AIMC Topic: Sequence Analysis, Protein

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Coevolutionary signals in multiple sequence alignments improve virulence factor prediction with an MSA Transformer.

Scientific reports
Identification of virulence factors (VFs) is critical for expanding our knowledge on bacterial pathogenesis and also for developing targeted strategies for the prevention and treatment of related infectious diseases. Understanding virulence factors r...

From Signal to Symphony: Exploring 2D Sequence Representations for Protein Function Prediction.

Journal of chemical information and modeling
Predicting protein function from its primary sequence is a fundamental challenge in computational biology. While deep learning has excelled, the optimal representation of sequence data remains an open question. This study explores protein sonificatio...

OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders.

PLoS computational biology
Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal Deep Learning model for proteins that integrates stru...

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one
Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is neede...

Rprot-Vec: a deep learning approach for fast protein structure similarity calculation.

BMC bioinformatics
BACKGROUND: Predicting protein structural similarity and detecting homologous sequences remain fundamental and challenging tasks in computational biology. Accurate identification of structural homologs enables function inference for newly discovered ...

A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

The AI revolution comes to protein sequencing.

Science (New York, N.Y.)
By identifying unknown proteins, new systems could aid research in many areas.

TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.

Communications biology
Identifying DNA-binding proteins and their binding residues is critical for understanding diverse biological processes, but conventional experimental approaches are slow and costly. Existing machine learning methods, while faster, often lack accuracy...

SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq.

IEEE journal of biomedical and health informatics
In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has be...

Predicting protein-protein interaction with interpretable bilinear attention network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein function and disease pathology. Identification of PPIs and their interaction types through wet experime...