Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

Gardiner, Laura‐Jayne; Rusholme‐Pilcher, Rachel; Colmer, Joshua; Rees, Hannah; Crescente, Juan Manuel; Carrieri, Anna Paola; Duncan, Susan; Pyzer‐Knapp, Edward O.; Krishna, Ritesh; Halll, Anthony

doi:10.1101/2021.02.04.429826

Cited by 4 publications

(2 citation statements)

References 84 publications

(151 reference statements)

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“…Explainable AI helps us to understand the predictions made by ML models and offer insights into the predicted phenotype. Only recently has explainable AI been applied to diverse single omic datasets [ 24 , 25 ], but the potential of this to enable precision medicine has yet to be fully exploited. Here our aim, for IBD patients, is to predict patient-specific drug response and derive insights into the potential biological (genetic or otherwise) basis of this variation in response to enable improvements in the translation of preclinical models to the clinic, potentially informing clinical trial design for novel therapies.…”

Section: Introductionmentioning

confidence: 99%

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

et al. 2022

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Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models’ predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNFα from the fresh IBD tissues in the presence/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNFα levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by ex vivo assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.

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Section: Introductionmentioning

confidence: 99%

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

et al. 2022

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“…Explainable AI helps us to understand the predictions made by ML models and offer insights into the predicted phenotype. Only recently has explainable AI been applied to diverse single omic datasets [24][25], but the potential of this to enable precision medicine has yet to be fully exploited. Here our aim, for IBD patients, is to predict patient-specific drug response and derive insights into the potential biological (genetic or otherwise) basis of this variation in response.…”

Section: Introductionmentioning

confidence: 99%

Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

Gardiner

Bingham

et al. 2021

Preprint

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Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from a complex interaction between the host and environmental factors. Investigating drug efficacy in cultured human fresh IBD tissues can improve our understanding of the reasons why certain medications are more or less effective for different patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to informatively integrate multi-modal data to predict inter-patient specific variation in drug response. Using explanation of our models, we interpret the models' predictions inferring unique combinations of important features associated with human tissue pharmacological responses. The inferred multi-modal features originate from multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data and all are associated with drug efficacy generated by a preclinical human fresh IBD tissue assay. To pharmacologically assess patient variation in response to IBD treatment, we used the reduction in the release of the inflammatory cytokine TNFa; from the fresh IBD tissues in the presence or absence of test drugs, as a measure of drug efficacy. The TNF pathway is a common target in current therapies for IBD; we initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor on the production of TNFa; however, we later show the approach can be applied to other targets, test drugs or mechanisms of interest. Our best model was able to predict TNFa; levels from a combination of integrated demographic, medicinal and genomic features with an error as low as 4.98% on unseen patients. We incorporated transcriptomic data to validate and expand insights from genomic features. Our results showed variations in drug effectiveness between patients that differed in gender, age or condition and linked new genetic polymorphisms in our cohort of IBD patients to variation in response to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models drug response in a relevant human tissue model of IBD while also identifying its most predictive features as part of a transparent ML-based precision medicine strategy.

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Bluster or Lustre: Can AI Improve Crops and Plant Health?

Gardiner¹,

Krishna²

2021

Plants

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In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question—Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.

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Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

Cited by 4 publications

References 84 publications

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

Bluster or Lustre: Can AI Improve Crops and Plant Health?

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