Towards a knowledge-based Human Protein Atlas

By Mathias Uhlen, P. Oksvold, Linn Fagerberg, E. Lundberg, K. Jonasson, Mattias Forsberg, M. Zwahlen, C. Kampf, K. Wester, S. Hober and 3 more. Nature Biotechnology, 2010

2,444 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f9462d8157c78918c33fdc1b756af57cdff35135

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Citations in the corpus

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT (2012, background): “The INIT (Integrative Network Inference for Tissues) algorithm is formulated as a mixed integer-linear problem (MILP) and is specially tailored to use the evidence from the HPA as input.”
In silico prediction of protein-protein interactions in human macrophages (2001, methodology): “…by showing that it is enriched in proteins related to the immune response, expressed in macrophages according to the Human Protein Atlas [29] and HPRD [30] and belonging to the host regulatory network during MTB infection [31] as well as in interactions reported to occur in macrophages…”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “The Human Protein Atlas project has generated a repository of > 25,000 specific antibodies, targeting proteins from > 17,000 human genes (7).”
The UniProtKB guide to the human proteome (2016, methodology): “The human reference proteome in UniProtKB When our project to manually curate all human proteincoding genes was initiated, we were first focusing on proteins that were characterized in the literature, or described in other databases, such as HGNC (6), OMIM (7) or the Human Protein Atlas (8).”
COMPARTMENTS: unification and visualization of protein subcellular localization evidence (2014, methodology): “When only a single antibody has been used for staining, we instead make use of the validation score provided by HPA.”
CP-CHARM: segmentation-free image classification made accessible (2016, methodology): “Then, we illustrated the usability of our approach on several kinds of biological datasets, namely high-throughput cell-based assay data freely available from the Broad Bioimage Benchmark Collection (BBBC, [18]) and tissue images from the Human Protein Atlas (HPA, [19]).”
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations (2014, background): “…localization based on immunohistochemistry, and transcript expression levels for a large number of human tissues, cancers and cell lines (16); BioGPS (http://biogps.org/) for tissue-specific pattern of mRNA expression (17); Wikipedia pages include information about protein function, RNA…”
A subcellular map of the human proteome (2017, methodology): “As an integrated part of the HPA effort (23), antibodies have been generated, affinity-purified using the antigen, and validated by protein microarray analysis to ensure specific and selective binding to the intended target antigen (32).”
Tissue-based map of the human proteome (2015, background): “A complement to these efforts is the Human Protein Atlas program (14), which is exploring the human proteome using genecentric and genomewide antibody-based profiling on tissue microarrays.”