The paper audits zero‑shot vision‑language models—CLIP, BioCLIP, BioCLIP2, and a multilingual Jina CLIP v2—on 10,321 images of seven Bangladeshi freshwater fish species. BioCLIP2 achieves the highest accuracy (72.36% on BFF‑15 with English common names and 68.91% on SylFishBD with scientific names), while generic CLIP lags far behind (25.15% and 14.40%). The study also shows that Bengali prompts perform poorly, that Jina CLIP partially improves Bengali discrimination, and that image context (blur, masking) affects performance differently across species.
By Nazim-E-Alam, Tarek Rahman, Md Kishor Morol
arXiv:2609.37675v1 Announce Type: new
Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and str...
By Biswajit Banerjee, Claudia Alvarez Carreno, Anton S. Petrov
ProtLingo is a protein language modeling framework that enhances a pretrained single‑sequence Transformer backbone with conditional local memory and sparse expert routing. It maps residue representations into discrete codes, composes local windows into latent N‑gram addresses, and retrieves reusable residual signals for recurring sequence contexts. The model also converts selected feed‑forward blocks into sparse Mixture‑of‑Experts layers, allowing residue‑dependent computation while activating only a subset of parameters, achieving competitive performance on protein fitness prediction, FLIP benchmarks, and supervised contact prediction with a 150M‑parameter backbone.
By Mingrui Li, Sixian Shen, Minzhang Li, Ruiyi Zhang, Kexin Zhang, Jiakai Zhang, Jingyi Yu
arXiv:2610.11761v1 Announce Type: new
Abstract: The genome holds the blueprint that governs the biological properties of the cell. Consequently, advancing our knowledge of genomic function is crucial...
By Frederikke Isa Marin, Panagiotis Antoniadis, Dionysia Danai Brilli, Andreas Bjerregaard, Rachael DeVries, Yan Li, Ole Winther, Wouter Boomsma
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:2508. 07345v2 Announce Type: replace-cross Abstract: \textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages.
By Samiha Afaf Neha, Md. Ishrak Khan, Abir Ahammed Bhuiyan
arXiv:2609.08059v1 Announce Type: cross
Abstract: Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related biopro...
By Honghan Shen
arXiv:2509.23552v2 Announce Type: replace-cross
Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
By Md. Saiful Bari Siddiqui, Nowshin Tarannum
The study evaluates 4‑bit quantization and low‑rank adapter fine‑tuning (QLoRA) on several large protein language models, finding that many model‑task pairs retain over 90% of full fine‑tuning performance while achieving up to 90% GPU memory savings. QLoRA preserves early‑layer representations and induces task‑specific changes in later layers, closely resembling full fine‑tuning with smaller representational shifts. For generative models, 4‑bit quantization largely maintains structural and sequence‑level properties, though token‑level analysis reveals model‑dependent changes in autoregressive output distributions.
By Ilan Yaniv Zeisler, Sebastian Clancy, Pouriya Bayat, Saaim Raad, Ivan Kraskov, Matthew Xie, Vivian White, Spencer Perkins, Serena Singh, Sepehr Bayat, Keith Pardee
arXiv:2606. 11868v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases.
By Dongxin Lyu, Jingbo Zhou, Hongxin Xiang, Yuqiang Li, Jun Xia
The study demonstrates that a simple, sequence-only approach using 330 interpretable descriptors and the TabPFN tabular foundation model can outperform complex multimodal deep learning methods for multi-label antimicrobial peptide activity prediction. On the ESCAPE benchmark (82,359 peptides, five labels), a label‑powerset TabPFN model achieved a mean average precision of 77.8%, surpassing the previous best of 72.1%. The approach also shows that predicted structure is unnecessary, that a small set of global physicochemical scalars can recover most performance, and that modeling label dependence benefits rare activities and informs assay prioritization.
By Raunak Kumar, Anuj Pal, Dhruvi Solanki, Parikshit Pareek, Juhi Singh, Jitin Singla
arXiv:2606. 02629v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) are essential for many biological processes.
By Zaifei Yang, Samuel Ping-Man Choi, James Kwok