cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qgatd6
title: Occurrence Download

description: A dataset containing 1882 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coleonyx elegans Gray, 1845"
 ]
}

 The dataset includes 1882 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016342-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.87ks2y
title: Occurrence Download

description: A dataset containing 47581 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Trifolium aureum Pollich"
 ]
}

 The dataset includes 47581 records from 850 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016338-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.skhs55
title: Occurrence Download

description: A dataset containing 570 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "TaxonKey is Ratufa bicolor (Sparrman, 1778)"
 ]
}

 The dataset includes 570 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016343-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9qkt5a
title: Occurrence Download

description: A dataset containing 1299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Engraulis japonicus Temminck & Schlegel, 1846",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1299 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016344-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27958365
title: thesis.phd.wp.supplementarytables.3

description: Supplementary tables for a thesis titled "Entourage: A Bioinformatics Pipeline for Virus Identification and Discovery"
DOI: 10.6084/m9.figshare.27958365.v1
title: thesis.phd.wp.supplementarytables.3

description: Supplementary tables for a thesis titled "Entourage: A Bioinformatics Pipeline for Virus Identification and Discovery"
DOI: 10.15468/dl.fzvh74
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation)",
 "OccurrenceStatus is Present",
 "TaxonKey is Tribolium destructor Uyttenboogaart, 1934",
 "Year 2014-2024"
 ]
}

 The dataset's 1 records were derived from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016332-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g9vfbu
title: Occurrence Download

description: A dataset containing 1079 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Trifolium carolinianum Michx."
 ]
}

 The dataset includes 1079 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016345-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4jayz
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation)",
 "Country is South Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, February, March, April, May, June, August, July, October, September, November, December)",
 "TaxonKey is Pleroma urvilleanum (DC.) P.J.F.Guim. & Michelang."
 ]
}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016346-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wnjkg5
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Engraulis encrasicolus (Linnaeus, 1758)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016347-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96gwjp
title: Occurrence Download

description: A dataset containing 2619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Micromesistius poutassou (Risso, 1827)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2619 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016348-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.77ftt5
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Katsuwonus pelamis (Linnaeus, 1758)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 79 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016349-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pj9wty
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scomber pelamis Brünnich, 1768",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016350-241126133413365/datasets/export for details.
DOI: 10.15468/dl.c95gnd
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation)",
 "Country is South Africa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, February, March, April, May, June, August, July, October, September, November, December)",
 "TaxonKey is Pleroma urvilleanum (DC.) P.J.F.Guim. & Michelang."
 ]
}

 The dataset includes 30 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016351-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kz5296
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Capsala katsuwoni (Ishii, 1936) Price, 1938",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016352-241126133413365/datasets/export for details.
DOI: 10.6084/m9.figshare.24805084
title: Interplay between COVID-19 and Secukinumab treatment in Spondylarthritis patients during the omicron surge: a retrospective cohort study

description: The objective of this retrospective cohort study was to assess the relationship between Corona Disease 2019 (COVID-19) and Secukinumab treatment in patients with Spondylarthritis (SpA) in China during the omicron surge. Researchers retrieved 1018 medical records of Secukinumab-treated patients between January 2020 and January 2023 from the West China Hospital of Sichuan University. Out of these, 190 SpA patients from the rheumatology clinic were selected for the study. Guided phone questionnaires were administered by research staff to collect baseline characteristics, SpA disease status, and COVID-19 clinical outcomes. Cohabitants served as the control group and provided COVID-19 related data. Of the 190 potential SpA patients, 122 (66%) completed the questionnaire <i>via</i> phone, along with 259 cohabitants. 84.4% of SpA patients were diagnosed with Ankylosing Spondylitis (AS), and 15.6% were diagnosed with Psoriatic Arthritis (PsA). The rate of SARS-CoV-2 infection was 83.6% in the Secukinumab group and 88.8% in the cohabitants control group, with no significant difference (OR = 0.684, CI 0.366–1.275). One instance of severe COVID-19 was observed in the Secukinumab group, while two were identified in the cohabitants control group. Patients in the Secukinumab group had less time with fever caused by COVID-19 (<i>p</i> = 0.004). Discontinuing Secukinumab after SARS-CoV-2 infection did not significantly affect the course of COVID-19 or worsen SpA status according to our data. Our study suggests that administering Secukinumab to SpA patients does not increase their susceptibility to contracting SARS-CoV-2, and may have a positive effect on the course of SARS-CoV-2 infection.
DOI: 10.6084/m9.figshare.24805084.v1
title: Interplay between COVID-19 and Secukinumab treatment in Spondylarthritis patients during the omicron surge: a retrospective cohort study

description: The objective of this retrospective cohort study was to assess the relationship between Corona Disease 2019 (COVID-19) and Secukinumab treatment in patients with Spondylarthritis (SpA) in China during the omicron surge. Researchers retrieved 1018 medical records of Secukinumab-treated patients between January 2020 and January 2023 from the West China Hospital of Sichuan University. Out of these, 190 SpA patients from the rheumatology clinic were selected for the study. Guided phone questionnaires were administered by research staff to collect baseline characteristics, SpA disease status, and COVID-19 clinical outcomes. Cohabitants served as the control group and provided COVID-19 related data. Of the 190 potential SpA patients, 122 (66%) completed the questionnaire <i>via</i> phone, along with 259 cohabitants. 84.4% of SpA patients were diagnosed with Ankylosing Spondylitis (AS), and 15.6% were diagnosed with Psoriatic Arthritis (PsA). The rate of SARS-CoV-2 infection was 83.6% in the Secukinumab group and 88.8% in the cohabitants control group, with no significant difference (OR = 0.684, CI 0.366–1.275). One instance of severe COVID-19 was observed in the Secukinumab group, while two were identified in the cohabitants control group. Patients in the Secukinumab group had less time with fever caused by COVID-19 (<i>p</i> = 0.004). Discontinuing Secukinumab after SARS-CoV-2 infection did not significantly affect the course of COVID-19 or worsen SpA status according to our data. Our study suggests that administering Secukinumab to SpA patients does not increase their susceptibility to contracting SARS-CoV-2, and may have a positive effect on the course of SARS-CoV-2 infection.
DOI: 10.6084/m9.figshare.27338532
title: Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn’s disease by integrating bioinformatics and machine learning

description: Crohn’s disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. This study aimed to identify specific diagnostic markers for CD using advanced computational approaches. Four gene expression datasets from the Gene Expression Omnibus (GEO) were analyzed, identifying differentially expressed genes (DEGs) through gene set enrichment analysis in R. Key biomarkers were selected using machine learning algorithms, including LASSO logistic regression, SVM‑RFE, and Random Forest, and their accuracy was assessed using receiver operating characteristic (ROC) curves and nomogram models. Immune cell infiltration was analyzed using the CIBERSORT algorithm, which helped reveal associations between diagnostic markers and immune cell patterns in CD. From a training set of 605 CD samples and 82 normal controls, we identified eight significant biomarkers: LCN2, FOLH1, CXCL1, FPR1, S100P, IGFBP5, CHP2, and AQP9. The diagnostic model showed high predictive power (AUC=0.954) and performed well in external validation (AUC = 1). Immune cell infiltration analysis highlighted various immune cells involved in CD, with all diagnostic markers strongly linked to immune cell interactions. Our findings propose candidate hub genes and present a nomogram for CD diagnosis, providing potential diagnostic biomarkers for clinical applications in CD.
DOI: 10.6084/m9.figshare.27338532.v1
title: Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn’s disease by integrating bioinformatics and machine learning

description: Crohn’s disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. This study aimed to identify specific diagnostic markers for CD using advanced computational approaches. Four gene expression datasets from the Gene Expression Omnibus (GEO) were analyzed, identifying differentially expressed genes (DEGs) through gene set enrichment analysis in R. Key biomarkers were selected using machine learning algorithms, including LASSO logistic regression, SVM‑RFE, and Random Forest, and their accuracy was assessed using receiver operating characteristic (ROC) curves and nomogram models. Immune cell infiltration was analyzed using the CIBERSORT algorithm, which helped reveal associations between diagnostic markers and immune cell patterns in CD. From a training set of 605 CD samples and 82 normal controls, we identified eight significant biomarkers: LCN2, FOLH1, CXCL1, FPR1, S100P, IGFBP5, CHP2, and AQP9. The diagnostic model showed high predictive power (AUC=0.954) and performed well in external validation (AUC = 1). Immune cell infiltration analysis highlighted various immune cells involved in CD, with all diagnostic markers strongly linked to immune cell interactions. Our findings propose candidate hub genes and present a nomogram for CD diagnosis, providing potential diagnostic biomarkers for clinical applications in CD.
DOI: 10.6084/m9.figshare.27679925
title: Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis

description: Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by downloading the training set GSE69683 and validation set GSE137268 from the GEO dataset. SVM-RFE analysis and the LASSO regression model were used to screen key genes, and CIBERSORT was used to assess immune infiltration in the samples. A total of 20 DEGs were identified in this study, mainly enriched for lymph node-like receptors, b-cell receptors, and neutrophil extracellular trap pathway. Comparative validation set GSE137268 identified thioredoxin (TXN) and coagulation factor V (F5) were identified as diagnostic markers of severe asthma. CIBERSORT analysis revealed that TXN and F5 are associated with multiple immune cell infiltrates. In addition, we identified miRNA and TF at the transcriptional level that may regulate F5 and TXN, and found that several commonly used drugs may exert therapeutic effects by targeting F5 and TXN. Taken together, TXN and F5 may be key genes in the development of severe asthma and are associated with immune infiltration. Our study can help to better understand the pathogenesis of asthma and provide new ideas for clinical treatment.
DOI: 10.6084/m9.figshare.27679925.v1
title: Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis

description: Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by downloading the training set GSE69683 and validation set GSE137268 from the GEO dataset. SVM-RFE analysis and the LASSO regression model were used to screen key genes, and CIBERSORT was used to assess immune infiltration in the samples. A total of 20 DEGs were identified in this study, mainly enriched for lymph node-like receptors, b-cell receptors, and neutrophil extracellular trap pathway. Comparative validation set GSE137268 identified thioredoxin (TXN) and coagulation factor V (F5) were identified as diagnostic markers of severe asthma. CIBERSORT analysis revealed that TXN and F5 are associated with multiple immune cell infiltrates. In addition, we identified miRNA and TF at the transcriptional level that may regulate F5 and TXN, and found that several commonly used drugs may exert therapeutic effects by targeting F5 and TXN. Taken together, TXN and F5 may be key genes in the development of severe asthma and are associated with immune infiltration. Our study can help to better understand the pathogenesis of asthma and provide new ideas for clinical treatment.
DOI: 10.6084/m9.figshare.25459369
title: Innovate therapeutic targets for autoimmune diseases: insights from proteome-wide mendelian randomization and Bayesian colocalization

description: Despite growing knowledge regarding the pathogenesis of autoimmune diseases (ADs) onset, the current treatment remains unsatisfactory. This study aimed to identify innovative therapeutic targets for ADs through various analytical approaches. Utilizing Mendelian randomization, Bayesian co-localization, phenotype scanning, and protein–protein interaction network, we explored potential therapeutic targets for 14 ADs and externally validated our preliminary findings. This study identified 12 circulating proteins as potential therapeutic targets for six ADs. Specifically, IL12B was judged to be a risk factor for ankylosing spondylitis (<i>p</i> = 1.61E − 07). TYMP (<i>p</i> = 6.28E − 06) was identified as a protective factor for ulcerative colitis. For Crohn’s disease, ERAP2 (<i>p</i> = 4.47E − 14), HP (<i>p</i> = 2.08E − 05), and RSPO3 (<i>p</i> = 6.52E − 07), were identified as facilitators, whereas FLRT3 (<i>p</i> = 3.42E − 07) had a protective effect. In rheumatoid arthritis, SWAP70 (<i>p</i> = 3.26E − 10), SIGLEC6 (<i>p</i> = 2.47E − 05), ISG15 (<i>p</i> = 3.69E − 05), and FCRL3 (<i>p</i> = 1.10E − 10) were identified as risk factors. B4GALT1 (<i>p</i> = 6.59E − 05) was associated with a lower risk of Type 1 diabetes (T1D). Interestingly, CTSH was identified as a protective factor for narcolepsy (<i>p</i> = 1.58E − 09) but a risk factor for T1D (<i>p</i> = 7.36E − 11), respectively. External validation supported the associations of eight of these proteins with three ADs. Our integrated study identified 12 potential therapeutic targets for ADs and provided novel insights into future drug development for ADs.
DOI: 10.6084/m9.figshare.25459369.v1
title: Innovate therapeutic targets for autoimmune diseases: insights from proteome-wide mendelian randomization and Bayesian colocalization

description: Despite growing knowledge regarding the pathogenesis of autoimmune diseases (ADs) onset, the current treatment remains unsatisfactory. This study aimed to identify innovative therapeutic targets for ADs through various analytical approaches. Utilizing Mendelian randomization, Bayesian co-localization, phenotype scanning, and protein–protein interaction network, we explored potential therapeutic targets for 14 ADs and externally validated our preliminary findings. This study identified 12 circulating proteins as potential therapeutic targets for six ADs. Specifically, IL12B was judged to be a risk factor for ankylosing spondylitis (<i>p</i> = 1.61E − 07). TYMP (<i>p</i> = 6.28E − 06) was identified as a protective factor for ulcerative colitis. For Crohn’s disease, ERAP2 (<i>p</i> = 4.47E − 14), HP (<i>p</i> = 2.08E − 05), and RSPO3 (<i>p</i> = 6.52E − 07), were identified as facilitators, whereas FLRT3 (<i>p</i> = 3.42E − 07) had a protective effect. In rheumatoid arthritis, SWAP70 (<i>p</i> = 3.26E − 10), SIGLEC6 (<i>p</i> = 2.47E − 05), ISG15 (<i>p</i> = 3.69E − 05), and FCRL3 (<i>p</i> = 1.10E − 10) were identified as risk factors. B4GALT1 (<i>p</i> = 6.59E − 05) was associated with a lower risk of Type 1 diabetes (T1D). Interestingly, CTSH was identified as a protective factor for narcolepsy (<i>p</i> = 1.58E − 09) but a risk factor for T1D (<i>p</i> = 7.36E − 11), respectively. External validation supported the associations of eight of these proteins with three ADs. Our integrated study identified 12 potential therapeutic targets for ADs and provided novel insights into future drug development for ADs.
DOI: 10.6084/m9.figshare.25195456.v1
title: Bioinformatics and systems-biology approach to identify common pathogenic mechanisms for COVID-19 and systemic lupus erythematosus

description: The Coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don’t know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches. The common differentially expressed genes (DEGs) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis. Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGs were selected. Hub genes were identified by protein-protein interaction (PPI) analysis. few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE. COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 or COVID-19 combined with SLE.