Results 81 to 90 of about 43,821 (259)
SAGE is a unified framework for spatial domain identification in spatial transcriptomics that jointly models tissue architecture and gene programs. Topic‐driven gene selection (NMF plus classifier‐based scoring) highlights spatially informative genes, while dual‐view graph embedding fuses local expression and non‐local functional relations.
Yi He +5 more
wiley +1 more source
Microbial synthesis of nanomaterials (NMs) is eco‐friendly, but the screening of microorganisms is limited by inefficient traditional methods (currently only involving∽400 microorganisms/90 NMs). We propose AI framework MicrobeDiscover, integrating a knowledge graph of microbe‐NM interactions.
Ludi Wang +12 more
wiley +1 more source
Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation
PScalpel, a machine learning model integrating protein structure extraction, graph contrastive learning, and a genetic algorithm, guides the engineering of protein phase separation ability. It adopts transfer learning methods to provide predictive recommendations for protein phase separation ability changes through single amino acid mutations in a ...
Chenqiu Zhang +9 more
wiley +1 more source
A novel low complexity, low latency rate 1/2 FEC code
In this paper, a relatively simple and low complexity rate 1/2 FEC (Forward Error Correction) code has been proposed. The proposed encoder combines the effect of the low cross correlation of two orthogonal sequences along with the effect of the ...
Maan A.S. Al-Adwany +2 more
doaj +1 more source
SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao +11 more
wiley +1 more source
Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence
Individual responses to food vary dramatically, challenging traditional dietary advice. This review explores how the unique genetic makeup, gut microbiome, and brain activity shape host metabolic health. We examine how artificial intelligence integrates these multimodal data to predict individualized dietary needs, moving beyond one‐size‐fits‐all ...
Yuanqing Fu +5 more
wiley +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Interleaved Convolutional Code and Its Viterbi Decoder Architecture
We propose an area-efficient high-speed interleaved Viterbi decoder architecture, which is based on the state-parallel architecture with register exchange path memory structure, for interleaved convolutional code. The state-parallel architecture uses as
Kong Jun Jin, Parhi Keshab K
doaj +1 more source
This manuscript presents the WDMS platform, an AI‐assisted, self‐powered wearable dual‐mode sensor for tele‐neurology. It integrates a contact–separation TENG insole with stretchable polyurethane optical‐fiber strain sensors to synchronously track plantar pressure and lower‐limb muscle deformation.
Tianliang Li +12 more
wiley +1 more source
The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience.
Hendy Briantoro +2 more
doaj +1 more source

