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A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes

https://doi.org/10.1109/tcbb.2020.2988450

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4615-5689-3openalex-snapshot

Feature Selection for Knowledge Discovery and Data Mining

inferredcites →https://doi.org/10.1007/978-3-319-97919-9openalex-snapshot

Hierarchical Feature Selection for Knowledge Discovery

inferredcites →https://doi.org/10.1007/978-3-642-01307-2_4openalex-snapshot

DTU: A Decision Tree for Uncertain Data

inferredcites →https://doi.org/10.1007/978-3-642-23544-3_25openalex-snapshot

Feature Selection with Mutual Information for Uncertain Data

inferredcites →https://doi.org/10.1007/978-94-017-2053-3openalex-snapshot

Lazy Learning

inferredcites →https://doi.org/10.1007/s10462-017-9541-yopenalex-snapshot

An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features

inferredcites →https://doi.org/10.1007/s10522-017-9683-yopenalex-snapshot

A review of supervised machine learning applied to ageing research

inferredcites →https://doi.org/10.1016/j.exger.2017.08.005openalex-snapshot

Mechanisms underlying longevity: A genetic switch model of aging

inferredcites →https://doi.org/10.1016/j.ymeth.2016.08.014openalex-snapshot

Feature selection methods for big data bioinformatics: A survey from the search perspective

inferredcites →https://doi.org/10.1038/35041682openalex-snapshot

Why do we age?

inferredcites →https://doi.org/10.1038/75556openalex-snapshot

Gene Ontology: tool for the unification of biology

inferredcites →https://doi.org/10.1038/nature08980openalex-snapshot

The genetics of ageing

inferredcites →https://doi.org/10.1038/s41598-018-22240-wopenalex-snapshot

Prediction and characterization of human ageing-related proteins by using machine learning

inferredcites →https://doi.org/10.1093/bioinformatics/btg047openalex-snapshot

Learning rule-based models of biological process from gene expression time profiles using Gene Ontology

inferredcites →https://doi.org/10.1093/nar/gky1131openalex-snapshot

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

inferredcites →https://doi.org/10.1098/rspb.2004.2732openalex-snapshot

Protein networks, pleiotropy and the evolution of senescence

inferredcites →https://doi.org/10.1098/rstb.2010.0286openalex-snapshot

Computational biology for ageing

inferredcites →https://doi.org/10.1109/tcbb.2014.2355218openalex-snapshot

Predicting the Pro-Longevity or Anti-Longevity Effect of Model Organism Genes with New Hierarchical Feature Selection Methods

inferredcites →https://doi.org/10.1111/j.1474-9726.2008.00442.xopenalex-snapshot

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

inferredcites →https://doi.org/10.1137/1.9781611972832.10openalex-snapshot

Discriminative Feature Selection for Uncertain Graph Classification

inferredcites →https://doi.org/10.1137/1.9781611975321.83openalex-snapshot

A Novel Genetic Algorithm for Feature Selection in Hierarchical Feature Spaces

inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot

The WEKA data mining software

inferredcites →https://doi.org/10.1201/9781584888796openalex-snapshot

Computational Methods of Feature Selection

inferredcites →https://doi.org/10.1242/dmm.001040openalex-snapshot

The role of insulin/IGF-like signaling in C. elegans longevity and aging

inferredcites →https://doi.org/10.1371/journal.pone.0208626openalex-snapshot

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

inferredcites →https://doi.org/10.3233/ida-2011-0491openalex-snapshot

Lazy attribute selection: Choosing attributes at classification time

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Simultaneously feature selection and parameters optimization by teaching–learning and genetic algorithms for diagnosis of breast cancer

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124568openalex-snapshot

Explicit unsupervised feature selection based on structured graph and locally linear embedding

inferred← citeshttps://doi.org/10.1016/j.ins.2024.121821openalex-snapshot

Class-specific semi-supervised feature selection with fuzzy convex balling information granularity

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Dimension reduction method of high-dimensional fault datasets based on C_M_t-SNE under unsupervised background

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Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

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An Innovative Machine Learning based Heart Disease Assessment System by Sequential Feature Selection Approach

inferred← citeshttps://doi.org/10.1109/icdm51629.2021.00148openalex-snapshot

An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data

inferred← citeshttps://doi.org/10.1109/iciccs56967.2023.10142473openalex-snapshot

An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and Machine Learning Techniques

inferred← citeshttps://doi.org/10.1109/jstars.2023.3297278openalex-snapshot

mRMR-Tri-ConcaveHull Detector for Floating Small Targets in Sea Clutter

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Satellite telemetry data feature selection method based on genetic particle swarm optimization

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Incorporating I Ching Knowledge Into Prediction Task via Data Mining

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