Edo Dotan

This paper introduces BetaInfer, a generative model for phylogenetic tree inference that maps unaligned sequences to trees, achieving competitive performance compared to traditional methods.

This paper introduces BetaReconstruct, a generative model for ancestral sequence reconstruction that outperforms traditional methods without requiring multiple sequence alignments or phylogenetic trees.

BetaAlign uses deep learning to align biological sequences, achieving accuracy comparable to or better than existing tools.

The paper introduces BetaDescribe, a model that generates rich textual descriptions of proteins, enhancing understanding of their functions and properties.

This study evaluates how different tokenization methods affect transformer models' performance on various biological sequence tasks, showing improved accuracy and efficiency with optimized tokenizers.