May 2, 2024 · We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear ...

As a sequence-based deep learning method for predicting protein function, MMSMAPlus is trained on multi-view sequence features of proteins, which can rapidly ...

There are a variety of genomic data that are relevant to a protein's function: its sequence, its interactions with other proteins, expression of its gene, etc.

The problem of predicting protein function using Gene Ontology terms is a hierarchical classification problem. There are a variety of genomic data that are ...

In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and ...

We propose ATP-Deep, a novel protein-ATP binding residues predictor. ATP-Deep harnesses the capabilities of unsupervised pre-trained language models.

May 31, 2023 · We propose a multi-view, multi-scale and multi-attention deep neural model (MMSMA) for protein function prediction.

PDF | The problem of predicting protein function using Gene Ontology terms is a hierarchical classification problem. There are a variety of genomic data.

Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the ...

Feb 26, 2024 · We present GO-LTR, a multi-view multi-label prediction model for automatic function annotation, based on the latent tensor reconstruction ...