ArticleNucleic acids research2008
The Stanford Tissue Microarray Database.
Article in Nucleic acids research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 100 citations in OpenAlex.
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- Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis.Journal of personalized medicine · 2023Article
- Harnessing multimodal data integration to advance precision oncology.Nature reviews. Cancer · 2022Review
- CAR T cells with dual targeting of CD19 and CD22 in adult patients with recurrent or refractory B cell malignancies: a phase 1 trial.Nature medicine · 2021Article
- Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries.Blood advances · 2021Article
- KLF1/EKLF expression in acute leukemia is correlated with chromosomal abnormalities.Blood cells, molecules & diseases · 2020Article
- LibMI: An Open Source Library for Efficient Histopathological Image Processing.Journal of pathology informatics · 2020Article
- Proceedings of the fourth international molecular pathological epidemiology (MPE) meeting.Cancer causes & control : CCC · 2019Article
- Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer.JAMA network open · 2019Article
- High frequency of CD74 expression in lymphomas: implications for targeted therapy using a novel anti-CD74-drug conjugate.The journal of pathology. Clinical research · 2019Article
- Incorporating Deep Features in the Analysis of Tissue Microarray Images.Statistics and its interface · 2019Article
- Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.EBioMedicine · 2018Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
- Recurrent rearrangements of the Myb/SANT-like DNA-binding domain containing 3 gene (MSANTD3) in salivary gland acinic cell carcinoma.PloS one · 2017Article
- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.Nature communications · 2016Article
- An inter-species protein-protein interaction network across vast evolutionary distance.Molecular systems biology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
The Stanford Tissue Microarray Database (TMAD; http://tma.stanford.edu) is a public resource for disseminating annotated tissue images and associated expression data. Stanford University pathologists, researchers and their collaborators worldwide use TMAD for designing, viewing, scoring and analyzing their tissue microarrays. The use of tissue microarrays allows hundreds of human tissue cores to be simultaneously probed by antibodies to detect protein abundance (Immunohistochemistry; IHC), or by labeled nucleic acids (in situ hybridization; ISH) to detect transcript abundance. TMAD archives multi-wavelength fluorescence and bright-field images of tissue microarrays for scoring and analysis. As of July 2007, TMAD contained 205 161 images archiving 349 distinct probes on 1488 tissue microarray slides. Of these, 31 306 images for 68 probes on 125 slides have been released to the public. To date, 12 publications have been based on these raw public data. TMAD incorporates the NCI Thesaurus ontology for searching tissues in the cancer domain. Image processing researchers can extract images and scores for training and testing classification algorithms. The production server uses the Apache HTTP Server, Oracle Database and Perl application code. Source code is available to interested researchers under a no-cost license.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.