The paper presents a deep learning approach that converts nanopore resistive pulse signals into scaleograms using continuous wavelet transforms, enabling the classification of peptides as an image‑classification problem. On a dataset of 42 peptides, the method achieves an 82% macro‑averaged accuracy, outperforming previous descriptor‑based techniques by 8.6 percentage points. The models also remain accurate after significant weight pruning and 8‑bit quantization, making them suitable for deployment on embedded sensing hardware.
By Julian Ho{\ss}bach, Samuel Tovey, Sandro Kuppel, Tobias Ensslen, Jan C. Behrends, Christian Holm
arXiv:2607. 20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics.
By Sandro Kuppel, Julian Ho{\ss}bach, Samuel Tovey, Christian Holm
arXiv:2608.30877v1 Announce Type: new
Abstract: Recent advances in large language models (LLMs) have demonstrated exceptional performance in protein-ligand interaction prediction, but state-of-the-ar...
By Rui Xiao, Yili Xu
arXiv:2606. 26520v1 Announce Type: cross Abstract: Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene.
By Johnny Peng, Thanh Tung Khuat, Ellen Otte, Katarzyna Musial, Bogdan Gabrys
arXiv:2608. 16870v1 Announce Type: new Abstract: Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential.
By Serena Su, Yifan Wang, Senwei Liang
arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.
By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia
The paper presents a novel approach to monitoring industrial anaerobic co‑digestion by combining a generative LSTM‑beta‑VAE with a multi‑output LSTM soft sensor. By synthesising physically valid sequences of unstable operation from a year of plant data, the method augments sparse datasets and enables simultaneous prediction of key stability indicators such as pH, volatile acid ratio, and biogas production. The augmented model outperforms standard algorithms and unaugmented LSTMs, offering robust early‑warning capabilities for bioprocess stability.
By Rohit Murali, Benaissa Dekhici, Tao Chen, Dongda Zhang, Michael Short
arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.
By Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge, Brook Byrns, Heather L. Wilson, Sureesh Tikoo, Steven Rayan, Gordon Broderick
The paper introduces a unified framework that combines adaptive surrogate modeling with simulation-driven inverse design for microdroplet generators. High‑fidelity Volume‑of‑Fluid simulations supply training data, while neural‑network ensembles predict flow regimes and droplet diameters; ensemble‑based uncertainty quantification identifies critical parameter regions near regime transitions. A physics‑informed enrichment strategy and Particle Swarm Optimization are used to refine the surrogates and perform constrained inverse design, achieving markedly higher prediction accuracy and reliable identification of physically realizable configurations.
By David Gabriel Harispe, Mat\'ias Gerard, Pablo A. Kler
arXiv:2608. 06993v1 Announce Type: cross Abstract: Large-scale pretrained time-series models achieve strong results through large-scale pretraining and task-agnostic representation learning, but they rely on abundant, diverse data that industrial and scientific domains often lack.
By Gregor Molan (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Grafika Jati (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Francesco Barchi (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Andrea Acquaviva (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Alja\v{z} Osterman (LE-Tehnika d.o.o., \v{S}uceva 27, Kranj, 4000, Slovenia), Martin Molan (Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland)
Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across cell lines and media, and runs with near-identical early behaviour can diverge into different futures.
arXiv:2602. 22822v3 Announce Type: replace Abstract: Tandem mass spectrometry (MS/MS) is central to small molecule identification, but current deep learning systems for spectrum prediction still remain difficult to evaluate and deploy in practice.
By Yunhua Zhong, Yixuan Tang, Yifan Li, Pan Liu, Zhiwen Yang, Jie Yang, Jun Xia