ArticleBioinformatics advances2025
NifFinder: improved Nif protein prediction using SWeeP vectors and neural networks.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Motivation: Biological nitrogen fixation is a vital process for global ecosystems and agriculture; however, the diversity and complexity of Results: We present NifFinder, a computational framework that integrates SWeeP vector encoding with neural network classifiers to predict up to 24 different Nif protein classes across Archaea and Bacteria. NifFinder achieved an average accuracy of 84.31%, with sensitivity (86.49%), precision (81.97%), F1-score (82.33%), and a class correlation coefficient of 0.94. Benchmarking against Nif curated resources showed strong agreement and robust classification even under class imbalance. By expanding beyond traditional subsets of Availability and implementation: The NifFinder installation instructions and source code can be accessed at https://sourceforge.net/projects/NifFinder.
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