ArticleScientific reports2022
Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 24 citations in OpenAlex.
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- m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.BioData mining · 2024Article
- A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma.Genes · 2023Article
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- PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites.Frontiers in genetics · 2023Article
- Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.International journal of molecular sciences · 2022Article
- m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew's correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.
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