ArticleScientific reports2021
Evaluating machine learning methodologies for identification of cancer driver genes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 74 citations in OpenAlex.
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- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation.BMC medical informatics and decision making · 2024Article
- TOP1 and R-loops facilitate transcriptional DSBs at hypertranscribed cancer driver genes.iScience · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.BioData mining · 2024Article
- An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma.Scientific reports · 2023Article
- BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling.Journal of cheminformatics · 2023Article
- Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors.Current oncology (Toronto, Ont.) · 2023Article
- Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.Diagnostics (Basel, Switzerland) · 2023Article
- EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.Genes · 2023Article
- DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.Computational and structural biotechnology journal · 2023Article
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.International journal of molecular sciences · 2022Article
- Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma.Scientific reports · 2022Article
- EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.Nucleic acids research · 2022Article
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew's correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.