Heuristic-enabled active machine learning - A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster - Research summary

Aug 5, 2026

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Computational prediction of absolute essential genes using machine learning has become a major focus, yet many genes are conditionally rather than absolutely essential. Experimental identification of conditionally essential genes is reliable but laborious, time-consuming, and resource-intensive. This study introduces a heuristic-enabled active machine learning framework that uses a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. It proposes a new sampling selection strategy and a heuristic function to replace the human component in classic active learning, improving iterative classifier performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Abstract

Method and heuristic-enabled active learning framework

Experimental/benchmark evaluation and gene discovery

Functional enrichment analysis and proposed future use

PLOS ONE OPEN ACCESS Citation: Aromolaran OT, Isewon I, Adedeji E, Oswald M, Adebiyi E, Koenig R, et al. (2023) Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster. PLoS ONE 18(8): e0288023 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0288023](doi.org/10.1371/journal.pone.0288023) [Editor:](Editor: Jian Xu)[ Jian Xu](Editor: Jian Xu), [East China Normal University](East China Normal University)[ ](East China Normal University)School of Life Sciences, CHINA Received: April 3, 2023 Accepted: June 18, 2023 Published: August 9, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal.pone.0288023](https://doi.org/10.1371/journal.pone.0288023) [Copyright:](Copyright:) © [2023 Aromolaran](2023 Aromolaran) et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data used for model evaluation is publicly available on UCI machine learning repository. The source code for RESEARCH ARTICLE Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster Olufemi Tony Aromolaran1,2 *, Itunu Isewon1,2, Eunice Adedeji2,3, Marcus Oswald4,5, Ezekiel Adebiyi1,2, Rainer Koenig4,5, Jelili Oyelade1,2 * 1 Department of Computer & Information Sciences, Covenant University, Ota, Ogun State, Nigeria, 2 Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria, 3 Department of Biochemistry, Covenant University, Ota, Ogun State, Nigeria, 4 Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Am Klinikum, Jena, Germany, 5 Institute of Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum, Jena, Germany * [ola.oyelade@covenantuniversity.edu.ng](ola.oyelade@covenantuniversity.edu.ng) (JO); [olufemi.aromolaran@stu.edu.cu.ng](olufemi.aromolaran@stu.edu.cu.ng) (OTA) Abstract Computational prediction of absolute essential genes using machine learning has gained wide attention in recent years. However, essential genes are mostly conditional and not absolute. Experimental techniques provide a reliable approach of identifying conditionally essential genes; however, experimental methods are laborious, time and resource consuming, hence computational techniques have been used to complement the experimental methods. Computational techniques such as supervised machine learning, or flux balance analysis are grossly limited due to the unavailability of required data for training the model or simulating the conditions for gene essentiality. This study developed a heuristic-enabled active machine learning method based on a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. We proposed a new sampling selection technique and introduced a heuristic function which replaces the human component in traditional active learning models. The heuristic function dynamically selects the unlabelled samples to improve the performance of the classifier in the next iteration. Testing the proposed model with four benchmark datasets, the proposed model showed superior performance when compared to traditional active learning models (random sampling and uncertainty sampling) . Applying the model to identify conditionally essential genes, four novel essential immune response genes

FAQs

Why are computational methods needed to study essential genes?

Essential genes are often conditional, and experimental identification is laborious, time- and resource-consuming. Computational approaches complement experiments but must overcome limitations in available training data and condition simulation.

What is the core idea of the proposed heuristic-enabled active machine learning method?

The method uses a light gradient boosting model with a new sampling selection technique and a heuristic function that dynamically chooses unlabeled samples to improve the classifier in the next iteration.

How were the model predictions validated and interpreted biologically?

The approach was tested on four benchmark datasets and showed superior performance versus traditional active learning strategies. Predicted conditionally essential genes were further analyzed using functional enrichment to reveal significantly enriched immune response and embryonic developmental processes.

Aug 5, 2026

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