Machine learning approach to gene essentiality prediction: a review

Aromolaran, Olufemi; Aromolaran, Damilare; Isewon, Itunuoluwa; Oyelade, Jelili

doi:10.1093/bib/bbab128

Cited by 68 publications

(45 citation statements)

References 134 publications

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“…Despite an increased interest in machine learning for essentiality prediction, current methods still suffer from limitations in their accuracy and ability to generalize across environmental conditions or species 6 . This is partly due to the lack of gene featurization strategies that are predictive of essentiality.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the cost of such screens, there is substantial interest in computational methods that can rapidly explore the impact of gene deletions and complement current experimental efforts to determine gene essentiality. Such approaches typically employ machine learning in combination with various properties such as sequence homology and gene-function ontologies 5,6 .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of gene essentiality using machine learning and genome-scale metabolic models

Freischem

Barahona

Oyarzún

2022

Preprint

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The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for gene essentiality prediction. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach implicitly assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large changes in cell physiology to meet other objectives for survival. Here we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of gene essentiality using machine learning and genome-scale metabolic models

Freischem

Barahona

Oyarzún

2022

Preprint

View full text Add to dashboard Cite

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“…Some of the cited methods with higher observed AUC use alternative features (e.g., network topology, gene ontology-based features, more gene expression) and larger training set sizes. Our method can be improved by exploring some of these alternative features (see recent review [66]), as was seen in Figure 3 where an expanded feature set provided modest improvement in overall mean and variance of the AUC. Furthermore, exploration of what types of genes are misclassified (Tables S1 and S2) may help suggest the types of features that should be included.…”

Section: Discussionmentioning

confidence: 99%

Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study

Daniels

Dvorkin²,

Powers

et al. 2021

MCA

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Integrating gene-level data is useful for predicting the role of genes in biological processes. This problem has typically focused on supervised classification, which requires large training sets of positive and negative examples. However, training data sets that are too small for supervised approaches can still provide valuable information. We describe a hierarchical mixture model that uses limited positively labeled gene training data for semi-supervised learning. We focus on the problem of predicting essential genes, where a gene is required for the survival of an organism under particular conditions. We applied cross-validation and found that the inclusion of positively labeled samples in a semi-supervised learning framework with the hierarchical mixture model improves the detection of essential genes compared to unsupervised, supervised, and other semi-supervised approaches. There was also improved prediction performance when genes are incorrectly assumed to be non-essential. Our comparisons indicate that the incorporation of even small amounts of existing knowledge improves the accuracy of prediction and decreases variability in predictions. Although we focused on gene essentiality, the hierarchical mixture model and semi-supervised framework is standard for problems focused on prediction of genes or other features, with multiple data types characterizing the feature, and a small set of positive labels.

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“…Area under precision-recall curve is a more effective metric than area under receiver optimizer characteristics curve when applied on highly skewed tasks [ 31 , 84 ]. Matthews correlation coefficient [ 106 ] has also been successfully used in SL prediction study [ 24 ] of which the samples are highly imbalanced. Besides, Li et al.…”

Section: Challenges and Future Workmentioning

confidence: 99%

Computational methods, databases and tools for synthetic lethality prediction

Wang¹,

Zhang²,

Han³

et al. 2022

Briefings in Bioinformatics

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Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

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Machine learning approach to gene essentiality prediction: a review

Cited by 68 publications

References 134 publications

Prediction of gene essentiality using machine learning and genome-scale metabolic models

Prediction of gene essentiality using machine learning and genome-scale metabolic models

Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study

Computational methods, databases and tools for synthetic lethality prediction

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