PSL-LCCL: a resource for subcellular protein localization in liver cancer cell line SK_HEP1 is a dataset published in Database (2022). On theSindex it has a DataRank of 0.381, placing it in the top 51.9% of the data-sharing corpus. It has been cited 5 times, with 5 citing works in its 1-hop citation network. Its calibrated FAIR score is 43/100.
Abstract The characterization of subcellular protein localization provides a basis for further understanding cellular behaviors. A delineation of subcellular localization of proteins on cytosolic membrane-bound organelles in human liver cancer cell lines (hLCCLs) has yet to be performed. To obtain its proteome-wide view, we isolated and enriched six cytosolic membrane-bound organelles in one of the hLCCLs (SK_HEP1) and quantified their proteins using mass spectrometry. The vigorous selection of marker proteins and a machine-learning-based algorithm were implemented to localize proteins at cluster and neighborhood levels. We validated the performance of the proposed method by comparing the predicted subcellular protein localization with publicly available resources. The profiles enabled investigating the correlation of protein domains with their subcellular localization and colocalization of protein complex members. A subcellular proteome database for SK_HEP1, including (i) the subcellular protein localization and (ii) the subcellular locations of protein complex members and their interactions, was constructed. Our research provides resources for further research on hLCCLs proteomics. Database URL: http://www.igenetics.org.cn/project/PSL-LCCL/
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.269
From this paper's citation signal
Citation Network Contribution
0.112
From 5 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 71% comes from its base citations and 29% from the citation network (5 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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