An intrinsically interpretable neural network architecture for sequence-to-function learning is a research paper published in Bioinformatics (2023). On theSindex it has a DataRank of 0. It has been cited 6 times.
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National Institutes of Health
Grant: R01EY030546
National Institutes of Health
Grant: U24DK112331
National Institutes of Health
Grant: R01HG009299
National Institutes of Health
Grant: R01HL127349
National Institutes of Health
Grant: R01HL159805
National Institutes of Health
Grant: R01HL157879
National Institutes of Health
Grant: R01AI04360321
DARPA
Grant: N6600119C4022
NSF
Grant: 2238125
CAREER: Concise descriptors of genomic data facilitate mechanistic inference
NIH HHS
Grant: S10 OD028483
National Institutes of Health
Grant: 5R01HL159805-07
Interpretable graphical models for large multi-modal COPD data (R01HL159805)
National Institutes of Health
Grant: 7R01HL157879-02
COPD SUBTYPES AND EARLY PREDICTION USING INTEGRATIVE PROBABILISTIC GRAPHICAL MODELS R01HL157879
National Institutes of Health
Grant: 3U24DK112331-01S1
PAGES: Physical Activity Genomics, Epigenomics/transcriptomics Site
National Institutes of Health
Grant: 5R01EY030546-02
Discovery and characterization of ocular regulatory elements through evolutionary analysis
National Institutes of Health
Grant: 1S10OD028483-01A1
High-Throughput Computing for Genomics and Bioinformatics Research
National Institutes of Health
Grant: 5R01HL127349-07
Genomic Analysis of Tissue and Cellular Heterogeneity in IPF
National Institutes of Health
Grant: 5R01HG009299-07
Title: Functional Annotation of Genomes via Phenotypic Convergence within Large Multi-species Alignments
FWCI
0.75
Citation Percentile
0.7%
Citation Trend
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