CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction

Abstract

Motivation Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein–protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations. Results We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder-decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show cross-fused protein representations by multi-head attention mechanism is at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction. Availability The source code of CFAGO and experiments data are available at: http://bliulab.net/CFAGO/. Supplementary information Supplementary data are available at Bioinformatics online.

iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models

“…In recent years, novel natural language processing (NLP) techniques ( Ferruz and Höcker, 2022 ; Wu et al , 2023 ; Yan et al , 2023 ) (e.g. word embedding, attention mechanism and pre-trained language model) have shown the impressive ability to extract complex features from sentences.…”

DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

“…Recent protein function prediction methods rely on different sources of information such as sequence, interactions, protein tertiary structure, literature, coexpression, phylogenetic analysis, or the information provided in GO [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The methods may use sequence domain annotations [5,6,8,11,21], directly apply deep convolutional neural networks (CNN) [13] or language models such as LSTMs [9] and transformers [14], or use pretrained protein language models [10,15] to represent amino acid sequences.…”

“…The methods may use sequence domain annotations [5,6,8,11,21], directly apply deep convolutional neural networks (CNN) [13] or language models such as LSTMs [9] and transformers [14], or use pretrained protein language models [10,15] to represent amino acid sequences. Models may also incorporate protein-protein interactions through knowledge graph embeddings [12,16], approaches using k-nearest neighbors [21], and graph convolutional neural networks [6]. Also, natural language models applied to scientific literature have been successful in automated function prediction [8].…”

DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection

“…PPI networks provide additional information into how proteins work cooperatively to exert a certain function, which is difficult to determine directly from protein sequences or structures. Therefore, several deep learning methods have been proposed to combine PPI networks with sequence features (Fan et al ., 2020; Wu et al ., 2023) and achieve comparable performance with other state-of-the-art (SOTA) mixed-source ensemble models. According to the STRING PPI database (Szklarczyk et al ., 2023) there are seven types of evidence to define an interaction between two proteins: neighborhood, fusion, cooccurence, coexpression, experimental, database and textmining.…”

“…According to the STRING PPI database (Szklarczyk et al ., 2023) there are seven types of evidence to define an interaction between two proteins: neighborhood, fusion, cooccurence, coexpression, experimental, database and textmining. However, most of existing network-based methods simply concatenate or average over latent factors encoded from PPI networks of all types of evidence (Cho et al ., 2016; Gligorijević et al ., 2018), or use a combined PPI network that integrates edges from all evidence (Fan et al ., 2020; Wu et al ., 2023). As PPI networks from different evidence can be represented as graphs with an equal number of nodes but different numbers of edges, these networks have different topological properties including density and connectivity.…”