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Found 11 papers, 5 papers with code
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion
In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise.
ProGen2: Exploring the Boundaries of Protein Language Models
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
FLIP: Benchmark tasks in fitness landscape inference for proteins
Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications.
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models
Predicting the fitness, i. e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data.
Deep Extrapolation for Attribute-Enhanced Generation
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution.
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks.
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Transformer architectures have proven to learn useful representations for protein classification and generation tasks.
ProGen: Language Modeling for Protein Generation
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks
Graph neural networks are a quickly emerging field for non-Euclidean data that leverage the inherent graphical structure to predict node, edge, and global-level properties of a system.
Bimodal network architectures for automatic generation of image annotation from text
The source of big data is typically large image collections and clinical reports recorded for these images.
Fast and accurate classification of echocardiograms using deep learning
The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views.
