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Showing 1 to 12 of 47 entries
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GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data.

Scientific reports

Araujo FA, Barh D, Silva A, Guimarães L, Ramos RTJ.
PMID: 29379090
Sci Rep. 2018 Jan 29;8(1):1794. doi: 10.1038/s41598-018-20211-9.

Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the...

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Araujo FA, Barh D, Silva A, et al. GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Sci Rep. 2018;8(1):1794doi: 10.1038/s41598-018-20211-9.
Araujo, F. A., Barh, D., Silva, A., Guimarães, L., & Ramos, R. T. J. (2018). GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Scientific reports, 8(1), 1794. https://doi.org/10.1038/s41598-018-20211-9
Araujo, Fabricio Almeida, et al. "GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data." Scientific reports vol. 8,1 (2018): 1794. doi: https://doi.org/10.1038/s41598-018-20211-9
Araujo FA, Barh D, Silva A, Guimarães L, Ramos RTJ. GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data. Sci Rep. 2018 Jan 29;8(1):1794. doi: 10.1038/s41598-018-20211-9. PMID: 29379090; PMCID: PMC5789007.
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Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis.

International ophthalmology

Sun H, Cheng Y, Yan Z, Liu X, Zhang J.
PMID: 31953631
Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17.

PURPOSE: Diabetic retinopathy (DR) especially proliferative diabetic retinopathy (PDR) is a serious eye disease. We aimed to identify key pathway and hub genes associated with PDR by analyzing the expression of retinal fibrovascular tissue in PDR patients.METHODS: First raw...

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Sun H, Cheng Y, Yan Z, et al. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020;40(2):269-279doi: 10.1007/s10792-019-01158-w.
Sun, H., Cheng, Y., Yan, Z., Liu, X., & Zhang, J. (2020). Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. International ophthalmology, 40(2), 269-279. https://doi.org/10.1007/s10792-019-01158-w
Sun, Haiyan, et al. "Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis." International ophthalmology vol. 40,2 (2020): 269-279. doi: https://doi.org/10.1007/s10792-019-01158-w
Sun H, Cheng Y, Yan Z, Liu X, Zhang J. Mining the proliferative diabetic retinopathy-associated genes and pathways by integrated bioinformatic analysis. Int Ophthalmol. 2020 Feb;40(2):269-279. doi: 10.1007/s10792-019-01158-w. Epub 2020 Jan 17. PMID: 31953631.
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Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset.

Lipids in health and disease

Guan YZ, Yin RX, Zheng PF, Deng GX, Liu CX, Wei BL.
PMID: 31647035
Lipids Health Dis. 2019 Oct 23;18(1):184. doi: 10.1186/s12944-019-1131-3.

BACKGROUND: This study aimed to investigate the angiotensin converting enzyme (ACE) co-expression genes and their pathways involved in ST-segment elevation myocardial infarction (STEMI) at different time points.METHODS: The array data set of GSE59867 was examined for the ACE co-expression...

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Guan YZ, Yin RX, Zheng PF, et al. Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids Health Dis. 2019;18(1):184doi: 10.1186/s12944-019-1131-3.
Guan, Y. Z., Yin, R. X., Zheng, P. F., Deng, G. X., Liu, C. X., & Wei, B. L. (2019). Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids in health and disease, 18(1), 184. https://doi.org/10.1186/s12944-019-1131-3
Guan, Yao-Zong, et al. "Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset." Lipids in health and disease vol. 18,1 (2019): 184. doi: https://doi.org/10.1186/s12944-019-1131-3
Guan YZ, Yin RX, Zheng PF, Deng GX, Liu CX, Wei BL. Potential molecular mechanism of ACE gene at different time points in STEMI patients based on genome-wide microarray dataset. Lipids Health Dis. 2019 Oct 23;18(1):184. doi: 10.1186/s12944-019-1131-3. PMID: 31647035; PMCID: PMC6813054.
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The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease.

American journal of respiratory cell and molecular biology

Schiller HB, Montoro DT, Simon LM, Rawlins EL, Meyer KB, Strunz M, Vieira Braga FA, Timens W, Koppelman GH, Budinger GRS, Burgess JK, Waghray A, van den Berge M, Theis FJ, Regev A, Kaminski N, Rajagopal J, Teichmann SA, Misharin AV, Nawijn MC.
PMID: 30995076
Am J Respir Cell Mol Biol. 2019 Jul;61(1):31-41. doi: 10.1165/rcmb.2018-0416TR.

Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized...

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Schiller HB, Montoro DT, Simon LM, et al. The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. Am J Respir Cell Mol Biol. 2019;61(1):31-41doi: 10.1165/rcmb.2018-0416TR.
Schiller, H. B., Montoro, D. T., Simon, L. M., Rawlins, E. L., Meyer, K. B., Strunz, M., Vieira Braga, F. A., Timens, W., Koppelman, G. H., Budinger, G. R. S., Burgess, J. K., Waghray, A., van den Berge, M., Theis, F. J., Regev, A., Kaminski, N., Rajagopal, J., Teichmann, S. A., Misharin, A. V., & Nawijn, M. C. (2019). The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. American journal of respiratory cell and molecular biology, 61(1), 31-41. https://doi.org/10.1165/rcmb.2018-0416TR
Schiller, Herbert B, et al. "The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease." American journal of respiratory cell and molecular biology vol. 61,1 (2019): 31-41. doi: https://doi.org/10.1165/rcmb.2018-0416TR
Schiller HB, Montoro DT, Simon LM, Rawlins EL, Meyer KB, Strunz M, Vieira Braga FA, Timens W, Koppelman GH, Budinger GRS, Burgess JK, Waghray A, van den Berge M, Theis FJ, Regev A, Kaminski N, Rajagopal J, Teichmann SA, Misharin AV, Nawijn MC. The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. Am J Respir Cell Mol Biol. 2019 Jul;61(1):31-41. doi: 10.1165/rcmb.2018-0416TR. PMID: 30995076; PMCID: PMC6604220.
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CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression.

IEEE/ACM transactions on computational biology and bioinformatics

Mu Q, Wang J.
PMID: 31581092
IEEE/ACM Trans Comput Biol Bioinform. 2021 Jan-Feb;18(1):306-311. doi: 10.1109/TCBB.2019.2944827. Epub 2021 Feb 03.

Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level,...

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Mu Q, Wang J. CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(1):306-311doi: 10.1109/TCBB.2019.2944827.
Mu, Q., & Wang, J. (2021). CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression. IEEE/ACM transactions on computational biology and bioinformatics, 18(1), 306-311. https://doi.org/10.1109/TCBB.2019.2944827
Mu, Quanhua, and Wang, Jiguang. "CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression." IEEE/ACM transactions on computational biology and bioinformatics vol. 18,1 (2021): 306-311. doi: https://doi.org/10.1109/TCBB.2019.2944827
Mu Q, Wang J. CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression. IEEE/ACM Trans Comput Biol Bioinform. 2021 Jan-Feb;18(1):306-311. doi: 10.1109/TCBB.2019.2944827. Epub 2021 Feb 03. PMID: 31581092.
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Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host.

Nature communications

Lloréns-Rico V, Gregory AC, Van Weyenbergh J, Jansen S, Van Buyten T, Qian J, Braz M, Menezes SM, Van Mol P, Vanderbeke L, Dooms C, Gunst J, Hermans G, Meersseman P, Wauters E, Neyts J, Lambrechts D, Wauters J, Raes J.
PMID: 34716338
Nat Commun. 2021 Oct 29;12(1):6243. doi: 10.1038/s41467-021-26500-8.

Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the respiratory microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 respiratory...

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Lloréns-Rico V, Gregory AC, Van Weyenbergh J, et al. Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host. Nat Commun. 2021;12(1):6243doi: 10.1038/s41467-021-26500-8.
Lloréns-Rico, V., Gregory, A. C., Van Weyenbergh, J., Jansen, S., Van Buyten, T., Qian, J., Braz, M., Menezes, S. M., Van Mol, P., Vanderbeke, L., Dooms, C., Gunst, J., Hermans, G., Meersseman, P., Wauters, E., Neyts, J., Lambrechts, D., Wauters, J., & Raes, J. (2021). Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host. Nature communications, 12(1), 6243. https://doi.org/10.1038/s41467-021-26500-8
Lloréns-Rico, Verónica, et al. "Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host." Nature communications vol. 12,1 (2021): 6243. doi: https://doi.org/10.1038/s41467-021-26500-8
Lloréns-Rico V, Gregory AC, Van Weyenbergh J, Jansen S, Van Buyten T, Qian J, Braz M, Menezes SM, Van Mol P, Vanderbeke L, Dooms C, Gunst J, Hermans G, Meersseman P, Wauters E, Neyts J, Lambrechts D, Wauters J, Raes J. Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host. Nat Commun. 2021 Oct 29;12(1):6243. doi: 10.1038/s41467-021-26500-8. PMID: 34716338; PMCID: PMC8556379.
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Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis.

Medical science monitor : international medical journal of experimental and clinical research

Li Z, Chyr J, Jia Z, Wang L, Hu X, Wu X, Song C.
PMID: 32888289
Med Sci Monit. 2020 Sep 05;26:e923514. doi: 10.12659/MSM.923514.

BACKGROUND Hypertension is one of the most widespread health conditions in the world, and the molecular mechanism of it is still unclear. In this study, we identified the hub genes (hub miRNA genes) associated with hypertension and explored the...

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Li Z, Chyr J, Jia Z, et al. Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Med Sci Monit. 2020;26:e923514doi: 10.12659/MSM.923514.
Li, Z., Chyr, J., Jia, Z., Wang, L., Hu, X., Wu, X., & Song, C. (2020). Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Medical science monitor : international medical journal of experimental and clinical research, 26e923514. https://doi.org/10.12659/MSM.923514
Li, Zongjin, et al. "Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis." Medical science monitor : international medical journal of experimental and clinical research vol. 26 (2020): e923514. doi: https://doi.org/10.12659/MSM.923514
Li Z, Chyr J, Jia Z, Wang L, Hu X, Wu X, Song C. Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis. Med Sci Monit. 2020 Sep 05;26:e923514. doi: 10.12659/MSM.923514. PMID: 32888289; PMCID: PMC7491244.
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Guidelines for reporting single-cell RNA-seq experiments.

Nature biotechnology

Füllgrabe A, George N, Green M, Nejad P, Aronow B, Fexova SK, Fischer C, Freeberg MA, Huerta L, Morrison N, Scheuermann RH, Taylor D, Vasilevsky N, Clarke L, Gehlenborg N, Kent J, Marioni J, Teichmann S, Brazma A, Papatheodorou I.
PMID: 33188371
Nat Biotechnol. 2020 Dec;38(12):1384-1386. doi: 10.1038/s41587-020-00744-z.

No abstract available.

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Füllgrabe A, George N, Green M, et al. Guidelines for reporting single-cell RNA-seq experiments. Nat Biotechnol. 2020;38(12):1384-1386doi: 10.1038/s41587-020-00744-z.
Füllgrabe, A., George, N., Green, M., Nejad, P., Aronow, B., Fexova, S. K., Fischer, C., Freeberg, M. A., Huerta, L., Morrison, N., Scheuermann, R. H., Taylor, D., Vasilevsky, N., Clarke, L., Gehlenborg, N., Kent, J., Marioni, J., Teichmann, S., Brazma, A., & Papatheodorou, I. (2020). Guidelines for reporting single-cell RNA-seq experiments. Nature biotechnology, 38(12), 1384-1386. https://doi.org/10.1038/s41587-020-00744-z
Füllgrabe, Anja, et al. "Guidelines for reporting single-cell RNA-seq experiments." Nature biotechnology vol. 38,12 (2020): 1384-1386. doi: https://doi.org/10.1038/s41587-020-00744-z
Füllgrabe A, George N, Green M, Nejad P, Aronow B, Fexova SK, Fischer C, Freeberg MA, Huerta L, Morrison N, Scheuermann RH, Taylor D, Vasilevsky N, Clarke L, Gehlenborg N, Kent J, Marioni J, Teichmann S, Brazma A, Papatheodorou I. Guidelines for reporting single-cell RNA-seq experiments. Nat Biotechnol. 2020 Dec;38(12):1384-1386. doi: 10.1038/s41587-020-00744-z. PMID: 33188371.
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An Introduction to Systems Analytics and Integration of Big Omics Data.

Genes

Hardiman G.
PMID: 32111000
Genes (Basel). 2020 Feb 26;11(3). doi: 10.3390/genes11030245.

A major technological shift in the research community in the past decade has been the adoption of high throughput (HT) technologies to interrogate the genome, epigenome, transcriptome, and proteome in a massively parallel fashion [...].

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Hardiman G. An Introduction to Systems Analytics and Integration of Big Omics Data. Genes (Basel). 2020;11(3)doi: 10.3390/genes11030245.
Hardiman, G. (2020). An Introduction to Systems Analytics and Integration of Big Omics Data. Genes, 11(3), . https://doi.org/10.3390/genes11030245
Hardiman, Gary. "An Introduction to Systems Analytics and Integration of Big Omics Data." Genes vol. 11,3 (2020). doi: https://doi.org/10.3390/genes11030245
Hardiman G. An Introduction to Systems Analytics and Integration of Big Omics Data. Genes (Basel). 2020 Feb 26;11(3). doi: 10.3390/genes11030245. PMID: 32111000; PMCID: PMC7140791.
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Onco-proteogenomics: cancer proteomics joins forces with genomics.

Nature methods

Alfaro JA, Sinha A, Kislinger T, Boutros PC.
PMID: 25357240
Nat Methods. 2014 Nov;11(11):1107-13. doi: 10.1038/nmeth.3138.

The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer...

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Alfaro JA, Sinha A, Kislinger T, et al. Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods. 2014;11(11):1107-13doi: 10.1038/nmeth.3138.
Alfaro, J. A., Sinha, A., Kislinger, T., & Boutros, P. C. (2014). Onco-proteogenomics: cancer proteomics joins forces with genomics. Nature methods, 11(11), 1107-13. https://doi.org/10.1038/nmeth.3138
Alfaro, Javier A, et al. "Onco-proteogenomics: cancer proteomics joins forces with genomics." Nature methods vol. 11,11 (2014): 1107-13. doi: https://doi.org/10.1038/nmeth.3138
Alfaro JA, Sinha A, Kislinger T, Boutros PC. Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods. 2014 Nov;11(11):1107-13. doi: 10.1038/nmeth.3138. PMID: 25357240.
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Precision oncology in the age of integrative genomics.

Nature biotechnology

Kumar-Sinha C, Chinnaiyan AM.
PMID: 29319699
Nat Biotechnol. 2018 Jan 10;36(1):46-60. doi: 10.1038/nbt.4017.

Precision oncology applies genomic and other molecular analyses of tumor biopsies to improve the diagnosis and treatment of cancers. In addition to identifying therapeutic options, precision oncology tracks the response of a tumor to an intervention at the molecular...

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Kumar-Sinha C, Chinnaiyan AM. Precision oncology in the age of integrative genomics. Nat Biotechnol. 2018;36(1):46-60doi: 10.1038/nbt.4017.
Kumar-Sinha, C., & Chinnaiyan, A. M. (2018). Precision oncology in the age of integrative genomics. Nature biotechnology, 36(1), 46-60. https://doi.org/10.1038/nbt.4017
Kumar-Sinha, Chandan, and Chinnaiyan, Arul M. "Precision oncology in the age of integrative genomics." Nature biotechnology vol. 36,1 (2018): 46-60. doi: https://doi.org/10.1038/nbt.4017
Kumar-Sinha C, Chinnaiyan AM. Precision oncology in the age of integrative genomics. Nat Biotechnol. 2018 Jan 10;36(1):46-60. doi: 10.1038/nbt.4017. PMID: 29319699; PMCID: PMC6364676.
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Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart.

Cell reports

Cui Y, Zheng Y, Liu X, Yan L, Fan X, Yong J, Hu Y, Dong J, Li Q, Wu X, Gao S, Li J, Wen L, Qiao J, Tang F.
PMID: 30759401
Cell Rep. 2019 Feb 12;26(7):1934-1950.e5. doi: 10.1016/j.celrep.2019.01.079.

The heart is the central organ of the circulatory system, and its proper development is vital for maintaining human life. Here, we used single-cell RNA sequencing to profile the gene expression landscapes of ∼4,000 cardiac cells from human embryos...

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Cui Y, Zheng Y, Liu X, et al. Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart. Cell Rep. 2019;26(7):1934-1950.e5doi: 10.1016/j.celrep.2019.01.079.
Cui, Y., Zheng, Y., Liu, X., Yan, L., Fan, X., Yong, J., Hu, Y., Dong, J., Li, Q., Wu, X., Gao, S., Li, J., Wen, L., Qiao, J., & Tang, F. (2019). Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart. Cell reports, 26(7), 1934-1950.e5. https://doi.org/10.1016/j.celrep.2019.01.079
Cui, Yueli, et al. "Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart." Cell reports vol. 26,7 (2019): 1934-1950.e5. doi: https://doi.org/10.1016/j.celrep.2019.01.079
Cui Y, Zheng Y, Liu X, Yan L, Fan X, Yong J, Hu Y, Dong J, Li Q, Wu X, Gao S, Li J, Wen L, Qiao J, Tang F. Single-Cell Transcriptome Analysis Maps the Developmental Track of the Human Heart. Cell Rep. 2019 Feb 12;26(7):1934-1950.e5. doi: 10.1016/j.celrep.2019.01.079. PMID: 30759401.
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Showing 1 to 12 of 47 entries