690 results for ""PhD+2001"+"Professor+of+Bioinformatics"+2012+18000+citations+profile"

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• Bibliography Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here we expose in a yearly and cumulative manner, the publications, citations, publishers journals and impact factor of EGA related studies. Publications citing data stored at the EGA The studies that use EGA datasets are requested to cite the EGA into their bibliography. This fact allows for a faster spreading of the role and purpose of the EGA, being the availability of their studies for testability and reusability purposes. In order to track the studies we use the unique study accession, provided by the EGA when submitting a new study, and comprised of the keyword EGAS followed by 11 digits such as EGAS00000000001. We use the Europe PMC RESTful Web Service in order to query, and index studies where the EGAS accession was used. Below, you can find a chart showing the journal publishers of the studies citing data stored in the EGA being cited: Publications Citing Data Stored in the EGA pieChart('publishers-citations', 'https://stats.ega-archive.org/publishers/citations', 'Publishers') Number of publications citing data stored in the EGA by year Here, you can find a chart showing the number of publications citing data stored in the EGA by year. These publications could have deposit, or re-used the data. The study acession can be found in the publication. The default values are non-cumulative. You can see cumulative values by clicking "Cumulative". Number of Publications Citing Data Stored in the EGA by Year barChart('publications-by-year', 'https://stats.ega-archive.org/publications/year', ['Number of Publications by Year', 'Publications by Year']) Journal Impact Factor of Publications Citing Data Stored in the EGA Here, you can find a chart showing the journal impact factor of publications citing data stored in the EGA. We have used Scimago Journal Rank for this purpose. Low impact treshold was defined between 0.0 and 5.0 excluding; Medium impact treshold was defined between 5.0 and 10.0 excluding; High impact range was established from values equal to or higher than 10.0; Journal Impact Factor of Publications Citing Data Stored in the EGA pieChart('citations-publication-impact-factor', 'https://stats.ega-archive.org/citations/publication/impact/factor', 'Publication Impact Factor') Publications citing a publication containing a EGA study accession The publications, stating a EGA study accession in the bibliography, can be cited for testibility or reusability purposes. Below, you can find a chart showing the publishers of the publications being cited: Publications Citing a Publication Containing a EGA Study Accession pieChart('publications-citations', 'https://stats.ega-archive.org/publications/citations', 'Publishers') Number of publications citing a publication containing a EGA study accession by year Here, you can find a chart showing the number of publications citing a publication containing a EGA study accession by year by year. The values are non-cumulative: Number of published citations by year barChart('citations-year', 'https://stats.ega-archive.org/citations/year', ['Number of Publications', 'Citations by Year'])

  Documentation about/statistics/bibliography

• Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer

  A subset of Cancer-Associated Fibroblasts (CAF-S1) mediates immunosuppression in breast cancers (BC), but its heterogeneity and its impact on immunotherapy response remain unknown. Here, we identify 8 CAF-S1 clusters by analyzing more than 18000 single CAF-S1 fibroblasts from BC. We validate the 5 most abundant clusters by flow cytometry and in silico analyses in other cancer types, highlighting their relevance. Myofibroblasts from clusters 0 and 3, characterized by extra-cellular matrix proteins and TGF signaling respectively, are indicative of primary resistance to immunotherapies. Cluster 0 up-regulates PD-1 and CTLA-4 protein levels in regulatory T lymphocytes (Tregs), which in turn increases CAF-S1 cluster 3 cellular content. Thus, our study highlights a positive feedback loop between specific CAF-S1 clusters and Tregs and uncovers their role in immunotherapy resistance.

  Study EGAS00001004030

• Targeted sequencing of Burkitt lymphoma tumour samples from the UK's Haematological Malignancy Research Network

  The UK’s Haematological Malignancy Research Network (www.HMRN.org) was established in 2004 to provide robust generalizable data to inform clinical practice and research. HMRN is a collaboration between researchers in the Epidemiology & Statistics Group (ECSG) at the University of York, a unified Clinical Network operating across 14 hospitals, and an integrated Haematological Malignancy Diagnostic Service (HMDS) in Leeds. Covering a population of around 4 million, HMRN collects detailed information about all patients diagnosed with a haematological malignancy within the HMRN region, accruing around 2,400 new diagnoses each year. The population has a similar socio-demographic profile to the country as a whole, and HMRN’s maturing data present an increasingly valuable resource to address real questions of concern to haematologists, commissioners and health service researchers – locally, nationally and internationally. This study forms part of a larger project employing targeted exome sequencing on tumour samples from patients diagnosed with a variety of both lymphoid and myeloid malignancies. The results for samples collected from 39 Burkitt lymphoma patients, diagnosed within HMRN from 2005 to 2012, are provided.

  Study EGAS00001004649

• Targeted sequencing of diffuse large B-cell lymphoma tumour samples from the UK's Haematological Malignancy Research Network

  The UK’s Haematological Malignancy Research Network (www.HMRN.org) was established in 2004 to provide robust generalizable data to inform clinical practice and research. HMRN is a collaboration between researchers in the Epidemiology & Statistics Group (ECSG) at the University of York, a unified Clinical Network operating across 14 hospitals, and an integrated Haematological Malignancy Diagnostic Service (HMDS) in Leeds. Covering a population of around 4 million, HMRN collects detailed information about all patients diagnosed with a haematological malignancy within the HMRN region, accruing around 2,400 new diagnoses each year. The population has a similar socio-demographic profile to the country as a whole, and HMRN’s maturing data present an increasingly valuable resource to address real questions of concern to haematologists, commissioners and health service researchers – locally, nationally and internationally. This study forms part of a larger project employing targeted exome sequencing on tumour samples from patients diagnosed with a variety of both lymphoid and myeloid malignancies. The results for samples collected from 928 diffuse large B-cell lymphoma patients, diagnosed within HMRN from 2005 to 2012, are provided.

  Study EGAS00001005953

• UK10K_NEURO_FSZ REL-2012-11-27

  UK10K_NEURO_FSZ REL-2012-11-27

  Dataset EGAD00001000318

• UK10K_OBESITY_SCOOP REL-2012-01-13

  UK10K_OBESITY_SCOOP REL-2012-01-13

  Dataset EGAD00001000181

• Effects of GATA4-inhibiting compound 3i-2012 on HB-243 hepatoblastoma cells

  In order to study the effects of GATA4 inhibition, HB-243 hepatoblastoma cells were treated with GATA4-inhibiting compound 3i-2012 for 24 hours. Total RNA was extracted and sequenced.

  Study EGAS50000000999

• Genome-Wide Association Study of Lung Cancer Susceptibility in Never-Smoking Women in Asia

  To learn about the etiology of lung cancer among never-smoking women in Asia, we formed the Female Lung Cancer Consortium in Asia (FLCCA), which includes studies of lung cancer in Eastern Asia and conducted a GWAS. We analyzed a total of 5510 lung cancer cases and 4544 controls and identified 3 novel loci (Lan et al., 2012). Of these study subjects, 4922 lung cancer cases and 3959 controls are available for posting. Lan et al.., Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia. Nat Genet. 2012 Dec;44(12):1330-5. doi: 10.1038/ng.2456. Epub 2012 Nov 11. PubMed PMID: 23143601.

  Study phs000716

• Epigenomic profile of diverse cancer

  Epigenomic profile of diverse cancer.

  Study EGAS00001004352

• UK10K_RARE_THYROID REL-2012-01-13

  UK10K_RARE_THYROID REL-2012-01-13

  Dataset EGAD00001000152

• UK10K_RARE_SIR REL-2012-01-13

  UK10K_RARE_SIR REL-2012-01-13

  Dataset EGAD00001000153

• Plasma DNA profile in DNASE1L3 deficiency

  Plasma DNA profile in DNASE1L3 deficiency

  Study EGAS00001004342

• Plasma DNA profile in DNASE1L3 deficiency-Mouse AAV samples

  Plasma DNA profile in DNASE1L3 deficiency-Mouse AAV

  Study EGAS00001004409

• CNV profile in HSP90 inhibitor resistant K562 cells

  Analysis of the acquired copy number profile in K562 cell which got resistant against HSP90 inhibitors.

  Study EGAS00001006385

• Sardinian Chromsome Y 2012 Analysis

  1204 Sardinian males

  Dataset EGAD00001000618

• RNA sequencing of chondrosarcoma

  To profile chondrosarcoma patients.

  Study EGAS00001004585

• UK10K_NEURO_FSZNK REL-2012-11-27

  UK10K_NEURO_FSZNK REL-2012-11-27

  Dataset EGAD00001000332

• UK10K_NEURO_UKSCZ REL-2012-11-27

  UK10K_NEURO_UKSCZ REL-2012-11-27

  Dataset EGAD00001000335

• Copy number and mutation profiling of Stage 1 epithelial ovarian cancer biopsies

  This project aims at characterizing the genome-wide copy number profile and the variant profile of 139 genes om the five major histotytpes of early-stage (stage I) epithelial ovarian cancer (EOC), to provide insight in the the aethiology and development of the disease.

  Study EGAS00001004961

• UK10K_RARE_THYROID REL-2012-11-27

  UK10K_RARE_THYROID REL-2012-11-27

  Dataset EGAD00001000329

• UK10K_RARE_SIR REL-2012-11-27

  UK10K_RARE_SIR REL-2012-11-27

  Dataset EGAD00001000334

• UK10K_OBESITY_SCOOP REL-2012-11-27

  UK10K_OBESITY_SCOOP REL-2012-11-27

  Dataset EGAD00001000336

• Plasma DNA profile in DNASE1L3 deficiency

  Plasma DNA profile in DNASE1L3 deficiency

  Dataset EGAD00001006216

• REL-2017-07-2012

  HipSci - Primary Immune Deficiency - Genotyping Array - July 2017

  Dataset EGAD00010001358

• Characterization of Leukemic Stem Cells in DNMT3Amut and NPM1mut AMLs

  Trascriptomic profile of FACS-sorted LSCs from diagnostic DNMT3Amut and NPM1mut AML patients using a novel sorting strategy. Briefly, positive selection for GPR56 and a negative selection for NKG2DLigands were used to enrich for LSCs in both CD34+ and CD34- subpopulations. Primary samples were retrospectively collected based on their mutational profile.

  Study EGAS00001006527