Mill Valley, California, United States
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Perplexity made it to the Forbes Cloud 100 top companies list today. Thanks to NASDAQ for this shoutout in the Times Square. In terms of hitting the…
Perplexity made it to the Forbes Cloud 100 top companies list today. Thanks to NASDAQ for this shoutout in the Times Square. In terms of hitting the…
Liked by Vishal Patel, MD, PhD
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Please join me at the 2024 Global Wellness Summit in St. Andrews, Scotland, November 4-7, where leaders and visionaries from around the world will…
Please join me at the 2024 Global Wellness Summit in St. Andrews, Scotland, November 4-7, where leaders and visionaries from around the world will…
Liked by Vishal Patel, MD, PhD
Experience & Education
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Publications
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Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: A case study of azathioprine
Journal of Biomedical Informatics
Objective
To demonstrate the use of aggregated and de-identified electronic health record (EHR) data for multivariate post-marketing pharmacosurveillance in a case study of azathioprine (AZA).
Other authorsSee publication -
Network Signatures of Survival in Glioblastoma Multiforme
PLoS Computational Biology
To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature…
To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included "protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.
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Colorectal Cancer and Its Molecular Subsystems: Construction, Interpretation, and Validation
Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany.
In this chapter, we begin by introducing a specific type of cancer – colorectal cancer – and the bioinformatic tools applied in the analysis of colorectal tumor specimens. We then introduce an emerging approach for analyzing multidimensional datasets, and we refer to the resulting construct as a “molecular subsystem.” We describe relevant issues related to the construction, interpretation, and validation of these subsystems. In particular, we provide a worked example at the end of chapter to…
In this chapter, we begin by introducing a specific type of cancer – colorectal cancer – and the bioinformatic tools applied in the analysis of colorectal tumor specimens. We then introduce an emerging approach for analyzing multidimensional datasets, and we refer to the resulting construct as a “molecular subsystem.” We describe relevant issues related to the construction, interpretation, and validation of these subsystems. In particular, we provide a worked example at the end of chapter to illustrate the statistical concerns in constructing a molecular subsystem from proteomic data.
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Azathioprine-Induced Comorbidity Network Reveals Patterns and Predictors of Nephrotoxicity and Neutrophilia
2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB)
We sought to examine the frequencies and patterns of nephrotoxicity and neutrophilia due to azathioprine (AZA), and to develop a prototype method for using large de-identified electronic health record (EHR) data to aid in post-market drug surveillance. We leveraged a de-identified database of over 10 million patient EHRs to construct a network of comorbidities induced by administration of AZA, where comorbidities were defined by baseline-controlled laboratory values. To gauge the significance…
We sought to examine the frequencies and patterns of nephrotoxicity and neutrophilia due to azathioprine (AZA), and to develop a prototype method for using large de-identified electronic health record (EHR) data to aid in post-market drug surveillance. We leveraged a de-identified database of over 10 million patient EHRs to construct a network of comorbidities induced by administration of AZA, where comorbidities were defined by baseline-controlled laboratory values. To gauge the significance of the identified disease patterns, we calculated the relative risk of developing a comorbidity pair relative to a control cohort of patients taking one of 12 other anti-rheumatic agents. Nephrotoxicity as gauged by elevations in creatinine was present in 11% of patients taking AZA, and this frequency was significantly higher than in patients taking other anti-rheumatic agents (RR: 1.2, 95% CI: 1.04-1.43). Neutrophilia was highly prevalent (45%) in the population and was also unique to AZA (RR: 1.2, 95% CI: 1.17-1.28). Using a comorbidity network analysis, we hypothesized that the joint consideration of anemia (hemoglobin 190 IU/L) may serve as a predictor of impending renal dysfunction. Indeed, these two laboratory values provide approximately 100% sensitivity in predicting subsequent elevations in creatinine. Furthermore, the predictive power is unique to AZA, for jointly considering anemia and an elevated LDH provides only 50% sensitivity in predicting creatinine elevations with other anti-rheumatic agents. Our work demonstrates that the construction of comorbidity networks from de-identified EHR data sets can provide both sufficient insight and statistical power to uncover novel patterns and predictors of disease.
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BiC: a web server for calculating bimodality of coexpression between gene and protein networks
Bioinformatics
Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool…
Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data.
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PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling
BMC Bioinformatics
Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc. Computational methods are, thus, required to predict which pathways are likely…
Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc. Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed. We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc1638N+/-mouse to test the network-based hypotheses. The predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.
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Prediction and Testing of Biological Networks Underlying Intestinal Cancer
PLoS ONE
Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a…
Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.
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Languages
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Gujarati
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More activity by Vishal
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Congratulations to Simone Biles for winning gold in the women's gymnastics all-around competition today at the 2024 Olympics in Paris! We were so…
Congratulations to Simone Biles for winning gold in the women's gymnastics all-around competition today at the 2024 Olympics in Paris! We were so…
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Much of dementia—perhaps half—could be prevented or substantially delayed with attention to 14 modifiable risk factors The Lancet Commission 2024…
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I’ve long believed that we are largely in control of our own state. There’s a misperception that states just “happen to you.” And in some ways that’s…
I’ve long believed that we are largely in control of our own state. There’s a misperception that states just “happen to you.” And in some ways that’s…
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I'm excited to share that my company, Vysual, is now open for business! We've developed a unique AI-driven platform to help nonprofits and…
I'm excited to share that my company, Vysual, is now open for business! We've developed a unique AI-driven platform to help nonprofits and…
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Yes, nothing more frustrating than seeing an awesome piece of software you contributed to sitting on a digital shelf, gathering digital dust…
Yes, nothing more frustrating than seeing an awesome piece of software you contributed to sitting on a digital shelf, gathering digital dust…
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The chair of the Global Wellness Summit, Susie Ellis has been fundamental in defining the spa and wellness industry and continues to drive it…
The chair of the Global Wellness Summit, Susie Ellis has been fundamental in defining the spa and wellness industry and continues to drive it…
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We have no clinical test to assess our immune system status. The case for an immunome. https://lnkd.in/dpfvq6nx
We have no clinical test to assess our immune system status. The case for an immunome. https://lnkd.in/dpfvq6nx
Liked by Vishal Patel, MD, PhD
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It was incredible to be a part of this impactful commercial. Thank you FIGS for your support of the amazing humans I have the privilege of working…
It was incredible to be a part of this impactful commercial. Thank you FIGS for your support of the amazing humans I have the privilege of working…
Liked by Vishal Patel, MD, PhD
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When I was in graduate school, a Kripalu program felt financially out-of-reach even though it was directly in line with who I wanted to be. See…
When I was in graduate school, a Kripalu program felt financially out-of-reach even though it was directly in line with who I wanted to be. See…
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One of my favorite ChatGPT / Claude hacks: answer levels. Give a level 1, level 2, and level 3 answer. - A level 1 answer is boring, blah…
One of my favorite ChatGPT / Claude hacks: answer levels. Give a level 1, level 2, and level 3 answer. - A level 1 answer is boring, blah…
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We agree on great potential for #AI to improve accuracy of medical diagnoses https://lnkd.in/gnWbKN4J My piece in Science https://lnkd.in/gRsM84ku
We agree on great potential for #AI to improve accuracy of medical diagnoses https://lnkd.in/gnWbKN4J My piece in Science https://lnkd.in/gRsM84ku
Liked by Vishal Patel, MD, PhD
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We’re thrilled to announce Yuki Kiyono as a panelist for the Destination Deluxe Awards & Wellness Day 2024 on September 19! Yuki Kiyono, the…
We’re thrilled to announce Yuki Kiyono as a panelist for the Destination Deluxe Awards & Wellness Day 2024 on September 19! Yuki Kiyono, the…
Liked by Vishal Patel, MD, PhD
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GREAT IMMIGRANTS * GREAT AMERICANS "Every Fourth of July, Carnegie Corporation of New York celebrates remarkable Americans — all naturalized…
GREAT IMMIGRANTS * GREAT AMERICANS "Every Fourth of July, Carnegie Corporation of New York celebrates remarkable Americans — all naturalized…
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