Protein Function Prediction
23 papers with code • 3 benchmarks • 2 datasets
For GO terms prediction, given the specific function prediction instruction and a protein sequence, models characterize the protein functions using the GO terms presented in three different domains (cellular component, biological process, and molecular function).
Benchmarks
These leaderboards are used to track progress in Protein Function Prediction
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FLIP
Latest papers with no code
ProTranslator: zero-shot protein function prediction using textual description
Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we do not need to know any associated proteins for this function.
λ-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences
Attention-based deep networks have been successfully applied on textual data in the field of NLP.
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction
We propose an effective method to improve Protein Function Prediction (PFP) utilizing hierarchical features of Gene Ontology (GO) terms.
Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction
In contrast, most of the existing methods delete the rare protein functions to reduce the label space.
Random Embeddings and Linear Regression can Predict Protein Function
Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction.
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries.
Hierachial Protein Function Prediction with Tails-GNNs
Protein function prediction may be framed as predicting subgraphs (with certain closure properties) of a directed acyclic graph describing the hierarchy of protein functions.
Combining graph and sequence information to learn protein representations
Using these representations, we train machine learning models that outperform existing methods on the task of tissue-specific protein function prediction on 10 out of 13 tissues.
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful.
Using Ontologies To Improve Performance In Massively Multi-label Prediction
Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions.
