32 citations · 32 across the 2 of their papers we have counts for
3 papers
dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning
Arnav Shah, Junzhe Li, Parsa Idehpour +7
Genomic foundation models have the potential to decode DNA syntax, yet face a fundamental tradeoff in their input representation. Standard fixed-vocabulary tokenizers fragment biol…
FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction
Adamo Young, Fei Wang, David S Wishart +3
Compound identification from tandem mass spectrometry (MS/MS) data is a critical step in the analysis of complex mixtures. Typical solutions for the MS/MS spectrum to compound (MS2…
To Transformers and Beyond: Large Language Models for the Genome
Micaela E. Consens, Cameron Dufault, Michael Wainberg +6
In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges. This review focuses on the transformative r…