AIMC Topic: Humans

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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

BMC bioinformatics
BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug d...

AI analysis for ejection fraction estimation from 12-lead ECG.

Scientific reports
Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the heart ejection fraction (EF) is crucial for diagnosing and monitoring HF. Although echocardiography...

Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.

Scientific reports
A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. This study, conducted at a university hospital, included 6335 participants ...

DrugGen enhances drug discovery with large language models and reinforcement learning.

Scientific reports
Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions...

Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy.

Scientific reports
This primary research paper emphasizes cross-validation, where data samples are reshuffled in each iteration to form randomized subsets divided into n folds. This method improves model performance and achieves higher accuracy than the baseline model....

Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.

Scientific reports
The purpose of this study is to examine and interpret machine learning models that predict dry eye (DE)-related clinical signs, subjective symptoms, and clinician diagnoses by heavily weighting lifestyle factors in the predictions. Machine learning m...

AI-driven cybersecurity framework for software development based on the ANN-ISM paradigm.

Scientific reports
With the increasing reliance on software applications, cybersecurity threats have become a critical concern for developers and organizations. The answer to this vulnerability is AI systems, which help us adapt a little better, as traditional measures...

Dual-channel knowledge attention for traditional Chinese medicine syndrome differentiation.

Scientific reports
With the rapid advancement of Natural Language Processing (NLP) technologies, the application of NLP to enable intelligent syndrome differentiation in Traditional Chinese Medicine (TCM) has become a popular research focus. However, TCM texts contain ...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Scientific reports
Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of patients is crucial to optimize treatment and improve prognosis under the new global definition of AR...