Reconstructing cell cycle and disease progression using deep learning is a research paper (2016). On theSindex it has a DataRank of 0.869. It has been cited 21 times, with 16 citing works in its 1-hop citation network.
We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by recon-structing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
0.405
From 14 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 53% comes from its base citations and 47% from the citation network (14 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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