Results 71 to 80 of about 228,260 (299)

Contrastive Network Representation Learning

CoRR
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection.
Zihan Dong, Xin Zhou, Ryumei Nakada, Lexin Li, Linjun Zhang   +4 more
openaire   +2 more sources

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source

Higher-order Network Representation Learning [PDF]

Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate the effectiveness of learning higher-order network representations.
Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh   +2 more
openaire   +1 more source

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Positive-Unlabeled Learning for Network Link Prediction

Mathematics, 2022
Link prediction is an important problem in network data mining, which is dedicated to predicting the potential relationship between nodes in the network.
Shengfeng Gan, Mohammed Alshahrani, Shichao Liu   +2 more
doaj   +1 more source

HARP: Hierarchical Representation Learning for Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
We present HARP, a novel method for learning low dimensional embeddings of a graph’s nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e.
Haochen Chen, Bryan Perozzi, Yifan Hu 0001, Steven Skiena   +3 more
openaire   +2 more sources

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Exploiting Centrality Information with Graph Convolutions for Network Representation Learning

, 2019
Network embedding has been proven effective to learn low-dimensional vector representations for network vertices, and recently received a tremendous amount of research attention.
Tong Chen, Wen-Chih Peng, Hongxu Chen, Quoc Viet Hung Nguyen, Peng, WC, Li, X, Chen, T, Hongzhi Yin, Xue Li, Chen, H, Yin, H, Nguyen, QVH   +11 more
core   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source

Self-supervised speech representation learning based on positive sample comparison and masking reconstruction

Tongxin xuebao, 2022
To solve the problem that existing contrastive prediction based self-supervised speech representation learning methods need to construct a large number of negative samples, and their performance depends on large training batches, requiring a lot of ...
Wenlin ZHANG, Xuepeng LIU, Tong NIU, Qi CHEN, Dan QU   +4 more
doaj   +2 more sources