Results 121 to 130 of about 6,969,735 (365)
On Two-Stage Feature Selection Methods for Text Classification
IEEE Access, 2018 Text classification is a high dimensional pattern recognition problem where feature selection is an important step. Although researchers still propose new feature selection methods, there exist many two-stage feature selection methods combining existing ...
Alper Kursat Uysal
doaj +1 more source
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
Human-in-the-Loop Feature Selection
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Feature selection is a crucial step in the conception of Machine Learning models, which is often performed via datadriven approaches that overlook the possibility of tapping into the human decision-making of the model’s designers and users. We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback ...
Correia, Alvaro, Lecue, Freddy
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ERBIN limits epithelial cell plasticity via suppression of TGF‐β signaling
FEBS Letters, EarlyView.In breast and lung cancer patients, low ERBIN expression correlates with poor clinical outcomes. Here, we show that ERBIN inhibits TGF‐β‐induced epithelial‐to‐mesenchymal transition in NMuMG breast and A549 lung adenocarcinoma cell lines. ERBIN suppresses TGF‐β/SMAD signaling and reduces TGF‐β‐induced ERK phosphorylation.
Chao Li +3 more
wiley +1 more source
Feature selection based on bootstrapping
2005 ICSC Congress on Computational Intelligence Methods and Applications, 2006 The results of feature selection methods have a great influence on the success of data mining processes, especially when the data sets have high dimensionality. In order to find the optimal result from feature selection methods, we should check each possible subset of features to obtain the precision on classification, i.e., an exhaustive search ...
Díaz Díaz, Norberto +3 more
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Wrappers for feature subset selection
Artificial Intelligence, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ron Kohavi, George H. John
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Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Genome Biology, 2019 Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation.
F. W. Townes +3 more
semanticscholar +1 more source
FEBS Letters, EarlyView.Knowing how proteases recognise preferred substrates facilitates matching proteases to applications. The S1′ pocket of protease EA1 directs cleavage to the N‐terminal side of hydrophobic residues, particularly leucine. The S1′ pocket of thermolysin differs from EA's at only one position (leucine in place of phenylalanine), which decreases cleavage ...
Grant R. Broomfield +3 more
wiley +1 more source
Informative Feature Selection for Domain Adaptation
IEEE Access, 2019 Domain adaptation aims at extracting knowledge from an auxiliary source domain to assist the learning task in a target domain. When the data distribution of the target domain is different from that of the source domain, the direct use of source data for ...
Feng Sun +5 more
doaj +1 more source
, 2017 Feature selection is used in many application areas relevant to expert and intelligent systems, such as machine learning, data mining, cheminformatics and natural language processing. In this study we propose methods for feature selection and features analysis based on Support Vector Machines (SVM) with linear kernels.
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