Classification of Lung Adenocarcinoma Using Convolutional Neural Networks: A Bioinformatics Approach
Bibliographic record
Abstract
Lung cancer, recognized as one of the most lethal malignancies globally, manifests predominantly as lung adenocarcinoma (LUAD) within the broader classification of nonsmall cell lung cancer (NSCLC).The imperative for accurate and prompt diagnosis to facilitate efficacious treatment underscores the significance of advancements in diagnostic methodologies.This study introduces a convolutional neural network (CNN) framework tailored for the interpretation of bioinformatics datasets, specifically focusing on the classification of lung adenocarcinoma.Emphasizing the integration of gene-based biomarker informatics, this approach endeavors to mitigate hierarchical discrepancies inherent within similarity indices encountered during dataset processing.Through the utilization of three gene expression datasets-GSE118370, GSE85841, and GSE32863sourced from the Gene Expression Omnibus (GEO), key features indicative of lung adenocarcinoma were meticulously analyzed.This methodology not only facilitates the precise categorization of data samples into lung adenocarcinoma but also enhances the reliability of the findings.The implementation of this CNN framework on the specified datasets yielded a classification accuracy of 93.32% and a precision of 94.56%, thereby surpassing the performance metrics of existing techniques.This research underscores the potential of integrating CNNs with bioinformatics for the refined classification of lung adenocarcinoma, heralding a significant step forward in the precise identification of this prevalent form of lung cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".