Correction: Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study

UNSTRUCTURED Description: The corresponding author should be Shenyuan Liu. The 2 and 7 authors’ affiliation missing China. The 3-5 authors’ affiliation should be Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China.

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New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

10.1101/702118 ◽

2019 ◽

Cited By ~ 3

Author(s):

Evan Greene ◽

Greg Finak ◽

Leonard A. D’Amico ◽

Nina Bhardwaj ◽

Candice D. Church ◽

...

Keyword(s):

Machine Learning ◽

Flow Cytometry ◽

T Cell ◽

Single Cell ◽

Cancer Immunotherapy ◽

Effector Memory ◽

Machine Learning Method ◽

Learning Method ◽

Modeling Framework ◽

Interpretable Machine Learning

AbstractHigh-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying it de novo in two additional independent trials. FAUST’s phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we call Phenotypic and Functional Differential Abundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.

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An interpretable machine learning method for detecting novel pathogens

10.21203/rs.2.20477/v1 ◽

2020 ◽

Author(s):

Xiaoyong Zhao ◽

Ningning Wang

Keyword(s):

Machine Learning ◽

Infectious Diseases ◽

Bacterial Pathogen ◽

World Health ◽

Machine Learning Method ◽

Learning Method ◽

Human Pathogens ◽

Healthcare Applications ◽

Interpretable Machine Learning ◽

Shapley Values

Abstract Background: According to the World Health Organization (WHO), infectious diseases continue to one of the leading causes of death worldwide. Since the core microbiota flora of humans is largely diverse and horizontal gene transfer (HGT), it is very challenging to determine whether a particular bacterial strain is commensal or pathogenic to humans. With the latest advances in next-generation sequencing (NGS) technology, bioinformatics tools and techniques using NGS data have increasingly been used for the diagnosis and monitoring of infectious diseases. Even if the biological background is not available, the machine learning method can still infer the pathogenic phenotype from the NGS readings, independent of the database of known organisms, and being studied intensively.However, previous methods have not considered opportunistic pathogenic and interpretability of black box model, are not well suited for clinical requirements. Results:In this study, we proposed a novel interpretable machine learning approach (IMLA) to identify the pathogenicity of bacterial genomes: human pathogens (HP), opportunistic pathogenicity (OHP) or non-pathogenicity(NHP), then use the following model-agnostic interpretation methods to interpret model: feature importance, accumulated local effects and Shapley values, due to the model interpretability is essential for healthcare applications. To our knowledge, our paper is the first attempt to infer opportunistic pathogenicity and explain the model. Conclusions: According to the simulation results, our approach IMLA can be a great addition to detect novel pathogens. Keywords: interpretable; machine learning; bacterial pathogen;

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An interpretable machine learning method for detecting novel pathogens

10.21203/rs.2.20477/v2 ◽

2020 ◽

Author(s):

Xiaoyong Zhao ◽

Ningning Wang

Keyword(s):

Machine Learning ◽

Infectious Diseases ◽

World Health ◽

Machine Learning Method ◽

Learning Method ◽

Human Pathogens ◽

Healthcare Applications ◽

Interpretable Machine Learning ◽

Shapley Values ◽

Opportunistic Pathogenic

Abstract Background: According to the World Health Organization (WHO), infectious diseases continue to one of the leading causes of death worldwide. Since the core microbiota flora of humans is largely diverse and horizontal gene transfer (HGT), it is very challenging to determine whether a particular bacterial strain is commensal or pathogenic to humans. With the latest advances in next-generation sequencing (NGS) technology, bioinformatics tools and techniques using NGS data have increasingly been used for the diagnosis and monitoring of infectious diseases. Even if the biological background is not available, the machine learning method can still infer the pathogenic phenotype from the NGS readings, independent of the database of known organisms, and being studied intensively.However, previous methods have not considered opportunistic pathogenic and interpretability of black box model, are not well suited for clinical requirements. Results :In this study, we proposed a novel interpretable machine learning approach (IMLA) to identify the pathogenicity of bacterial genomes: human pathogens (HP), opportunistic pathogenicity (OHP) or non-pathogenicity(NHP), then use the following model-agnostic interpretation methods to interpret model: feature importance, accumulated local effects and Shapley values, due to the model interpretability is essential for healthcare applications. To our knowledge, our paper is the first attempt to infer opportunistic pathogenicity and explain the model. Conclusions: According to the simulation results, our approach IMLA can be a great addition to detect novel pathogens.

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