Majority‐Voting Overlapping Method for Error Correction in DNA Data Storage
We propose an overlapping‐based majority‐voting method for DNA data storage error correction. By aligning multiple reads and choosing the most frequent base per position, it suppresses substitution errors without prior models. Validated on synthetic and real sequencing data, it achieves high‐fidelity, scalable, and cost‐effective reconstruction ...
Thi Bich Ngoc Nguyen +5 more
wiley +1 more source
Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniques [PDF]
Reyes JS +6 more
europepmc +1 more source
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Deep Learning-Based EEG Emotion Recognition: A Review. [PDF]
Liu Y, Xue W, Yang L, Li M.
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
DSN-STC: Leveraging Siamese networks for optimized short text clustering. [PDF]
Molaei M +3 more
europepmc +1 more source
Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar +5 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Improved one-dimensional residual network high-voltage DC diagnosis for high-precision fault identification. [PDF]
Li R, Zhang X, Hao W, Wang T.
europepmc +1 more source
Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis. [PDF]
Chen Y, Zhang Q, Zhang MY.
europepmc +1 more source

