Feature selection techniques are essential in bioinformatics for reducing dimensionality and improving model performance. This review discusses various feature selection methods, including filter, wrapper, and embedded techniques, and their applications in sequence analysis, microarray analysis, and mass spectrometry. Filter methods assess feature relevance independently of the classifier, while wrapper methods integrate feature selection with model training. Embedded methods incorporate feature selection within the classifier itself. These techniques help avoid overfitting, improve model efficiency, and provide insights into biological processes. In sequence analysis, feature selection aids in identifying important motifs and predicting protein functions. In microarray analysis, it helps manage high-dimensional data and small sample sizes, using methods like univariate filters and multivariate techniques. In mass spectrometry, feature selection is crucial for handling large datasets and identifying relevant biomarkers. The review also addresses challenges in small sample domains, emphasizing the need for robust evaluation criteria and ensemble methods. Future directions include enhancing feature selection techniques for emerging areas like SNP analysis and text mining. Software tools for feature selection are provided, highlighting their utility in bioinformatics research. Overall, feature selection remains a vital tool for improving the accuracy and interpretability of bioinformatics models.Feature selection techniques are essential in bioinformatics for reducing dimensionality and improving model performance. This review discusses various feature selection methods, including filter, wrapper, and embedded techniques, and their applications in sequence analysis, microarray analysis, and mass spectrometry. Filter methods assess feature relevance independently of the classifier, while wrapper methods integrate feature selection with model training. Embedded methods incorporate feature selection within the classifier itself. These techniques help avoid overfitting, improve model efficiency, and provide insights into biological processes. In sequence analysis, feature selection aids in identifying important motifs and predicting protein functions. In microarray analysis, it helps manage high-dimensional data and small sample sizes, using methods like univariate filters and multivariate techniques. In mass spectrometry, feature selection is crucial for handling large datasets and identifying relevant biomarkers. The review also addresses challenges in small sample domains, emphasizing the need for robust evaluation criteria and ensemble methods. Future directions include enhancing feature selection techniques for emerging areas like SNP analysis and text mining. Software tools for feature selection are provided, highlighting their utility in bioinformatics research. Overall, feature selection remains a vital tool for improving the accuracy and interpretability of bioinformatics models.