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Article ; Online: Challenges and opportunities for omics-based precision medicine in chronic low back pain.

Firdous, Ayesha / Gopalakrishnan, Vanathi / Vo, Nam / Sowa, Gwendolyn

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

2022

Abstract: Purpose: Chronic low back pain (cLBP) is a common health condition worldwide and a leading cause of disability with an estimated lifetime prevalence of 80-90% in industrialized countries. However, we have had limited success in treating cLBP likely due ... ...

Abstract	Purpose: Chronic low back pain (cLBP) is a common health condition worldwide and a leading cause of disability with an estimated lifetime prevalence of 80-90% in industrialized countries. However, we have had limited success in treating cLBP likely due to its non-specific heterogeneous nature that goes beyond detectable anatomical changes. We propose that omics technologies as precision medicine tools are well suited to provide insight into its pathophysiology and provide diagnostic markers and therapeutic targets. Therefore, in this review, we explore the current state of omics technologies in the diagnosis and classification of cLBP. We identify factors that may serve as markers to differentiate between acute and chronic cases of low back pain (LBP). Finally, we also discuss some challenges that must be overcome to successfully apply precision medicine to the diagnosis and treatment of cLBP. Methods: A literature search for the current applications of omics technologies to chronic low back pain was performed using the following search terms- "back pain," "low back pain," "proteomics," "transcriptomics", "epigenomics," "genomics," "omics." We reviewed molecular markers identified from 35 studies which hold promise in providing information regarding molecular insights into cLBP. Results: GWAS studies have found evidence for the role of single nucleotide polymorphisms (SNPs) associated with pain pathways in individuals with cLBP. Epigenomic modifications in patients with cLBP have been found to be enriched among genes involved in immune signaling and inflammation. Transcriptomics profiles of patients with cLBP show multiple lines of evidence for the role of inflammation in cLBP. The glycomics profiles of patients with cLBP are similar to those of patients with inflammatory conditions. Proteomics and microbiomics show promise but have limited studies currently. Conclusion: Omics technologies have identified associations between inflammatory and pain pathways in the pathophysiology of cLBP. However, in order to integrate information across the range of studies, it is important for the field to identify and adopt standardized definitions of cLBP and control patients. Additionally, most papers have applied a single omics method to a sampling of cLBP patients which have yielded limited insight into the pathophysiology of cLBP. Therefore, we recommend a multi-omics approach applied to large global consortia for advancing subphenotyping and better management of cLBP, via improved identification of diagnostic markers and therapeutic targets.
Language	English
Publishing date	2022-12-24
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1115375-1
ISSN	1432-0932 ; 0940-6719
ISSN (online)	1432-0932
ISSN	0940-6719
DOI	10.1007/s00586-022-07457-8
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Article: Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery.

Balasubramanian, Jeya Balaji / Gopalakrishnan, Vanathi

World journal of clinical oncology

2018 Volume 9, Issue 5, Page(s) 98–109

Abstract: Aim: To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.: Methods: Bayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search ... ...

Abstract	Aim: To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine. Methods: Bayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks (BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL Results: We evaluated the degree of incorporation of prior knowledge into BRL Conclusion: BRL
Language	English
Publishing date	2018-08-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2587357-X
ISSN	2218-4333
ISSN	2218-4333
DOI	10.5306/wjco.v9.i5.98
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Article ; Online: A novel approach to modeling multifactorial diseases using Ensemble Bayesian Rule classifiers.

Balasubramanian, Jeya Balaji / Boes, Rebecca D / Gopalakrishnan, Vanathi

Journal of biomedical informatics

2020 Volume 107, Page(s) 103455

Abstract: Modeling factors influencing disease phenotypes, from biomarker profiling study datasets, is a critical task in biomedicine. Such datasets are typically generated from high-throughput 'omic' technologies, which help examine disease mechanisms at an ... ...

Abstract	Modeling factors influencing disease phenotypes, from biomarker profiling study datasets, is a critical task in biomedicine. Such datasets are typically generated from high-throughput 'omic' technologies, which help examine disease mechanisms at an unprecedented resolution. These datasets are challenging because they are high-dimensional. The disease mechanisms they study are also complex because many diseases are multifactorial, resulting from the collective activity of several factors, each with a small effect. Bayesian rule learning (BRL) is a rule model inferred from learning Bayesian networks from data, and has been shown to be effective in modeling high-dimensional datasets. However, BRL is not efficient at modeling multifactorial diseases since it suffers from data fragmentation during learning. In this paper, we overcome this limitation by implementing and evaluating three types of ensemble model combination strategies with BRL- uniform combination (UC; same as Bagging), Bayesian model averaging (BMA), and Bayesian model combination (BMC)- collectively called Ensemble Bayesian Rule Learning (EBRL). We also introduce a novel method to visualize EBRL models, called the Bayesian Rule Ensemble Visualizing tool (BREVity), which helps extract interpret the most important rule patterns guiding the predictions made by the ensemble model. Our results using twenty-five public, high-dimensional, gene expression datasets of multifactorial diseases, suggest that, both EBRL models using UC and BMC achieve better predictive performance than BMA and other classic machine learning methods. Furthermore, BMC is found to be more reliable than UC, when the ensemble includes sub-optimal models resulting from the stochasticity of the model search process. Together, EBRL and BREVity provides researchers a promising and novel tool for modeling multifactorial diseases from high-dimensional datasets that leverages strengths of ensemble methods for predictive performance, while also providing interpretable explanations for its predictions.
MeSH term(s)	Bayes Theorem ; Machine Learning
Language	English
Publishing date	2020-06-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2020.103455
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Article: An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

Liu, Yuzhe / Gopalakrishnan, Vanathi

Data

2017 Volume 2, Issue 1

Abstract: Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been ...

Abstract	Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.
Language	English
Publishing date	2017-01-25
Publishing country	Switzerland
Document type	Journal Article
ISSN	2306-5729
ISSN	2306-5729
DOI	10.3390/data2010008
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Article: Novel Application of Junction Trees to the Interpretation of Epigenetic Differences among Lung Cancer Subtypes.

Pineda, Arturo Lopez / Gopalakrishnan, Vanathi

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

2015 Volume 2015, Page(s) 31–35

Abstract: In this era of precision medicine, understanding the epigenetic differences in lung cancer subtypes could lead to personalized therapies by possibly reversing these alterations. Traditional methods for analyzing microarray data rely on the use of known ... ...

Abstract	In this era of precision medicine, understanding the epigenetic differences in lung cancer subtypes could lead to personalized therapies by possibly reversing these alterations. Traditional methods for analyzing microarray data rely on the use of known pathways. We propose a novel workflow, called Junction trees to Knowledge (J2K) framework, for creating interpretable graphical representations that can be derived directly from in silico analysis of microarray data. Our workflow has three steps, preprocessing (discretization and feature selection), construction of a Bayesian network and, its subsequent transformation into a Junction tree. We used data from the Cancer Genome Atlas to perform preliminary analyses of this J2K framework. We found relevant cliques of methylated sites that are junctions of the network along with potential methylation biomarkers in the lung cancer pathogenesis.
Language	English
Publishing date	2015-03-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2676378-3
ISSN	2153-4063
ISSN	2153-4063
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Article: Efficient Processing of Models for Large-scale Shotgun Proteomics Data.

Grover, Himanshu / Gopalakrishnan, Vanathi

International conference on collaborative computing : networking, applications and worksharing (CollaborateCom). International Conference on Collaborative Computing: Networking, Applications, and Worksharing

2014 Volume 2012, Page(s) 591–596

Abstract: Mass-spectrometry (MS) based proteomics has become a key enabling technology for the systems approach to biology, providing insights into the protein complement of an organism. Bioinformatics analyses play a critical role in interpretation of large, and ... ...

Abstract	Mass-spectrometry (MS) based proteomics has become a key enabling technology for the systems approach to biology, providing insights into the protein complement of an organism. Bioinformatics analyses play a critical role in interpretation of large, and often replicated, MS datasets generated across laboratories and institutions. A significant amount of computational effort in the workflow is spent on the identification of protein and peptide components of complex biological samples, and consists of a series of steps relying on large database searches and intricate scoring algorithms. In this work, we share our efforts and experience in efficient handling of these large MS datasets through database indexing and parallelization based on multiprocessor architectures. We also identify important challenges and opportunities that are relevant specifically to the task of peptide and protein identification, and more generally to similar multi-step problems that are inherently parallelizable.
Language	English
Publishing date	2014-09-16
Publishing country	United States
Document type	Journal Article
DOI	10.4108/icst.collaboratecom.2012.250716
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Article ; Online: Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure.

Adduri, Raju S R / Vasireddy, Ravikiran / Mroz, Margaret M / Bhakta, Anisha / Li, Yang / Chen, Zhe / Miller, Jeffrey W / Velasco-Alzate, Karen Y / Gopalakrishnan, Vanathi / Maier, Lisa A / Li, Li / Konduru, Nagarjun V

International archives of occupational and environmental health

2022 Volume 95, Issue 8, Page(s) 1785–1796

Abstract: Purpose: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not ... ...

Abstract	Purpose: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment. In a cohort of workers with Be exposure, we employed blood plasma extracellular vesicles (EVs) to discover novel biomarkers of exposure to Be. Methods: EVs were isolated from plasma using size-exclusion chromatography and subjected to mass spectrometry-based proteomics. A protein-based classifier was developed using LASSO regression and validated by ELISA. Results: We discovered a dual biomarker signature comprising zymogen granule protein 16B and putative protein FAM10A4 that differentiated between Be-exposed and -unexposed subjects. ELISA-based quantification of the biomarkers in an independent cohort of samples confirmed higher expression of the signature in the Be-exposed group, displaying high predictive accuracy (AUROC = 0.919). Furthermore, the biomarkers efficiently discriminated high- and low-exposure groups (AUROC = 0.749). Conclusions: This is the first report of EV biomarkers associated with Be exposure and exposure levels. The biomarkers could be implemented in resource-limited settings for Be exposure assessment.
MeSH term(s)	Beryllium/metabolism ; Biomarkers ; Extracellular Vesicles/chemistry ; Extracellular Vesicles/metabolism ; Humans ; Mass Spectrometry ; Proteomics/methods
Chemical Substances	Biomarkers ; Beryllium (OW5102UV6N)
Language	English
Publishing date	2022-05-12
Publishing country	Germany
Document type	Journal Article
ZDB-ID	129038-1
ISSN	1432-1246 ; 0340-0131 ; 0367-9977
ISSN (online)	1432-1246
ISSN	0340-0131 ; 0367-9977
DOI	10.1007/s00420-022-01871-7
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Article ; Online: Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models.

Liu, Yuzhe / Manners, Jody / Bittar, Yazan / Chou, Sherry H-Y / Gopalakrishnan, Vanathi

PloS one

2019 Volume 14, Issue 8, Page(s) e0220283

Abstract: Finding optimal blood pressure (BP) target and BP treatment after acute ischemic or hemorrhagic strokes is an area of controversy and a significant unmet need in the critical care of stroke victims. Numerous large prospective clinical trials have been ... ...

Abstract	Finding optimal blood pressure (BP) target and BP treatment after acute ischemic or hemorrhagic strokes is an area of controversy and a significant unmet need in the critical care of stroke victims. Numerous large prospective clinical trials have been done to address this question but have generated neutral or conflicting results. One major limitation that may have contributed to so many neutral or conflicting clinical trial results is the "one-size fit all" approach to BP targets, while the optimal BP target likely varies between individuals. We address this problem with the Acute Intervention Model of Blood Pressure (AIM-BP) framework: an individualized, human interpretable model of BP and its control in the acute care setting. The framework consists of two components: one, a model of BP homeostasis and the various effects that perturb it; and two, a parameter estimator that can learn clinically important model parameters on a patient by patient basis. By estimating the parameters of the AIM-BP model for a given patient, the effectiveness of antihypertensive medication can be quantified separately from the patient's spontaneous BP trends. We hypothesize that the AIM-BP is a sufficient framework for estimating parameters of a homeostasis perturbation model of a stroke patient's BP time course and the AIM-BP parameter estimator can do so as accurately and consistently as a state-of-the-art maximum likelihood estimation method. We demonstrate that this is the case in a proof of concept of the AIM-BP framework, using simulated clinical scenarios modeled on stroke patients from real world intensive care datasets.
MeSH term(s)	Aged ; Blood Pressure ; Critical Care/methods ; Humans ; Intracranial Hemorrhages/complications ; Linear Models ; Male ; Precision Medicine/methods ; Stroke/complications ; Stroke/physiopathology ; Stroke/therapy
Language	English
Publishing date	2019-08-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0220283
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Article ; Online: sfDM

Rafael Ceschin / Ashok Panigrahy / Vanathi Gopalakrishnan

Cancer Informatics, Vol 2015, Iss Suppl. 2, Pp 1-

Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping

2015 Volume 9

Keywords	Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282 ; Internal medicine ; RC31-1245 ; Medicine ; R
Language	English
Publishing date	2015-02-01T00:00:00Z
Publisher	Libertas Academica
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Towards precision critical care management of blood pressure in hemorrhagic stroke patients using dynamic linear models.

Yuzhe Liu / Jody Manners / Yazan Bittar / Sherry H-Y Chou / Vanathi Gopalakrishnan

PLoS ONE, Vol 14, Iss 8, p e

2019 Volume 0220283

Abstract	Finding optimal blood pressure (BP) target and BP treatment after acute ischemic or hemorrhagic strokes is an area of controversy and a significant unmet need in the critical care of stroke victims. Numerous large prospective clinical trials have been done to address this question but have generated neutral or conflicting results. One major limitation that may have contributed to so many neutral or conflicting clinical trial results is the "one-size fit all" approach to BP targets, while the optimal BP target likely varies between individuals. We address this problem with the Acute Intervention Model of Blood Pressure (AIM-BP) framework: an individualized, human interpretable model of BP and its control in the acute care setting. The framework consists of two components: one, a model of BP homeostasis and the various effects that perturb it; and two, a parameter estimator that can learn clinically important model parameters on a patient by patient basis. By estimating the parameters of the AIM-BP model for a given patient, the effectiveness of antihypertensive medication can be quantified separately from the patient's spontaneous BP trends. We hypothesize that the AIM-BP is a sufficient framework for estimating parameters of a homeostasis perturbation model of a stroke patient's BP time course and the AIM-BP parameter estimator can do so as accurately and consistently as a state-of-the-art maximum likelihood estimation method. We demonstrate that this is the case in a proof of concept of the AIM-BP framework, using simulated clinical scenarios modeled on stroke patients from real world intensive care datasets.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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