Search Results for author:
Found 7 papers, 5 papers with code
CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data
Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution.
ImmunoLingo: Linguistics-based formalization of the antibody language
It would facilitate a better understanding of how differences and similarities between natural language and biological sequences influence the quality of LMs, which is crucial for the design of interpretable models with extractable sequence-functions relationship rules, such as the ones underlying the antibody specificity prediction problem.
Linguistically inspired roadmap for building biologically reliable protein language models
Differences between protein sequence data and linguistic sequence data require the integration of more domain-specific knowledge in protein LMs compared to natural language LMs.
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth.
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification
Multiple Instance Learning
+1
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
