuals to convey nuanced messages. Traditional text-based approaches have been widely used to assess p...
PURPOSE: We developed Machine learning (ML) algorithms to predict ureteroscopy (URS) outcomes, offering insights into diagnosis and treatment planning, personalised care and improved clinical decision-making.
Colorectal liver metastasis (CRLM) is a primary factor contributing to poor prognosis and metastasis in colorectal cancer (CRC) patients. This study aims to develop and validate a machine learning (ML)-based risk prediction model using conventional c...
Journal of chemical information and modeling
May 12, 2025
Absolute stereochemical configurations and optical rotations were computed for 121,416 molecular structures from the QM9 quantum chemistry data set using density functional theory. A representation for the molecules was developed using Cartesian coor...
Journal of chemical information and modeling
May 12, 2025
Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield pr...
Journal of chemical information and modeling
May 12, 2025
Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiri...
Journal of chemical information and modeling
May 12, 2025
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effec...
Journal of chemical information and modeling
May 12, 2025
Today's drug discovery increasingly relies on computational and machine learning approaches to identify novel candidates, yet data scarcity remains a significant challenge. To address this limitation, we present , an application specifically designed...
Journal of chemical information and modeling
May 12, 2025
The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance betwee...
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