Author

Kalli Kappel

Broad InstituteORCID 0000-0002-2129-199XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Biophysics· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Rhiju DasiD· 2 shared Anna ShcherbinaiD· 1 shared Anshul Bharat KundajeiD· 1 shared Cameron AndressiD· 1 shared Fang-Chieh Chou· 1 shared Gokul RamaswamiiD· 1 shared Hongbin YaniD· 1 shared Inga JarmoskaiteiD· 1 shared

Activity by Year

2016-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersMolecular Biology

Rhiju DasORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Cameron Andress, Kalli Kappel, Marcus Elbert Villena, Miroslava Čuperlović‐Culf, Hongbin Yan, Yifeng Li

This paper introduces DAPTEV, an AI framework for designing aptamers targeting the SARS-CoV-2 spike protein, showing it can generate structurally complex aptamers with strong binding affinities.

PLoS Computational Biology · Jul 5, 2023Peer-reviewedBioinformatics

Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Xin Liu, Tao Sun, Anna Shcherbina, Qin Li, Inga Jarmoskaite, Kalli Kappel, Gokul Ramaswami, Rhiju Das, Anshul Bharat Kundaje, Jin Billy Li

The paper develops a CRISPR-based mutagenesis approach combined with machine learning to model cis-regulatory features of ADAR-mediated RNA editing, explaining over 70% of editing level variation.

Nature Communications · Apr 12, 2021Peer-reviewedMolecular Biology

Blind tests of RNA nearest neighbor energy prediction

Fang-Chieh Chou, Wipapat Kladwang, Kalli Kappel, Rhiju Das

This paper presents RECCES-Rosetta, a computational framework that accurately predicts RNA nearest neighbor energy parameters, including non-standard nucleotides, with RMSD below 1 kcal/mol in blind tests.

Proceedings of the National Academy of Sciences · Jul 8, 2016Peer-reviewedBiophysics