Network-Based Computational Modeling to Unravel Gene Essentiality

Granata, Ilaria; Giordano, Maurizio; Maddalena, Lucia; Manzo, Mario; Guarracino, Mario Rosario

doi:10.1007/978-3-031-33050-6_3

Cited by 3 publications

(9 citation statements)

References 70 publications

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“…In our previous works, using a different labelling strategy based on a knowledge-driven subdivision of CRISPR scores (CS), we identified the best configuration of E and NE genes by ML trials. We achieved the best performance training a model on biological and embedding attributes (CS0 (E) vs CS6-9 (NE): BA=0.844 [36]; CS0 (E) vs CS6-9 (NE): BA=0.846 [37]). The investigation of the intermediate groups led us to think about possibly overcoming the dichotomic view of essentiality.…”

Section: Discussionmentioning

confidence: 99%

“…This different localisation was also evident by visualizing the three groups in the PPI networks, which seems to reproduce the picture of a human cell, with the E genes localised in the core of the network, aE in the surrounding area and sNE widely spread in the cytoplasm and at the borders. Our recent work demonstrated that even integrating the PPI with a metabolic network to add a functional centrality to the physical one, the contribution was totally in charge of the PPI [37]. This can likely be explained by the fact that while the metabolic machinery comprises several alternative paths to achieve a specific objective, the lack of a component involved in many physical complexes and interactions is hard to tolerate.…”

Section: Discussionmentioning

confidence: 99%

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HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Granata,

Maddalena,

Manzo

et al. 2024

Preprint

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Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the state-of-the-art EG annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of EGs while confirming the existence of essentiality nuances.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Granata,

Maddalena,

Manzo

et al. 2024

Preprint

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show abstract

“…Another way of deriving thresholds to divide EGs from nEGs is by using a knowledge-driven approach. Motivated by the results in [15], we employed this approach in [41,42] to provide EG labels subsequently used for predictive experiments. In particular, the CRISPR scores (CS) were divided into eleven groups from CS0 to CS10, and the labels vector was obtained by assigning the label of the gene to the most frequent score group among the cell lines.…”

Section: Identification Methodsmentioning

confidence: 99%

“…Recently, several reviews have been devoted to computational methods for the prediction of EGs [42][43][44][45][46]. A concise summary highlighting the taxonomies proposed and the period covered by the reviewed literature is given in Table 2.…”

Section: Predictive Modelsmentioning

confidence: 99%

“…As previously mentioned, node centrality measures are correlated with the essentiality property. Various types of topology measures, based on neighbourhood, path, or eigenvector information, or their combination, are taken into account [42,47]. Also, network embedding [41,42,[48][49][50][51][52] is frequently adopted to exploit the underlying network information, encoded into a low-dimensional space.…”

Section: Predictive Modelsmentioning

confidence: 99%

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Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

Giordano,

Falbo,

Maddalena

et al. 2023

Biomolecules

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Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context. Because of their importance in vital biological processes, recognising context-specific EGs (csEGs) could help for identifying new potential pharmacological targets and to improve precision therapeutics. Since most of the computational procedures proposed to identify and predict EGs neglect their context-specificity, we focused on this aspect, providing a theoretical and experimental overview of the literature, data and computational methods dedicated to recognising csEGs. To this end, we adapted existing computational methods to exploit a specific context (the kidney tissue) and experimented with four different prediction methods using the labels provided by four different identification approaches. The considerations derived from the analysis of the obtained results, confirmed and validated also by further experiments for a different tissue context, provide the reader with guidance on exploiting existing tools for achieving csEGs identification and prediction.

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HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Granata,

Maddalena,

Manzo

et al. 2024

PLoS Comput Biol

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Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data. We propose HELP (Human Gene Essentiality Labelling & Prediction), a computational framework for labelling and predicting essential genes. Its double scope allows for identifying genes based on dependency or not on experimental data. The effectiveness of the labelling method was demonstrated by comparing it with other approaches in overlapping the reference sets of essential gene annotations, where HELP demonstrated the best compromise between false and true positive rates. The gene attributes, including multi-omics and network embedding features, lead to high-performance prediction of essential genes while confirming the existence of essentiality nuances.

show abstract

Network-Based Computational Modeling to Unravel Gene Essentiality

Cited by 3 publications

References 70 publications

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience

HELP: A computational framework for labelling and predicting human common and context-specific essential genes

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