AIMC Topic: Machine Learning

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A machine learning model and molecular clusters of epigenetic chromatin regulators in tuberculosis based on bioinformatics and clinical samples.

Scientific reports
The role of chromatin regulators (CRs) in mediating epigenetic changes during tuberculosis (TB) infection remains poorly understood. This study aimed to determine the efficacy of CRs in diagnosing TB and characterizing its heterogeneity. GSE83456 dat...

Machine learning driven multiomics analysis identifies disulfidptosis associated molecular subtypes in ovarian cancer.

Scientific reports
Precision oncology enables molecularly guided cancer therapy through multi-omics profiling, AI-driven classification, and biomarker-targeted interventions. Disulfidptosis has emerged as a promising therapeutic target, yet no ovarian cancer classifica...

Identification of novel biomarkers for epithelial ovarian cancer through machine learning and explainable artificial intelligence using in silico and in vitro analysis.

Scientific reports
Epithelial ovarian cancer (EOC) is a lethal gynecological malignancy. Ongoing research aimed to identify novel biomarkers and develop combined algorithms to improve diagnosis and prognosis prediction for EOC. RNA-seq related to EOC were obtained from...

Design and analysis of a GaN-based 2D photonic crystal biosensor integrated with machine learning techniques for detection of skin diseases.

Scientific reports
Photonic crystals are prevalent in the detection of assorted diseases and malignancies such as vitiligo and cutis laxa. A 2D photonic crystal utilizing GaN is demonstrated to detect skin diseases, highlighting its substantial relevance to the photoni...

Development and validation of a machine learning model to predict early recurrence after surgery in NSCLC patients.

Scientific reports
To develop and validate a machine learning (ML) model for predicting early recurrence (ER) within two years post-surgery in non-small cell lung cancer (NSCLC) patients. This multicenter cohort study included 3,171 NSCLC patients who underwent radical...

Hypertension Medication Recommendation via Synergistic and Selective Modeling of Heterogeneous Medical Entities: Development and Evaluation Study of a New Model.

JMIR medical informatics
BACKGROUND: Electronic health records (EHRs) contain comprehensive information regarding diagnoses, clinical procedures, and prescribed medications. This makes them a valuable resource for developing automated hypertension medication recommendation s...

A nomogram for predicting renal function recovery after robotic-assisted ureteral reconstruction: development and comparative validation using traditional and machine learning models.

Journal of robotic surgery
OBJECTIVE: To develop, validate, and compare a Traditional Multivariable Logistic Regression model with a Machine Learning-based LASSO Regression Model for predicting significant renal function recovery in adult patients undergoing surgical repair fo...

pKa prediction for small molecules: an overview of experimental, quantum, and machine learning-based approaches.

Journal of computer-aided molecular design
The pKa, also known as the logarithmic dissociation constant, is a crucial parameter that defines the ionization level of a molecule when it is in solution. It is essential for several physicochemical properties, including lipophilicity, solubility, ...

Breast cancer diagnosis from histopathological images and molecular signatures by fusing features with an explainable AI-based residual tabular network model.

Journal of computer-aided molecular design
Early Breast Cancer (BC) Diagnosis has the potential to cut BC death rates in the long term drastically. Identifying early-stage cancer cells is the most crucial step in determining the best prognosis. Despite recent advances in the use of AI-based m...