Results 181 to 190 of about 25,420,692 (388)
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
EMT‐associated bias in the Parsortix® system observed with pancreatic cancer cell lines
Molecular Oncology, EarlyView.The Parsortix® system was tested for CTC enrichment using pancreatic cancer cell lines with different EMT phenotypes. Spike‐in experiments showed lower recovery of mesenchymal‐like cells. This was confirmed with an EMT‐inducible breast cancer cell line.
Nele Vandenbussche +8 more
wiley +1 more source
Big Data Ethics in Research [PDF]
The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union.
Sfetcu, Nicolae
core
Analysis of Subsidence Data for the Big Hill Site, Texas
, 1999 Stephen J. Bauer
openalex +2 more sources
EXTENDING THE BIG POWER LAW IN THE SKY WITH TURBULENCE SPECTRA FROM WISCONSIN Hα MAPPER DATA [PDF]
, 2010 A. V. Chepurnov, A. Lazarian
openalex +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
Massively-Parallel Feature Selection for Big Data
, 2017 We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size).
Borboudakis, Giorgos +4 more
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