Results 71 to 80 of about 392,357 (290)
Spectral dimensionality reduction for HMMs [PDF]
, 2012 Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling.
Foster, Dean P. +2 more
core +2 more sources
Emerging role of ARHGAP29 in melanoma cell phenotype switching
Molecular Oncology, EarlyView.This study gives first insights into the role of ARHGAP29 in malignant melanoma. ARHGAP29 was revealed to be connected to tumor cell plasticity, promoting a mesenchymal‐like, invasive phenotype and driving tumor progression. Further, it modulates cell spreading by influencing RhoA/ROCK signaling and affects SMAD2 activity. Rho GTPase‐activating protein
Beatrice Charlotte Tröster +3 more
wiley +1 more source
A synthetic benzoxazine dimer derivative targets c‐Myc to inhibit colorectal cancer progression
Molecular Oncology, EarlyView.Benzoxazine dimer derivatives bind to the bHLH‐LZ region of c‐Myc, disrupting c‐Myc/MAX complexes, which are evaluated from SAR analysis. This increases ubiquitination and reduces cellular c‐Myc. Impairing DNA repair mechanisms is shown through proteomic analysis.
Nicharat Sriratanasak +8 more
wiley +1 more source
Two-Stage Dimensionality Reduction for Social Media Engagement Classification
Applied SciencesThe high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
Jose Luis Vieira Sobrinho +2 more
doaj +1 more source
Spectral Dimensionality Reduction [PDF]
In this paper, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence ...
Jean-François Paiement +5 more
core
Molecular Oncology, EarlyView.This study integrates transcriptomic profiling of matched tumor and healthy tissues from 32 colorectal cancer patients with functional validation in patient‐derived organoids, revealing dysregulated metabolic programs driven by overexpressed xCT (SLC7A11) and SLC3A2, identifying an oncogenic cystine/glutamate transporter signature linked to ...
Marco Strecker +16 more
wiley +1 more source
Using Dimensionality Reduction to Analyze Protein Trajectories
Frontiers in Molecular Biosciences, 2019 In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined ...
Gareth A. Tribello, Piero Gasparotto
doaj +1 more source
Screening for lung cancer: A systematic review of overdiagnosis and its implications
Molecular Oncology, EarlyView.Low‐dose computed tomography (CT) screening for lung cancer may increase overdiagnosis compared to no screening, though the risk is likely low versus chest X‐ray. Our review of 8 trials (84 660 participants) shows added costs. Further research with strict adherence to modern nodule management strategies may help determine the extent to which ...
Fiorella Karina Fernández‐Sáenz +12 more
wiley +1 more source
Dimensionality Reduction Algorithms on High Dimensional Datasets
Emitter: International Journal of Engineering Technology, 2014 Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicatedespecially when the number of possible ...
Iwan Syarif
doaj +3 more sources
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method [PDF]
, 2021 Artur Jordão +3 more
openalex +1 more source