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GenEx is a new pipeline that transforms raw SARS‑CoV‑2 gene sequences into codon co‑occurrence graphs, extracting over 25 graph features. It introduces two graph generation methods—MSCG and LAPCG—and applies spectral graph analysis via Singular Value Decomposition to improve class separability. Using these techniques, the authors trained 23 machine‑learning models that successfully detected all major SARS‑CoV‑2 variants.
arXiv:2609.14882v1 Announce Type: cross Abstract: Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinform...
arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.
The paper introduces Murmur2Vec, a lightweight, alignment‑free embedding that uses k‑mer counts hashed with MurmurHash to create a compact representation for biological sequences. It provides a full theoretical analysis, including bias/variance formulas, a Johnson–Lindenstrauss‑style concentration bound, and an excess‑risk bound that clarifies the trade‑off between hash‑table size and classifier performance. Empirically, Murmur2Vec matches or surpasses a fine‑tuned 650M‑parameter ESM‑2 protein language model across several classification tasks, including SARS‑CoV‑2 spike lineage and HIV‑1 Env subtype identification.
arXiv:2503. 22939v4 Announce Type: replace Abstract: The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics.
arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.