Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples

Listopad, Stanislav; Magnan, Christophe; Asghar, Aliya; Stolz, Andrew; Tayek, John A.; Liu, Zhang–Xu; Morgan, Timothy R.; Norden-Krichmar, Trina M.

doi:10.1016/j.jhepr.2022.100560

Cited by 8 publications

(8 citation statements)

References 43 publications

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“…Since there has been a substantial increase in the number of studies applying artificial intelligence (AI) in medical image analysis for the diagnosis of liver diseases over the past decade, 98 it is also reasonable to hypothesize that AI approaches using imaging-based methodologies will become soon available to develop highly accurate models for predicting disease severity and for non-invasive monitoring of treatment responses in NASH. 98,99 Figure 2 summarizes the beneficial liver-related and cardiometabolic effects of an "ideal" drug candidate for NASH.…”

Section: Imaging-based Methodologiesmentioning

confidence: 99%

NASH drug treatment development: challenges and lessons

Tilg,

Byrne,

Targher

2023

The Lancet Gastroenterology & Hepatology

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Section: Imaging-based Methodologiesmentioning

confidence: 99%

NASH drug treatment development: challenges and lessons

Tilg,

Byrne,

Targher

2023

The Lancet Gastroenterology & Hepatology

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“…Publicly available proteomic data from PBMCs was not available for the conditions in our study, and therefore, only the liver tissue datasets were validated using independent data. Information regarding the RNAseq liver tissue validation dataset can be found in our previous publication [11].…”

Section: Validation Datasetmentioning

confidence: 99%

“…The detailed methods used to classify RNAseq counts and identify best genes are described in [11]. Briefly, the classification was performed using nested cross-validation with feature selection.…”

Section: Rnaseq Classification and Feature Selection Pipelinementioning

confidence: 99%

“…The methods for independent validation were identical for both RNAseq and proteomic datatypes. The further description of these methods can be found in our previous publication [11] methods.…”

Section: Validation In Independent Liver Tissue Datamentioning

confidence: 99%

“…In a previous study, we established that gene expression biomarkers from liver tissue and peripheral mononuclear blood cells (PBMCs) can be used with a multiclass machine learning approach to successfully distinguish between multiple liver diseases [11]. In the present study, in addition to transcriptomic data, we also obtained proteomic data for participants from the same cohort [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning

Listopad,

Magnan,

Day

et al. 2024

PLOS Digit Health

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Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

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Insights into ALD and AUD diagnosis and prognosis: Exploring AI and multimodal data streams

Narayanan,

Wu,

Shah

et al. 2024

Hepatology

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The rapid evolution of artificial intelligence (AI) and the widespread embrace of digital technologies have ushered in a new era of clinical research and practice in hepatology. Although its potential is far from realization, these significant strides have generated new opportunities to address existing gaps in the delivery of care for patients with liver disease. In this review, we discuss how AI and opportunities for multimodal data integration can improve the diagnosis, prognosis and management of alcohol-associated liver disease (ALD). An emphasis is made on how these approaches will also benefit the detection and management of alcohol use disorder (AUD). Our discussion encompasses challenges and limitations, concluding with a glimpse into the promising future of these advancements.

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Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples

Cited by 8 publications

References 43 publications

NASH drug treatment development: challenges and lessons

NASH drug treatment development: challenges and lessons

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning

Insights into ALD and AUD diagnosis and prognosis: Exploring AI and multimodal data streams

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