Results 41 to 50 of about 168,985 (282)
Advanced Science, EarlyView.HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li +8 more
wiley +1 more source
Identifying protein complexes directly from high-throughput TAP data with Markov random fields
BMC Bioinformatics, 2007 Background Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process.
Krause Roland +3 more
doaj +1 more source
Markov random fields and iterated toric fibre products [PDF]
, 2018 We prove that iterated toric fibre products from a finite collection of toric varieties are defined by binomials of uniformly bounded degree. This implies that Markov random fields built up from a finite collection of finite graphs have uniformly bounded
Draisma, Jan, Oosterhof, Florian M.
core +3 more sources
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Advanced Science, EarlyView.Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source
Adaptive Markov Random Fields for Example-Based Super-resolution of Faces
EURASIP Journal on Advances in Signal Processing, 2006 Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution.
Stephenson Todd A, Chen Tsuhan
doaj +1 more source
Minimum Conditional Description Length Estimation for Markov Random Fields
, 2016 In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph $G=(V,E)$.
Neuhoff, David L., Reyes, Matthew G.
core +1 more source
NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction
Advanced Science, EarlyView.Chromatin loops are central to gene regulation and 3D genome organization. Leveraging Nanopore sequencing's ability to jointly capture DNA sequence and methylation, we present NanoLoop, the first framework for genome‐wide chromatin loop prediction using Nanopore data.
Wenjie Huang +5 more
wiley +1 more source
LifeAtherosclerosis, a leading cause of cardiovascular disease, necessitates advanced and innovative modeling techniques to better understand and predict plaque dynamics.
Amun G. Hofmann
doaj +1 more source
Learning Traffic Flow Dynamics Using Random Fields
IEEE Access, 2019 This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories.
Saif Eddin G. Jabari +3 more
doaj +1 more source
COMBINE MARKOV RANDOM FIELDS AND MARKED POINT PROCESSES TO EXTRACT BUILDING FROM REMOTELY SENSED IMAGES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Automatic building extraction from remotely sensed images is a research topic much more significant than ever. One of the key issues is object and image representation.
D. Chai, W. Förstner, M. Ying Yang
doaj +1 more source