Mariah Salcedo

Biochemistry, Genetics and Molecular BiologyComputer ScienceMedicine

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 4 papers. Top paper: “Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions”.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions

Bioinformatics(2023)10.1093/bioinformatics/btad046

N/A

0.533DataRank · unranked

34 citations

Wayland Yeung, Nathan Gravel, Mariah Salcedo, Saber Soleymani, Sheng Li +2 more

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

PeerJ(2023)10.7717/peerj.15815

N/A

0.266DataRank · unranked

3 citations

Nathan Gravel, Abbas Keshavarzi, Liang‐Chin Huang, Krzysztof Kochut, Natarajan Kannan +1 more

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies

Briefings in Bioinformatics(2023)10.1093/bib/bbac619

N/A

0DataRank · unranked

27 citations

Zhongliang Zhou, Liju Mathew, Nathan Gravel, Rahil Taujale, Brady O’Boyle +6 more

Using explainable machine learning to uncover the kinase–substrate interaction landscape

Bioinformatics(2024)10.1093/bioinformatics/btae033

N/A

0DataRank · unranked

15 citations

Wayland Yeung, Saber Soleymani, Nathan Gravel, Mariah Salcedo, Sheng Li +2 more