A Comparative Analysis of Machine Learning Models for Cancer Types Classification Using RNA-Seq Gene Expression Data

FastPedia-ML: An Interpretable Machine-Learning Framework for Pediatric Leukemia Subtype Classification using Gene-Expression Data

Pediatric Acute Myeloid Leukemia (pAML) is a heterogeneous disease with complicated genomic variants that make it difficult to subclassify the disease properly. The paper suggests a strong and explainable machine learning model applied to the classification of pediatric leukemia subtypes based on high-dimensional data in microarray gene expression. The framework combines ANOVA-based feature selection and variance-based filtering to minimize dimensionality, as well as adaptive SMOTE in order to deal with the imbalance of classes. The strategy of cross-validation is used in a nested way to guarantee the unbiased model evaluation and hyperparameters optimum. Three classifiers, which are Random Forest (RF), Support Vector Machine (SVM), and XGBoost are compared in the terms of weighted F1-score, MCC, and ROC-AUC. The experimental results on the GSE9476 dataset indicate that RF and SVM can be used to obtain perfect classification performance (F1-score = 1.000), whereas XGBoost can be used to obtain competitive results (F1 = 0.919). Statistical significance (p = 0.001) is proven by permutation testing. SHAP-based analysis also determines the biologically significant genes that correlate with the development of leukemia. The suggested framework has a high predictive power, robustness, and interpretability, which shows the possibility of using it in the context of precision medicine to diagnose pediatric leukemia.

OPTIMIZED DEEP NEURAL NETWORKS FOR HIGH DIMENSIONAL RNA SEQ GENE EXPRESSION ANALYSIS IN CANCER DIAGNOSIS

Cancer remains one of the leading causes of death worldwide, requiring accurate and early diagnostic techniques for effective treatment and improved patient survival. Recent advancements in Ribonucleic Acid sequencing technology have enabled the generation of large scale gene expression datasets that provide critical insights into cancer biology and molecular mechanisms. The high dimensionality of Ribonucleic Acid Sequencing data, which often contains thousands of gene features with relatively few samples, presents significant challenges such as overfitting, increased computational complexity, and reduced model performance when using traditional analytical methods. This study focused on the development of an optimized Deep Neural Network for high dimensional Ribonucleic Acid Sequencing gene expression analysis in cancer diagnosis. The approach integrates data preprocessing, feature selection, dimensionality reduction, and hyperparameter optimization to enhance classification accuracy and computational efficiency. Techniques such as Principal Component Analysis and Recursive Feature Elimination were applied to extract the most relevant gene features, while optimization strategies improved model convergence and generalization. The optimized Deep Neural Network was evaluated using standard performance metrics including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic Area Under the Curve. Experimental results demonstrate that the proposed model achieves high classification performance and outperforms conventional machine learning algorithms in identifying cancer related gene expression patterns. The findings confirmed that optimized deep learning approaches are highly effective for analyzing complex genomic data and have strong potential for improving cancer diagnosis and supporting precision medicine.

Machine Learning-Based Multi-Cancer Diagnostic System for Early Detection and Accurate Classification Across Diverse Cancer Types

Cancer is one of the major causes of death worldwide, mostly owing to late discovery and hence restricted treatment choices. Existing screening approaches are primarily invasive and often associated with complicated, long and expensive procedures. In biomedicine and bioinformatics, several research groups have examined the use of machine learning methods to solve the important challenge of categorizing cancer patients into high- and low-risk categories. These methodologies have thus been used to mimic the onset and treatment of cancer. The ability of ML algorithms to detect important characteristics in complex datasets further highlights their importance. Many of these approaches like as Decision Trees, Logistic Regression (LR), Support Vector Machines and K-Nearest Neighbours have been widely employed in cancer research to generate prediction models that aid decision makers to make better and more trustworthy decisions. ML methods are indeed able to improve our understanding of cancer formation, but need adequate validation to be regarded for application in ordinary clinical practice. Hence, an ML approach was utilized to simulate the progression of cancer. The prediction models shown here are based on several ML approaches and a broad variety of input features and Data Samples. The proposed framework incorporates data preprocessing, feature selection, and advanced classification algorithms to enhance diagnostic accuracy and facilitate timely clinical decision-making. The study emphasizes the potential of artificial intelligence in advancing precision oncology and improving healthcare outcomes.

Machine learning integration of tissue-specific metagenomic signatures for colorectal cancer diagnosis.

Colorectal cancer (CRC) represents a significant global health burden. Leveraging machine learning (ML) with metagenomic and tissue-specific data presents new opportunities for improving diagnostic accuracy and understanding the microbiome's role in CRC. This study was conducted to enhance diagnostic efficiency and identify crucial bacterial biomarkers in CRC using various ML models applied to metagenomic data. A total of 33 samples were analyzed, comprising 20 healthy controls and 13 CRC patients. Each sample included demographic data (age, gender) and bacterial information (Bacteroides, Enterococcus, Faecalibacterium, Proteobacteria, Gammaproteobacteria, Firmicutes, Enterobacteriaceae, Clostridia). Six models: Logistic Regression, Naive Bayes, Decision Tree, Support Vector Machine (SVM) with both linear and polynomial kernels and Multilayer Perceptron (MLP) were employed. Performance was evaluated using leave-one-out cross-validation (LOOCV). To address the class imbalance, F1-score was utilized as the primary metric for feature selection. A consensus-based feature elimination strategy, where bacterial features were iteratively removed only if their exclusion improved or maintained the F1-score across the majority of the models was implemented. For the MLP, a grid search was integrated into each iteration to optimize hidden layer architectures and solvers, thereby ensuring that robust performance was achieved for each feature subset. The analysis was conducted using a 10-feature initial set consisting of 2 demographic and 8 microbial features. Model performances were optimized through a consensus-based feature elimination strategy, and it was determined that diagnostic success increased with the exclusion of the Faecalibacterium, Age, and Enterobacteriaceae features during the process. The highest performance was achieved with the SVM model with Linear kernel when Bacteroides was excluded from the 9-feature subset (Table 4), reaching an accuracy of 87.88% and an F1-score of 83.33%. Within the final biomarker set, Enterococcus and Firmicutes were identified as the most critical predictive features due to the sharpest declines in F1-score observed in their absence. This study demonstrates that the systematic elimination of initial clinical and metagenomic features maximizes CRC diagnostic accuracy and model stability. The process, initiated with a 10-feature baseline set was subsequently refined to establish a high-precision diagnostic mechanism with an F1-score of 83.33%. The identified final microbial signatures, consisting of 5-6 taxa, provide a clinically applicable, non-invasive diagnostic foundation with low input requirements.

An AI-Driven Pipeline for High-Dimensional Gene Expression Analysis in Leukemia Classification (ALL vs AML)

High-dimensional gene expression datasets present major analytical challenges in biomedical research because the number of variables greatly exceeds the number of available samples. This study proposes an artificial intelligence–driven analytical pipeline for the classification of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) using microarray gene expression data from the Golub leukemia dataset. The aim of the study is to develop a robust, interpretable and leakage-free machine learning framework capable of supporting precision medicine and clinical decision-making. The dataset included 7,129 genes and 72 patient samples (47 ALL and 25 AML cases). An 80/20 train–test split was applied while preserving class proportions. Initially, variance filtering retained the 5,000 most informative genes from the training set. Differential expression analysis was then performed using the limma framework with Benjamini–Hochberg False Discovery Rate correction, identifying 734 statistically significant genes (FDR ≤ 0.05). Subsequently, supervised Principal Component Analysis was conducted on the selected genes, with the first principal component explaining approximately 41% of the total variance. The resulting components were used as inputs for ridge logistic regression with internal cross-validation. The proposed pipeline achieved excellent classification performance, with test Accuracy = 1.00, Sensitivity = 1.00, Specificity = 1.00, and AUC = 1.00, while 5-fold cross-validation produced a mean AUC of 0.983. Furthermore, permutation testing generated a mean AUC close to 0.50, confirming that the observed performance was not due to random chance or data leakage. Overall, the findings demonstrate that integrating statistical feature selection, supervised dimensionality reduction and regularized machine learning can provide highly accurate and interpretable models for leukemia classification, highlighting the growing role of artificial intelligence in precision oncology and public health informatics.

Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.