Author

Lucas A. N. Melo

Columbia UniversityORCID 0000-0002-7563-7554Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 3 Research Focus:benchmarking· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Harmen J. BussemakeriD· 4 shared Xiaoting LiiD· 3 shared Chaitanya RastogiiD· 2 shared H. Tomas RubeiD· 2 shared Neel H. ShahiD· 2 shared Richard S. ManniD· 2 shared Allyson LiiD· 1 shared Bach Viet· 1 shared

Activity by Year

2022-2025 · peak 2/yr

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10 top collaborators · 38 mutual co-author links

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4 shared papersGenomics

Harmen J. BussemakerORCID

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4 papers

Recent·Most cited

Predicting the DNA binding specificity of transcription factor mutants usingfamily-level biophysically interpretable machine learning

Shaoxun Liu, Pilar Gomez-Alcala, Christ Leemans, William J. Glassford, Lucas A. N. Melo, Xiang‐Jun Lu, Richard S. Mann, Harmen J. Bussemaker

The paper develops a method to predict how mutations in transcription factors affect their DNA binding specificity using biophysically interpretable machine learning.

Nucleic Acids Research · Aug 8, 2025Peer-reviewedGenomics

Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression

Dejan Gagoski, H. Tomas Rube, Chaitanya Rastogi, Lucas A. N. Melo, Xiaoting Li, Rashmi Voleti, Neel H. Shah, Harmen J. Bussemaker

The paper develops a sequence-to-affinity model for SH2 domains using multi-round peptide binding assays and free-energy regression to predict binding specificity and variant effects.

bioRxiv · Dec 23, 2024 · PreprintSystems Biology

Benchmarking DNA binding affinity models using allele-specific transcription factor binding data

Xiaoting Li, Lucas A. N. Melo, Harmen J. Bussemaker

The paper benchmarks DNA binding affinity models using allele-specific ChIP-seq data to predict allelic imbalances and improve TF binding models.

Genome biology · Oct 31, 2024Peer-reviewedGenomics

Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

H. Tomas Rube, Chaitanya Rastogi, Siqian Feng, Judith F. Kribelbauer-Swietek, Allyson Li, Basheer Becerra, Lucas A. N. Melo, Bach Viet, Xiaoting Li, Hammaad Adam, Neel H. Shah, Richard S. Mann, Harmen J. Bussemaker

The paper introduces ProBound, a machine learning framework that quantifies biophysical parameters of protein-ligand interactions using NGS data, and couples it with Kd-seq to determine absolute affinities.

Nature Biotechnology · May 23, 2022Peer-reviewedGenomics