Results 61 to 70 of about 206,716 (316)
Clustering graph data: the roadmap to spectral techniques
Discover Artificial IntelligenceGraph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance.
Rahul Mondal +5 more
doaj +1 more source
, 2017 This chapter introduces the topic of graph algorithms on GPUs. It starts by presenting and comparing the main important data structures and techniques applied for representing and analysing graphs on GPUs at the state of the art.It then presents the theory and an updated review of the most efficient implementations of graph algorithms for GPUs.
BUSATO, FEDERICO, BOMBIERI, Nicola
openaire +2 more sources
Molecular Oncology, EarlyView.Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova +14 more
wiley +1 more source
Molecular Oncology, EarlyView.CIN85 is highly expressed in osteosarcoma, particularly in metastatic lesions. Its overexpression increases cell migration and Matrigel invasion, while silencing CIN85 suppresses these behaviors. Transcriptome analysis shows that CIN85 regulates MMP2, COL3A1, and Akt/mTOR signaling. Targeting these pathways reverses CIN85‐induced motility, highlighting
Iryna Horak +10 more
wiley +1 more source
Exploiting structure to cope with NP-hard graph problems: Polynomial and exponential time exact algorithms [PDF]
, 2010 An ideal algorithm for solving a particular problem always finds an optimal solution, finds such a solution for every possible instance, and finds it in polynomial time.
VAN-'T-HOF, PIM
core
Capturing Topology in Graph Pattern Matching [PDF]
, 2011 Graph pattern matching is often defined in terms of subgraph isomorphism, an np-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead.
Huai, Jinpeng +9 more
core +1 more source
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 +7 more
wiley +1 more source
Malware Detection via Extended Label Propagation Through Graph Inference
IEEE Access, 2019 In this paper, we model the malware detection problem as a graph inference problem, and develop a novel belief propagation approach within a semi-supervised learning scheme that fully makes use of files' and hosts' connections to detect malware ...
Yitu Fu, Ju Xu
doaj +1 more source
MITF maintains genome stability in nonmelanocyte lineages
Molecular Oncology, EarlyView.MITF is essential for melanocyte survival and acts as an oncogene in 10%–20% of melanomas. We show that MITF depletion causes genome instability in nonmelanocytic cells, leading to LATS2‐mediated P53 activation, cell cycle arrest, and apoptosis. This study highlights the role of MITF as a genome maintenance factor beyond the melanocyte lineage. Created
Drifa H. Gudmundsdottir +13 more
wiley +1 more source
Genus Distribution for a Graph [PDF]
, 2009 In this paper we develop the technique of a distribution decomposition for a graph. A formula is given to determine genus distribution of a cubic graph.
Liangxia, Wan +2 more
core +1 more source