The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs data.Communicated by Ramaswamy H. Sarma.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1080/07391102.2023.2242492 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Explainable machine learning framework for cataracts recognition using visual features.
Vis Comput Ind Biomed Art
January 2025
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
Cataract is the leading ocular disease of blindness and visual impairment globally. Deep neural networks (DNNs) have achieved promising cataracts recognition performance based on anterior segment optical coherence tomography (AS-OCT) images; however, they have poor explanations, limiting their clinical applications. In contrast, visual features extracted from original AS-OCT images and their transform forms (e.
View Article and Find Full Text PDFPredicting EV battery state of health using long short term degradation feature extraction and FEA TimeMixer.
Sci Rep
January 2025
Hangzhou Xiangce Electronic Technology Co.Ltd, Hangzhou, 310018, China.
Accurately predicting the State of Health (SOH) of new energy vehicle batteries is critical for ensuring their reliable operation and extending battery's service life. To address the issue of low SOH prediction accuracy across different prediction lengths, this paper proposes a prediction method based on long-short-term battery degradation feature extraction and FEA-TimeMixer model. First, a novel automatic SOH extraction algorithm for offline charging data is introduced to label the battery SOH degradation data.
View Article and Find Full Text PDFMulticlass Synthetic Accessibility Prediction.
J Chem Inf Model
January 2025
X-Chem Global HQ, 100 Beaver Street, Waltham, Massachusetts 02453, United States.
Evaluating synthetic accessibility of molecules is an integral component of the drug discovery process. While the application of machine learning models to predict whether small molecules are easy or hard to synthesize has gained attention recently, predetermined thresholds and data set imbalances present challenges for these binary classification approaches. In this study, we introduce a novel multiclass fold-ensembled classification approach to predict the minimum number of steps needed to synthesize a small molecule.
View Article and Find Full Text PDFContinuous Near-Bed Movements of Microplastics in Open Channel Flows: Statistical Analysis.
Environ Sci Technol
January 2025
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
The ubiquitous distribution of microplastics (MPs) in aquatic environments is linked to their transport in rivers and streams. However, the specific mechanism of bedload microplastic (MP) transport, notably their stochastic behaviors, remains an underexplored area. To investigate this, particle tracking velocimetry was employed to examine the continuous near-bed movements of four types of MPs under nine setups with different experimental conditions in a laboratory flume, with an emphasis on their streamwise transport.
View Article and Find Full Text PDFExploring emotional patterns in social media through NLP models to unravel mental health insights.
Healthc Technol Lett
January 2025
This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!