Results 121 to 130 of about 217,546 (313)

Convolutional Goppa codes defined on fibrations [PDF]

arXiv, 2010
We define a new class of Convolutional Codes in terms of fibrations of algebraic varieties generalizaing our previous constructions of Convolutional Goppa Codes. Using this general construction we can give several examples of Maximum Distance Separable (MDS) Convolutional Codes.
arxiv  

A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery

Advanced Science, EarlyView.
Knowledge‐Guided Drug Relational Predictor (KGDRP), a graph representation learning approach, effectively integrates multiple omics data, including biological network data, gene expression data, and sequence data that incorporates chemical molecular structures.
Qing Ye, Yundian Zeng, Linlong Jiang, Yu Kang, Peichen Pan, Jiming Chen, Yafeng Deng, Haitao Zhao, Shibo He, Tingjun Hou, Chang‐Yu Hsieh   +10 more
wiley   +1 more source

On the arranging data transmission channel of an underwater navigation system with fast fading channel conditions

Известия высших учебных заведений России: Радиоэлектроника, 2015
The construction of hydroacoustic signal for object positioning in a fast fading aquatic environment is considered. The relevant data coding options of underwater signal are proposed.
D. S. Borovitskiy, V. P. Ipatov
doaj  

Spatially-Coupled QLDPC Codes [PDF]

Quantum
Spatially-coupled (SC) codes is a class of convolutional LDPC codes that has been well investigated in classical coding theory thanks to their high performance and compatibility with low-latency decoders.
Siyi Yang, Robert Calderbank
doaj   +1 more source

Integrative Multi‐Omics and Routine Blood Analysis Using Deep Learning: Cost‐Effective Early Prediction of Chronic Disease Risks

Advanced Science, EarlyView.
Omicsformer, a deep learning model, integrates multi‐omics and routine blood data to accurately predict risks for nine chronic diseases, including cancer and cardiovascular conditions. Validated using large scale clinical data, it reveals early risk trajectories, advancing personalized medicine and offering a cost‐effective, community‐based solution ...
Zhibin Dong, Pei Li, Yi Jiang, Zhihan Wang, Shihui Fu, Hebin Che, Meng Liu, Xiaojing Zhao, Chunlei Liu, Chenghui Zhao, Qin Zhong, Chongyou Rao, Siwei Wang, Suyuan Liu, Dayu Hu, Dongjin Wang, Juntao Gao, Kai Guo, Xinwang Liu, En Zhu, Kunlun He   +20 more
wiley   +1 more source

Machine Learning‐Enabled Drug‐Induced Toxicity Prediction

Advanced Science, EarlyView.
Unexpected toxicity accounts for 30% of drug development failures. This review highlights ML innovations in predicting drug‐induced toxicity, emphasizing comparative analyses, interpretable algorithms, and multi‐source data integration. It categorizes toxicity types, summarizes ML models, and organizes key databases, offering strategies to address ...
Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo   +5 more
wiley   +1 more source

Deep Learning‐Enhanced Hand‐Driven Microfluidic Chip for Multiplexed Nucleic Acid Detection Based on RPA/CRISPR

Advanced Science, EarlyView.
Efficient R‐CHIP HR‐HPV Screening System: The R‐CHIP system utilizes the RPA/CRISPR method, a hand‐driven centrifugal microfluidic device, a smartphone micro‐imaging system, and the ResNet‐18 deep‐learning algorithm to simplify the sample detection process, ensure accurate results, and reduce costs.
Tao Xu, Ying Zhang, Shunji Li, Chenxi Dai, Hongguo Wei, Dongjuan Chen, Yunjun Zhao, He Liu, Deliang Li, Peng Chen, Bi‐Feng Liu, Ye Tian   +11 more
wiley   +1 more source

Embryonic cranial cartilage defects in the Fgfr3Y367C/+ mouse model of achondroplasia

The Anatomical Record, EarlyView., 2023
Abstract Achondroplasia, the most common chondrodysplasia in humans, is caused by one of two gain of function mutations localized in the transmembrane domain of fibroblast growth factor receptor 3 (FGFR3) leading to constitutive activation of FGFR3 and subsequent growth plate cartilage and bone defects.
Susan M. Motch Perrine, Nishchal Sapkota, Kazuhiko Kawasaki, Yejia Zhang, Danny Z. Chen, Mizuho Kawasaki, Emily L. Durham, Yann Heuzé, Laurence Legeai‐Mallet, Joan T. Richtsmeier   +9 more
wiley   +1 more source

Generalized Feedback Decoding of Convolutional Codes [PDF]

, 1982
Wai-Hung Ng
openalex   +1 more source

Explainable Deep Multilevel Attention Learning for Predicting Protein Carbonylation Sites

Advanced Science, EarlyView.
Selective carbonylation sites (SCANS) are conceptualized, designed, evaluated, and released. SCANS captures segment‐level, protein‐level, and residue embeddings features. It utilizes elaborate loss function to penalize cross‐predictions at the residue level.
Jian Zhang, Jingjing Qian, Pei Wang, Xuan Liu, Fuhao Zhang, Haiting Chai, Quan Zou   +6 more
wiley   +1 more source