The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI

Santorsola, Mariangela; Lescai, Francesco

doi:10.1016/j.nbt.2023.06.002

Cited by 12 publications

(4 citation statements)

References 102 publications

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“…Understanding genotype-phenotype relationships: AI can assist in unravelling the complex relationship between a plant’s genotype and its phenotype in response to environmental conditions. Especially xAI can identify genetic variants that contribute to these traits, particularly those that have non-linear interactions - something that GWAS cannot do ( Santorsola and Lescai, 2023 ). Feed-forward NNs go beyond association testing and can use several individuals with many SNPs to predict traits with an acceptable performance ( Sharma et al., 2020 ).…”

Section: Accelerating Plant Breeding Processes With Explainable Aimentioning

confidence: 99%

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Chang-Brahim,

Koppensteiner,

Beltrame

et al. 2024

Front. Plant Sci.

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Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

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Section: Accelerating Plant Breeding Processes With Explainable Aimentioning

confidence: 99%

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Chang-Brahim,

Koppensteiner,

Beltrame

et al. 2024

Front. Plant Sci.

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“…Moreover, a major aspect in which ML models differ from, and possibly surpass, classic GS models, is their higher explainability. Explainability, defined as the ability to explain how a model works and makes predictions even after it has been trained ( Santorsola and Lescai, 2023 ), can provide information on the genomic sequences that contribute to the observed phenotypic variations. This is relevant for breeding programs and for the overall understanding of biological processes ( Danilevicz et al., 2022 ).…”

Section: Innovative Approaches To Accelerate Varietal Selection In Gr...mentioning

confidence: 99%

Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics

Magon,

De Rosa,

Martina

et al. 2023

Front. Plant Sci.

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The multifaceted nature of climate change is increasing the urgency to select resilient grapevine varieties, or generate new, fitter cultivars, to withstand a multitude of new challenging conditions. The attainment of this goal is hindered by the limiting pace of traditional breeding approaches, which require decades to result in new selections. On the other hand, marker-assisted breeding has proved useful when it comes to traits governed by one or few genes with great effects on the phenotype, but its efficacy is still restricted for complex traits controlled by many loci. On these premises, innovative strategies are emerging which could help guide selection, taking advantage of the genetic diversity within the Vitis genus in its entirety. Multiple germplasm collections are also available as a source of genetic material for the introgression of alleles of interest via adapted and pioneering transformation protocols, which present themselves as promising tools for future applications on a notably recalcitrant species such as grapevine. Genome editing intersects both these strategies, not only by being an alternative to obtain focused changes in a relatively rapid way, but also by supporting a fine-tuning of new genotypes developed with other methods. A review on the state of the art concerning the available genetic resources and the possibilities of use of innovative techniques in aid of selection is presented here to support the production of climate-smart grapevine genotypes.

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“…b. For each feature ‫ݔ‬ , computed the mean SHAP value for CN samples as equation (3), where ܰ ே represents the total number of CN samples in the dataset.…”

Section: Model Interpretationmentioning

confidence: 99%

“…One of the challenges faced by the application of AI approaches to multi-omic data is lack of interpretability. Complex ML models, like Deep Neural Networks (DNNs), although with unparalleled predictive power, are often considered as "black box" models as their decisionmaking processes are not easily inspected by human investigators [3]. Existing literature has reported the use of different AI frameworks to uncover deep interrelationships between gene expression and AD neuropathologies [4,5].…”

Section: Introductionmentioning

confidence: 99%

Interpretable deep learning framework for understanding molecular changes in human brains with Alzheimer’s disease: implications for microglia activation and sex differences

Trivedi,

Joshi,

Shah

et al. 2023

Preprint

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INTRODUCTIONThe objective of this study is to characterize the molecular changes associated with AD from gene expression data of brain tissues taking an interpretable deep learning approach which has not been fully exploited.METHODSWe trained multi-layer perceptron (MLP) models for the classification of neuropathologically confirmed AD vs. controls using the transcriptomic data of three brain regions from the ROSMAP study. The whole disease spectrum was then modeled as a progressive trajectory. SHAP (SHapley Additive exPlanations) value was derived to explain model predictions and identify significant implicated genes for subsequent network analysis of key gene modules underlying AD progression. The framework was validated using two external datasets: the Mayo RNA-seq study cohort and the Mount Sinai Brain Bank study cohort.RESULTSThe MLP models achieved superior performance in classification and prediction in external datasets. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions.DISCUSSIONWe identified common gene signatures in microglia and sex specific modules in neurons that are implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level.Research-in-ContextSystematic review: Postmortem brain transcriptomes have been analyzed to study the molecular changes associated with Alzheimer’s disease, usually by a direct contrast approach such as differential gene expression analysis. Nuanced gene regulations thus cannot be easily pinpointed from convoluted data such as those from bulk-tissue profiling. We applied a novel interpretable deep learning approach to dissect the RNA-seq data collected from three different brain regions of a large clinical cohort and identified significant genes for network analysis implicated for AD.Interpretation: Our models successfully predicted neuropathological and clinical traits in both internal and external validations. We corroborated known microglial biology in addition to revealing novel sex chromosome-linked gene contributing to sex dimorphism in AD.Future directions: The framework could have broader utility of interpreting multi-omics data such as those from single-cell profiling, to advance our understanding of molecular mechanism of complex human disease such as AD.HighlightsWe applied novel interpretable deep learning methods on the postmortem brain transcriptomes from three different brain regionsWe interpreted the models to identify the most important genes implicated for ADNetwork analysis corroborated known microglial biology and revealed novel sex specific transcriptional factors for neuronal loss in AD

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The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI

Cited by 12 publications

References 102 publications

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics

Interpretable deep learning framework for understanding molecular changes in human brains with Alzheimer’s disease: implications for microglia activation and sex differences

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