Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function

Abstract

Motivation With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functional therapeutic peptides (MFTP) via sequence-based computational tools. Results Here we propose a novel multi-label-based method, named ETFC, to predict 21 categories of therapeutic peptides. The method utilizes a deep learning-based model architecture, which consists of four blocks: embedding, text convolutional neural network, feed-forward network, and classification blocks. This method also adopts an imbalanced learning strategy with a novel multi-label focal dice loss function (MLFDL). MLFDL is applied in the ETFC method to solve the inherent imbalance problem in the multi-label dataset and achieve competitive performance. The experimental results state that the ETFC method is significantly better than the existing methods for MFTP prediction. With the established framework, we use the teacher-student-based knowledge distillation to obtain the attention weight from the self-attention mechanism in the MFTP prediction, and quantify their contributions towards each of the investigated activities. Availability The source code and dataset are available via: https://github.com/xialab-ahu/ETFC. Supplementary information Supplementary data are available at Bioinformatics online.

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction

“…We compared the proposed CELA-MFP with various existing methods, including four machine learning-based methods (CLR [ 43 ], RAKEL [ 44 ], RBRL [ 45 ], and MLDF [ 46 ]) and five deep learning-based methods (MPMABP [ 29 ], MLBP [ 30 ], PrMFTP [ 31 ], ETFC [ 32 ], and iMFP-LG [ 33 ]). Based on the performance evaluation conducted on the MFBP dataset ( Fig.…”

“…To better tackle the issue of sample imbalance, we employ a multi-label focal dice loss (MLFDLoss), which is also used in ETFC [ 32 ]. According to our experiments and results, we find it can work well.…”

“…Additionally, PrMFTP addresses the challenge of imbalanced data by using class-weighting optimization. ETFC [ 32 ] utilizes a loss function to alleviate the limitation of the imbalanced data. Additionally, iMFP-LG [ 33 ] proposes a method that utilizes a pLM and a graph attention mechanism to enhance the discovery of multi-functional peptides.…”

ACP-DRL: an anticancer peptides recognition method based on deep representation learning

“…This design introduces the BERT-based protein large language model and further pre-training techniques into the ACPs recognition for the first time, eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods. In recent years, recognizing various functional peptides like MFTP ( Fan et al, 2023 ), MLBP( Tang et al, 2022 ), and PrMFTP ( Yan et al, 2022 ) has seen significant advancements. These methods universally use encoders to transition peptide sequences into vectors.…”