Simple method for cutoff point identification in descriptive high-throughput biological studies

Suvorov, Alexander

doi:10.1186/s12864-022-08427-6

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…The full list of genes with their corresponding CGI numbers is available through Mendeley Data [ 20 ]. The threshold between the genes highly sensitive to chemical exposures and genes with low sensitivity was determined using a method for the identification of cutoff points in descriptive high-throughput omics studies [ 21 ]. This approach identifies an inflection point in a ranked distribution of variables if this distribution follows a biphasic pattern.…”

Section: Methodsmentioning

confidence: 99%

“…This information for 19,243 genes was downloaded for our analysis. We used the same method for the cutoff point identification as described in the previous paragraph [ 21 ] to determine the threshold between the underexplored (<200 publications/gene) and well-explored (≥200 publications/gene) genes. The 200 publications per gene cutoff corresponds to the maximum inflection point in the biphasic distribution of publication numbers per gene for a ranked list of genes and delineates a big group of genes with small publication numbers and a smaller group of genes with big publication numbers.…”

Section: Methodsmentioning

confidence: 99%

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Underexplored Molecular Mechanisms of Toxicity

Arowolo,

Suvorov

2024

JoX

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Social biases may concentrate the attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in a significant part of the responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play an important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures, we used publicly available databases. First, we used data on the published chemical–gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from the Find My Understudied Genes database. Thresholds were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. A total of 1110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. The metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Underexplored Molecular Mechanisms of Toxicity

Arowolo,

Suvorov

2024

JoX

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“…The full list of genes with their corresponding CGI numbers is available through Mendeley Data [20]. The threshold between genes highly sensitive to chemical exposures and genes with low sensitivity was determined using a method for the identification of cutoff points in descriptive high-throughput omics studies [21]. This approach identifies an inflection point in a ranked distribution of variables if this distribution follows a biphasic pattern.…”

Section: Sensitivity Of Genes To Chemical Exposuresmentioning

confidence: 99%

“…This information for 19,243 genes was downloaded for our analysis. We used the same method for cutoff point identification as described in the previous paragraph [21] to determine the threshold between underexplored (< 200 publications/gene) and well-explored ( 200 publications/gene) genes.…”

Section: Number Of Publications Per Genementioning

confidence: 99%

Underexplored Molecular Mechanisms of Toxicity

Arowolo,

Suvorov

2024

Preprint

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Social biases may concentrate attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in significant part of responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures we used publicly available databases. First, we used data on published chemical-gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from Find My Understudied Genes database. Threshold were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. 1,110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. Metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.

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“…For each of the resulting matrices, we then identified the top sensitive diseases that have the highest numbers of inference genes, using a method for the identification of cut-off points in descriptive high-throughput omics studies [78].…”

Section: Dimension Reduction Of the Pathway-disease Matrixmentioning

confidence: 99%

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures

Arowolo¹,

Salemme²,

Suvorov³

2022

Preprint

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Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multichemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

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Simple method for cutoff point identification in descriptive high-throughput biological studies

Cited by 7 publications

References 20 publications

Underexplored Molecular Mechanisms of Toxicity

Underexplored Molecular Mechanisms of Toxicity

Underexplored Molecular Mechanisms of Toxicity

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures

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