2 citations · 2 across the 11 of their papers we have counts for
13 papers
Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression
Alberto Caron, Tianyu Cui, Dmytro S. Lituiev +13
Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA…
PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
Azim Dehghani Amirabad, Junchao Zhu, Pushpak Pati +3
Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, e…
HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modeling
Max van Spengler, Artem Moskalev, Tommaso Mansi +2
Language models are increasingly applied to biological sequences like proteins and mRNA, yet their default Euclidean geometry may mismatch the hierarchical structures inherent to b…
GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction
Feng Jiang, Amina Mollaysa, Hehuan Ma +4
Drug target interaction (DTI) prediction is a cornerstone of computational drug discovery, enabling rational design, repurposing, and mechanistic insights. While deep learning has…
BioBO: Biology-informed Bayesian Optimization for Perturbation Design
Yanke Li, Tianyu Cui, Tommaso Mansi +2
Efficient design of genomic perturbation experiments is crucial for accelerating drug discovery and therapeutic target identification, yet exhaustive perturbation of the human geno…
Multimodal Modeling of CRISPR-Cas12 Activity Using Foundation Models and Chromatin Accessibility Data
Azim Dehghani Amirabad, Yanfei Zhang, Artem Moskalev +5
Predicting guide RNA (gRNA) activity is critical for effective CRISPR-Cas12 genome editing but remains challenging due to limited data, variation across protospacer adjacent motifs…
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models
Amina Mollaysa, Artem Moskale, Pushpak Pati +3
We present BioLangFusion, a simple approach for integrating pre-trained DNA, mRNA, and protein language models into unified molecular representations. Motivated by the central dogm…
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Feng Jiang, Mangal Prakash, Hehuan Ma +6
Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as a powerful paradigm to l…
Geometric Hyena Networks for Large-scale Equivariant Learning
Artem Moskalev, Mangal Prakash, Junjie Xu +3
Processing global geometric context while preserving equivariance is crucial when modeling biological, chemical, and physical systems. Yet, this is challenging due to the computati…
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
Tianyu Cui, Song-Jun Xu, Artem Moskalev +4
Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performa…
HELM: Hierarchical Encoding for mRNA Language Modeling
Mehdi Yazdani-Jahromi, Mangal Prakash, Tommaso Mansi +2
Messenger RNA (mRNA) plays a crucial role in protein synthesis, with its codon structure directly impacting biological properties. While Language Models (LMs) have shown promise in…
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
Junjie Xu, Artem Moskalev, Tommaso Mansi +2
Accurate prediction of RNA properties, such as stability and interactions, is crucial for advancing our understanding of biological processes and developing RNA-based therapeutics.…
SE(3)-Hyena Operator for Scalable Equivariant Learning
Artem Moskalev, Mangal Prakash, Rui Liao +1
Modeling global geometric context while maintaining equivariance is crucial for accurate predictions in many fields such as biology, chemistry, or vision. Yet, this is challenging…