Opportunities and obstacles for deep learning in biology and medicine is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2017). On theSindex it has a DataRank of 11.2. It has been cited 310 times, with 200 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.861
From this paper's citation signal
Citation Network Contribution
10.3
From 200 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 200 citers.
Natural Sciences and Engineering Research Council of Canada
Grant: unidentified
unidentified
National Institutes of Health
Grant: 1DP2GM123485-01
Deep learning frameworks for regulatory genomics.
National Institutes of Health
Grant: 1R01LM012482-01
Computationally modeling the impact of ontogeny on drug metabolic fate
National Science Foundation
Grant: 1245632
Hypothesis-driven Computational Genomics: Engaging Students in Lab Protocols and Bioinformatics via Inquiry
National Institutes of Health
Grant: 5R01AI116794-03
Biomedical Computing and Informatics Strategies for Infectious Disease Research
National Institutes of Health
Grant: 5U54AI117924-02
BD2K Center Consortium
National Institutes of Health
Grant: 5U54AI117924-05
The Center for Predictive Computational Phenotyping-1 Overall
National Institutes of Health
Grant: 5R21CA220398-02
CGDnet: Cancer Gene Drug Network: Using patient-specific drug-gene networks for recommending targeted cancer therapies.
National Institutes of Health
Grant: 3T32HG000046-15S1
Training Grant in Computational Genomics
National Institutes of Health
Grant: 2R01GM089753-06A1
New Computational Methods for Data-driven Protein Structure Prediction
National Institutes of Health
Grant: 3P30CA051008-16S1
CANCER CENTER SUPPORT GRANT
National Institutes of Health
Grant: 3T32GM007753-41S1
Medical Scientist Training Program
National Institutes of Health
Grant: 1R01LM012222-01
DATA AND TOOLS FOR MODELING METABOLISM AND REACTIVITY
National Science Foundation
Grant: 1531594
MRI: Acquisition of High Performance Scientific Computing Cluster at Trinity University
National Institutes of Health
Grant: 2R01GM089652-05A1
Continued Development of CellProfiler Cell Image Analysis Software
Fields of Study
Keywords
Sustainable Development Goals
Additional file 1 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 1 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 2 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 2 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 3 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 3 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 4 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology
Additional file 4 of An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology