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.
By Arefin Amin, Labiba Faiza Karim, M. Monir Uddin
arXiv:2609.14882v1 Announce Type: cross
Abstract: Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinform...
By Evgeny S. Saveliev, Krzysztof Kacprzyk, Charlotte Capitanchik, Neelanjan Mukherjee, Kate Matlin, Ryan Sheridan, Srinivas Ramachandran, Jernej Ule, David L. Bentley, Mihaela van der Schaar
arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.
By Wenbo Zhang
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.
By Sarwan Ali, Taslim Murad, Imdadullah Khan, Safi Faizullah
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.
By Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Harshith Guduru, Mohanad Mohammed
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.
By Fenosoa Randrianjatovo, Maya Saleh, Simon Girard, Amadou Barry
arXiv:2608.24121v1 Announce Type: new
Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fa...
By Yingshu Li, Yunyi Liu, Zhanyu Wang, Zailong Chen, Lingqiao Liu, Lei Wang, Luping Zhou
arXiv:2509.20702v3 Announce Type: replace-cross
Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to...
By Hongqian Niu, Jordan Bryan, Jacob Williams, Hufeng Zhou, Zhun Deng, Haoyu Zhang, Xihao Li, Didong Li
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.
By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo
arXiv:2607. 16553v1 Announce Type: new Abstract: Protein fold classification can be approached via sequence-based representations or structural descriptors, but direct comparisons between lightweight handcrafted descriptors and pretrained protein language model embeddings remain limited.
By Jianru Shen
GraphTFactor is a new end-to-end framework that represents protein sequences as graphs to directly classify transcription factors. By encoding sequences into graph structures, it handles variable-length inputs and preserves relational information without length constraints. Experiments show that GraphTFactor outperforms state‑of‑the‑art methods on benchmark datasets and offers interpretable insights into sequence regions driving predictions, with an open-source package and web app for easy use.
By Domenico Amato, Salvatore Calderaro, Giosu\`e Lo Bosco, Riccardo Rizzo, Filippo Vella
EvoLen is a tokenization method for DNA language models that incorporates evolutionary information to prioritize functional sequence patterns such as regulatory motifs. It groups DNA sequences by cross-species evolutionary signals, trains separate BPE tokenizers for each group, merges vocabularies with a rule that favors preserved patterns, and uses length-aware decoding with dynamic programming. Experiments show EvoLen better preserves functional motifs, differentiates genomic contexts, and aligns with evolutionary constraints while matching or surpassing standard BPE on various DNALM benchmarks.
By Nan Huang, Xiaoxiao Zhou, Junxia Cui, Mario Tapia-Pacheco, Tiffany Amariuta, Yang Li, Jingbo Shang