Results 101 to 110 of about 344,290 (331)
Molecular Oncology, EarlyView.This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh +8 more
wiley +1 more source
Molecular Oncology, EarlyView.The COMBAT classification system, developed through multi‐omics integration, stratifies adult patients with B‐cell acute lymphoblastic leukemia(B‐ALL) into three molecular subtypes with distinct surface antigen patterns, immune landscape, methylation patterns, biological pathways and prognosis.
Yang Song +11 more
wiley +1 more source
The Deeper Studies of IC 2488 and IC 2714
The Astrophysical JournalIn this paper, we use two methods, an algorithm of the density-based spatial clustering of applications with noise combined with the Gaussian mixture model, and the maximum likelihood method, to study two open clusters: IC 2488 and IC 2714 with Gaia Data
Jin-Sheng Qiu +4 more
doaj +1 more source
Calibration of radii and masses of open clusters with a simulation
, 2010 Context: A recent new approach to apply a simple dynamical mass estimate of tidally limited star clusters is based on the identification of the tidal radius in a King profile with the dynamical Jacobi radius.
A. Ernst +20 more
core +1 more source
Unraveling LINE‐1 retrotransposition in head and neck squamous cell carcinoma
Molecular Oncology, EarlyView.The novel RetroTest method allows the detection of L1 activation in clinical samples with low DNA input, providing global L1 activity and the identification of the L1 source element. We applied RetroTest to a real‐world cohort of HNSCC patients where we reported an early L1 activation, with more than 60% of T1 patients showing L1 activity.
Jenifer Brea‐Iglesias +12 more
wiley +1 more source
Multiple Machine Learning as a Powerful Tool for Star Cluster Analysis
The Astronomical JournalThis work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness of the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of an old binary cluster candidate—comprised ...
Denilso Camargo
doaj +1 more source
Molecular Oncology, EarlyView.Analysis of treatment‐naïve high‐grade serous ovarian carcinoma (HGSOC) and control tissues for ERVs, LINE‐1 (L1), inflammation, and immune checkpoints identified five clusters with diverse patient recurrence‐free survivals. An inflammation score was calculated and correlated with retroelement expression, where one novel cluster (Triple‐I) with high ...
Laura Glossner +6 more
wiley +1 more source
Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
wiley +1 more source
The Astrophysical JournalStars in an open cluster are assumed to have formed from a broadly homogeneous distribution of gas, implying that they should be chemically homogeneous.
Amaya Sinha +6 more
doaj +1 more source
Molecular Oncology, EarlyView.AZD9291 has shown promise in targeted cancer therapy but is limited by resistance. In this study, we employed metabolic labeling and LC–MS/MS to profile time‐resolved nascent protein perturbations, allowing dynamic tracking of drug‐responsive proteins. We demonstrated that increased NNMT expression is associated with drug resistance, highlighting NNMT ...
Zhanwu Hou +5 more
wiley +1 more source