Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (DOME) recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for...
OBJECTIVES: In this article, we provide a database of nonproliferative diabetes retinopathy, which focuses on early diabetes retinopathy with hard exudation, and further explore its clinical application in disease recognition.
BACKGROUND: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpo...
Combinatorial chemistry & high throughput screening
Jan 1, 2024
BACKGROUND: Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and renal failure. It is reported to be the fourth leading disease that leads to death worldwide.
Database : the journal of biological databases and curation
Dec 27, 2023
De novo molecular generation is a promising approach to drug discovery, building novel molecules from the scratch that can bind the target proteins specifically. With the increasing availability of machine learning algorithms and computational power,...
The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of exp...
INTRODUCTION: Amid clinicians' challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study...
With recent advances in computer vision, many applications based on artificial intelligence have been developed to facilitate the diagnosis of rare genetic disorders through the analysis of patients' two-dimensional frontal images. Some of these have...
Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integra...
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