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Article ; Online: Explainable Artificial Intelligence in Endocrinological Medical Research.

Webb-Robertson, Bobbie-Jo M

The Journal of clinical endocrinology and metabolism

2021 Volume 106, Issue 7, Page(s) e2809–e2810

MeSH term(s)	Artificial Intelligence ; Biomedical Research ; Endocrinology ; Humans
Language	English
Publishing date	2021-04-30
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	3029-6
ISSN	1945-7197 ; 0021-972X
ISSN (online)	1945-7197
ISSN	0021-972X
DOI	10.1210/clinem/dgab237
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Article: A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Alcazar, Oscar / Chuang, Sung-Ting / Ren, Gang / Ogihara, Mitsunori / Webb-Robertson, Bobbie-Jo M / Nakayasu, Ernesto S / Buchwald, Peter / Abdulreda, Midhat H

bioRxiv : the preprint server for biology

2024

Abstract: Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the ... ...

Abstract	Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.
Language	English
Publishing date	2024-02-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.02.09.579673
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Article ; Online: Missing data in multi-omics integration: Recent advances through artificial intelligence.

Flores, Javier E / Claborne, Daniel M / Weller, Zachary D / Webb-Robertson, Bobbie-Jo M / Waters, Katrina M / Bramer, Lisa M

Frontiers in artificial intelligence

2023 Volume 6, Page(s) 1098308

Abstract: Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the ... ...

Abstract	Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
Language	English
Publishing date	2023-02-09
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2023.1098308
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Article: Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.

Degnan, David J / Flores, Javier E / Brayfindley, Eva R / Paurus, Vanessa L / Webb-Robertson, Bobbie-Jo M / Clendinen, Chaevien S / Bramer, Lisa M

Metabolites

2023 Volume 13, Issue 10

Abstract: Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular ... ...

Abstract	Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.
Language	English
Publishing date	2023-10-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo13101101
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Article ; Online: malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data.

Leach, Damon T / Stratton, Kelly G / Irvahn, Jan / Richardson, Rachel / Webb-Robertson, Bobbie-Jo M / Bramer, Lisa M

Analytical chemistry

2023 Volume 95, Issue 33, Page(s) 12195–12199

Abstract: Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of ... ...

Abstract	Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.
MeSH term(s)	Chromatography, Liquid/methods ; Algorithms ; Mass Spectrometry/methods ; Gas Chromatography-Mass Spectrometry ; Research Design
Language	English
Publishing date	2023-08-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.3c01289
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Article ; Online: Missing data in multi-omics integration

Javier E. Flores / Daniel M. Claborne / Zachary D. Weller / Bobbie-Jo M. Webb-Robertson / Katrina M. Waters / Lisa M. Bramer

Frontiers in Artificial Intelligence, Vol

Recent advances through artificial intelligence

2023 Volume 6

Abstract: Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the ... ...

Abstract	Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
Keywords	data integration ; missing data ; multi-omics ; multi-view ; artificial intelligence ; machine learning ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Leach, Damon T. / Stratton, Kelly G. / Irvahn, Jan / Richardson, Rachel / Webb-Robertson, Bobbie-Jo M. / Bramer, Lisa M.

Analytical Chemistry. 2023 Aug. 08, v. 95, no. 33 p.12195-12199

2023

Abstract	Mass spectrometry is a powerful tool for identifying and analyzing biomolecules such as metabolites and lipids in complex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly involve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctuation, imprecise timing, column degradation, and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for omics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package, which consolidates 11 common batch effect correction methods for omics data into one place so users can easily implement and compare the following: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveICA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include the batch effect correction in a pmartR workflow without needing any additional data manipulation.
Keywords	analytical chemistry ; gas chromatography-mass spectrometry ; liquid chromatography ; metabolites ; temperature
Language	English
Dates of publication	2023-0808
Size	p. 12195-12199.
Publishing place	American Chemical Society
Document type	Article ; Online
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.3c01289
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

Stratton, Kelly G / Claborne, Daniel M / Degnan, David J / Richardson, Rachel E / White, Amanda M / McCue, Lee Ann / Webb-Robertson, Bobbie-Jo M / Bramer, Lisa M

Journal of proteome research

2023

Abstract: PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of ... ...

Abstract	PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the
Language	English
Publishing date	2023-12-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.3c00512
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Article ; Online: pmartR 2.0

Degnan, David J / Stratton, Kelly G / Richardson, Rachel / Claborne, Daniel / Martin, Evan A / Johnson, Nathan A / Leach, Damon / Webb-Robertson, Bobbie-Jo M / Bramer, Lisa M

Journal of proteome research

2023 Volume 22, Issue 2, Page(s) 570–576

Abstract: ... ...

Abstract	The
MeSH term(s)	Proteomics/methods ; Software ; Metabolomics/methods ; Gene Expression Profiling/methods ; Quality Control
Language	English
Publishing date	2023-01-09
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.2c00610
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Article ; Online: Genetic variants in the complement system and their potential link in the aetiology of type 1 diabetes.

Onengut-Gumuscu, Suna / Webb-Robertson, Bobbie-Jo M / Sarkar, Soumyadeep / Manichaikul, Ani / Hu, Xiaowei / Frazer-Abel, Ashley / Holers, V Michael / Rewers, Marian J / Rich, Stephen S

Diabetes/metabolism research and reviews

2023 Volume 40, Issue 1, Page(s) e3716

Abstract: Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin-secreting beta cells in the pancreas. This process results in life-long dependence on exogenous insulin for survival. Both genetic and environmental factors play a ...

Abstract	Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin-secreting beta cells in the pancreas. This process results in life-long dependence on exogenous insulin for survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre-clinical through to clinically apparent disease.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 1/genetics ; Pancreas/metabolism ; Insulin-Secreting Cells/metabolism ; Insulin/metabolism
Chemical Substances	Insulin
Language	English
Publishing date	2023-08-30
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1470192-3
ISSN	1520-7560 ; 1520-7552
ISSN (online)	1520-7560
ISSN	1520-7552
DOI	10.1002/dmrr.3716
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