Breast cancer is a heterogeneous disease whose molecular complexity complicates therapeutic stratification. Although PAM50 is a widely used molecular subtyping framework, discrete subtype assignments may underrepresent transitional molecular states and within-subtype heterogeneity relevant to treatment. This study develops a biologically interpretable representation learning framework that projects bulk RNA-seq profiles into nonlinear latent spaces and applies unsupervised hierarchical clustering, cross-validated against k-means and Gaussian mixture models (GMMs), to investigate additional tumor structure. The pipeline integrates a variational autoencoder (VAE), t-SNE/UMAP visualization, and functional (differential expression, pathway enrichment) and immune (ESTIMATE, quanTIseq) characterization of the resulting clusters. From a source pool of 19,131 transcriptomic profiles, a primary analytical cohort of 8744 unique, non-duplicated, PAM50-labeled tumor and normal samples was used for VAE training and all downstream analyses, without class balancing or oversampling. Across this primary cohort, tissue of origin and malignant-versus-normal status—not PAM50 subtype—consistently dominated clustering structure across every representation (PCA−40; $\mathrm{VAE}Z=10,20,40$) and every clustering algorithm tested, a pattern reproduced even in the raw gene expression space. Isolating the resulting 835-sample malignant subpopulation and reclustering it in isolation revealed subtype-specific structure obscured at the whole-cohort level: Basal-like disease separated cleanly under every representation, algorithm, and gene-panel size tested (83–98% purity); Luminal A resolved into a distinct cluster plus a continuum bridging Luminal B; HER2-enriched concentrated but only inconsistently achieved cluster-majority status; and Luminal B never achieved outright cluster majority under any configuration tested, behaving as a shared boundary population rather than an independently separable subtype. Among malignant-subset representations, $\mathrm{VAE}Z=10$ achieved the strongest internal validation metrics, the highest concordance with PAM50 ($\mathrm{Adjusted}\mathrm{Rand}\mathrm{Index}=0.377$), and—together with $\mathrm{VAE}Z=40$—a concordance index exceeding the PAM50-only baseline ($0.619$ and $0.643$ versus $0.606$). Immune deconvolution showed that immune-hot and immune-cold status cut across, rather than align with, PAM50 boundaries, including a regulatory T-cell/M1-macrophage-skewed HER2-enriched/Luminal B cluster and a CD8⁺-poor, B-cell-skewed Basal-like cluster. Bootstrap resampling and gene-panel-size sensitivity analyses confirmed that cluster stability tracked PAM50 purity, with Basal-like separation robust across all conditions and the HER2/Luminal B boundary consistently the least stable. These internally derived findings indicate that nonlinear representation learning can recover biologically coherent structure that refines, rather than replaces, PAM50 classification, though independent-cohort and spatial or single-cell validation are required before clinical interpretation.

Breast cancer is a heterogeneous disease whose molecular complexity complicates therapeutic stratification. Although PAM50 is a widely used molecular subtyping framework, discrete subtype assignments may underrepresent transitional molecular states and within-subtype heterogeneity. We therefore developed a biologically interpretable representation-learning framework that projects bulk RNA-seq profiles into nonlinear latent spaces and applies unsupervised clustering to investigate additional tumor structure. The pipeline integrates a variational autoencoder (VAE), t-SNE and UMAP visualization, agglomerative hierarchical clustering, and functional and immune characterization of the resulting clusters. The source dataset contained 19,131 transcriptomic profiles. An analytical cohort of 8,749 unique PAM50-labeled tumor and normal samples was used for downstream characterization, and label-informed oversampling generated 54,544 training records for VAE optimization. The VAE objective and clustering remained unsupervised, but the sampling scheme used known labels to balance representation during training. Three latent dimensions (Z = 10, 20, and 40) were benchmarked against a linear PCA baseline. Across internal analyses, the Z = 20 representation achieved the highest agreement with the reference labels (ARI = 0.5543) and identified seven clusters not resolved as clearly by PCA. These included a cluster spanning HER2-enriched and Luminal B samples, a highly proliferative cluster, and a B-cell/Treg-enriched group with a maximum inferred regulatory T-cell fraction of 0.711. The Z = 10 configuration yielded the highest prognostic discrimination among the tested representations (C-index = 0.553 versus 0.467 for the PAM50-based comparator), corresponding to an absolute difference of 0.086 and a relative increase of 18.4%. These internally derived findings suggest that nonlinear representations may capture complementary biological and prognostic structure, but independent-cohort and spatial or single-cell validation are required before clinical interpretation.

This research aims at exploring the evolutionary path of selected five grass species to understand how they evolve through time from a computer science perspective, by designing a new algorithm that employs raw data sequences to extract the information necessary for explaining the hypothesis that claims crossing over regions on genomes are responsible of pushing the evolutionary path of plant species. Basic Local Alignment Search Tool (BLAST) and Clustal Omega application were used to perform local alignment and global multiple alignments between species and define the relativity between them in order to approximately determine what species falls in the evolutionary path over time.