Results 81 to 90 of about 951,800 (293)

Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent [PDF]

arXiv, 2020
Feature selection aims to select a subset of features to optimize the performances of downstream predictive tasks. Recently, multi-agent reinforced feature selection (MARFS) has been introduced to automate feature selection, by creating agents for each feature to select or deselect corresponding features.
arxiv  

On‐treatment dynamics of circulating extracellular vesicles in the first‐line setting of patients with advanced non‐small cell lung cancer: the LEXOVE prospective study

Molecular Oncology, EarlyView.
The LEXOVE prospective study evaluated plasma cell‐free extracellular vesicle (cfEV) dynamics using Bradford assay and dynamic light scattering in metastatic non‐small cell lung cancer patients undergoing first‐line treatments, correlating a ∆cfEV < 20% with improved median progression‐free survival in responders versus non‐responders.
Valerio Gristina, Viviana Bazan, Nadia Barraco, Simona Taverna, Mauro Manno, Samuele Raccosta, Anna Paola Carreca, Marco Bono, Tancredi Didier Bazan Russo, Francesco Pepe, Pasquale Pisapia, Lorena Incorvaia, Giuseppe Badalamenti, Giancarlo Troncone, Umberto Malapelle, Daniele Santini, Antonio Russo, Antonio Galvano   +17 more
wiley   +1 more source

Beta Distribution-Based Cross-Entropy for Feature Selection

Entropy, 2019
Analysis of high-dimensional data is a challenge in machine learning and data mining. Feature selection plays an important role in dealing with high-dimensional data for improvement of predictive accuracy, as well as better interpretation of the data ...
Weixing Dai, Dianjing Guo
doaj   +1 more source

Cellular liquid biopsy provides unique chances for disease monitoring, preclinical model generation and therapy adjustment in rare salivary gland cancer patients

Molecular Oncology, EarlyView.
We quantified and cultured circulating tumor cells (CTCs) of 62 patients with various cancer types and generated CTC‐derived tumoroid models from two salivary gland cancer patients. Cellular liquid biopsy‐derived information enabled molecular genetic assessment of systemic disease heterogeneity and functional testing for therapy selection in both ...
Nataša Stojanović Gužvić, Florian Lüke, Steffi Treitschke, Andrea Coluccio, Martin Hoffmann, Giancarlo Feliciello, Adithi Ravikumar Varadarajan, Xin Lu, Kathrin Weidele, Catherine Botteron, Silvia Materna–Reichelt, Felix Keil, Katja Evert, Florian Weber, Thomas Schamberger, Michael Althammer, Jirka Grosse, Dirk Hellwig, Christian Schulz, Stephan Seitz, Peter Ugocsai, Anke Schlenska‐Lange, Roman Mayr, Ulrich Kaiser, Wolfgang Dietmaier, Bernhard Polzer, Jens Warfsmann, Kamran Honarnejad, Tobias Pukrop, Daniel Heudobler, Christoph A. Klein, Christian Werno   +31 more
wiley   +1 more source

Feature Selection for Latent Factor Models [PDF]

arXiv
Feature selection is crucial for pinpointing relevant features in high-dimensional datasets, mitigating the 'curse of dimensionality,' and enhancing machine learning performance. Traditional feature selection methods for classification use data from all classes to select features for each class. This paper explores feature selection methods that select
arxiv  

Information theory applied to feature selection of binary-coded infrared spectra for automated interpretation by retrieval of reference data [PDF]

, 1979
P. Dupuis, P. Cleij, H. A. Van ’T Klooster, A. Dijkstra   +3 more
openalex   +1 more source

Targeting rapid TKI‐induced AXL upregulation overcomes adaptive ERK reactivation and exerts antileukemic effects in FLT3/ITD acute myeloid leukemia

Molecular Oncology, EarlyView.
Adaptive ERK reactivation hinders FLT3 tyrosine kinase inhibitor (TKI) treatment in FLT3/ITD acute myeloid leukemia. Here, we report that FLT3 TKI treatment rapidly induces AXL expression and upregulation that is temporally associated with the adaptive ERK reactivation.
Tessa S. Seale, Li Li, J. Kyle Bruner, Melody Chou, Bao Nguyen, Jaesung Seo, Ruiqi Zhu, Mark J. Levis, Christine A. Pratilas, Donald Small   +9 more
wiley   +1 more source

On Supervised Feature Selection from High Dimensional Feature Spaces

APSIPA Transactions on Signal and Information Processing, 2022
Yijing Yang, Wei Wang, Hongyu Fu, C.-C. Jay Kuo   +3 more
doaj   +1 more source

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science [version 2; peer review: 2 approved, 1 approved with reservations]

Open Research Europe, 2023
Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields ...
Philip Loche, Alexander Goscinski, Guillaume Fraux, Victor Paul Principe, Benjamin Aaron Helfrecht, Sergei Kliavinek, Michele Ceriotti, Rose Kathleen Cersonsky   +7 more
doaj  

Feature selection environment for genomic applications

BMC Bioinformatics, 2008
Background Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction).
Martins David, Lopes Fabrício, Cesar Roberto M   +2 more
doaj   +1 more source