Predicting the functional impact of KCNQ1 variants with artificial neural networks is a research paper published in PLoS Computational Biology (2022). On theSindex it has a DataRank of 0. It has been cited 9 times.
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NIDA NIH HHS
Grant: R01 DA046138
National Institutes of Health
Grant: NIH R01 GM129261
National Institutes of Health
Grant: NIH R01 DA046138
National Institutes of Health
Grant: NIH S10 OD016216
NIGMS NIH HHS
Grant: R01 GM080403
NIGMS NIH HHS
Grant: R01 GM129261
National Institutes of Health
Grant: NIH S10 OD020154
NIH HHS
Grant: S10 OD020154
National Institutes of Health
Grant: NIH R01 GM080403
NHLBI NIH HHS
Grant: R01 HL122010
National Institutes of Health
Grant: NIH R01 HL122010
NIH HHS
Grant: S10 OD016216
National Institutes of Health
Grant: 5R01HL122010-08
Decrypting Variants of Uncertain Significance in Long-QT Syndrome
National Institutes of Health
Grant: 5R01DA046138-04
Structural Determinants of Allosteric Modulation of Brain GPCRs
National Institutes of Health
Grant: 1S10OD020154-01
GPU-Accelerated Parallel Computer for Drug Discovery Applications
National Institutes of Health
Grant: 1S10OD016216-01
Parallel Computer with High Memory Nodes
National Institutes of Health
Grant: 3R01GM080403-03S1
Membrane Protein Structure Elucidation from sparse NMR data (KAMP)
National Institutes of Health
Grant: 5R01GM129261-04
Topological Energetics and the Cellular Quality Control of Integral Membrane Proteins
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