arXiv Machine Learning
Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting
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arXiv Machine Learning
Sep 7
Deep Learning-Driven Peptide Classification in Biological Nanopores
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.
arXiv Machine Learning
Jul 23
Multi-modal transformer for signal classification in nanopore blockade experiments
arXiv:2607. 20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics.
arXiv Machine Learning
Sep 1
Deploying DeepSeek 175B Locally on a Single Consumer-Grade RTX 4060 Laptop with 32GB RAM for 200k-Scale Protein-Ligand Virtual Screening
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...
arXiv AI
Jun 26
Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting
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.
arXiv Machine Learning
Aug 18
Data-Efficient and Interpretable Classification of Circulating Tumor Cell Phenotypes in Microfluidic Devices via Deep Learning
arXiv:2608. 16870v1 Announce Type: new Abstract: Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential.
arXiv AI
Aug 14
Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
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.