Results 71 to 80 of about 256,683 (274)
, 2016 Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization.
Makse, Hernán A. +3 more
core +2 more sources
Molecular Oncology, EarlyView.Pre‐analytical handling critically determines liquid biopsy performance. This study defines practical best‐practice conditions for cell‐free DNA (cfDNA) and extracellular vesicle–derived DNA (evDNA), showing how processing time, storage conditions, tube type, and plasma input volume affect DNA integrity and mutation detection.
Jonas Dohmen +11 more
wiley +1 more source
Somatic mutational landscape in von Hippel–Lindau familial hemangioblastoma
Molecular Oncology, EarlyView.The causes of central nervous system (CNS) hemangioblastoma in Von Hippel–Lindau (vHL) disease are unclear. We used Whole Exome Sequencing (WES) on familial hemangioblastoma to investigate events that underlie tumor development. Our findings suggest that VHL loss creates a permissive environment for tumor formation, while additional alterations ...
Maja Dembic +5 more
wiley +1 more source
Dynamic Influence Maximization
, 2021 We initiate a systematic study on $\mathit{dynamic}$ $\mathit{influence}$ $\mathit{maximization}$ (DIM). In the DIM problem, one maintains a seed set $S$ of at most $k$ nodes in a dynamically involving social network, with the goal of maximizing the expected influence spread while minimizing the amortized updating cost. We consider two evolution models.
openaire +2 more sources
Molecular Oncology, EarlyView.Targeted therapy was evaluated in SHH medulloblastoma using neuroepithelial stem cell (NES) and tumor‐derived NES‐like (tNES) models in 2D monolayers and 3D spheroids. PI3K, AKT, and CDK4/6 inhibitors had minimal effects in NES but markedly reduced viability and growth and induced apoptosis in tNES cells, revealing distinct therapeutic vulnerabilities.
Monika Lukoseviciute +4 more
wiley +1 more source
Importance Sketching of Influence Dynamics in Billion-scale Networks
, 2017 The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks.
Dinh, Thang N. +3 more
core +1 more source
Molecular Oncology, EarlyView.We analyze cisplatin–DNA adducts (CDAs) and double‐strand breaks (DSBs) in a cell‐cycle‐dependent manner. We find that CDAs form similarly across all cell cycle phases. DSBs arise only in S‐phase. CDAs might not directly impair DSB repair, but S‐phase DSB lesions evolve in the presence of CDAs and disrupt repair in G2, also causing radiosensitization ...
Ye Qiu +10 more
wiley +1 more source
A Community-Based Method for Identifying Influential Nodes Using Network Embedding
International Journal of Information and Communication Technology Research, 2022 People's influence on their friends' personal opinions and decisions is an essential feature of social networks. Due to this, many businesses use social media to convince a small number of users in order to increase awareness and ultimately maximize ...
Narges Vafaei, Mohammad Reza Keyvanpour
doaj
Identifying Influential Individuals on Large-Scale Social Networks: A Community Based Approach
IEEE Access, 2018 Identifying a small subset of influential individuals on social networks can bring great benefits for many practical applications like viral marketing. This issue is typically formulated as the influence maximization problem.
Fanghua Ye +5 more
doaj +1 more source
A Node Embedding-Based Influential Spreaders Identification Approach
Mathematics, 2020 Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node ...
Dongming Chen +4 more
doaj +1 more source