ArticlePLoS computational biology2022
Eleven quick tips for data cleaning and feature engineering.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.Animals : an open access journal from MDPI · 2025Review
- Development and application of an early prediction model for risk of bloodstream infection based on real-world study.BMC medical informatics and decision making · 2025Article
- A teaching proposal for a short course on biomedical data science.PLoS computational biology · 2025Article
- Eight quick tips for biologically and medically informed machine learning.PLoS computational biology · 2025Article
- Hyperdimensional computing in biomedical sciences: a brief review.PeerJ. Computer science · 2025Article
- Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.Health information science and systems · 2024Article
- Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme.Journal of healthcare informatics research · 2024Article
- Ten quick tips for harnessing the power of ChatGPT in computational biology.PLoS computational biology · 2023Article
- Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment.PLoS computational biology · 2023Article
- Ten quick tips for avoiding pitfalls in multi-omics data integration analyses.PLoS computational biology · 2023Article
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Authors and funding
3 authors.
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Abstract
Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.
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