# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 1 Introduction  
Fungal infections pose a significant threat to human health, affecting over one billion people worldwide annually [1]. Unlike bacteria, fungi share similar biological characteristics with mammalian cells as eukaryotes, making it challenging to develop antifungal drugs [2]. Currently, the clinical treatment options for fungal infections are limited to polyenes, azoles, echinocandins, and a few auxiliary drugs like Dictyosine, which are constrained by fungal resistance and have drug toxicity side effects [3]. Therefore, there is a critical need to expand the repertoire of antifungal drugs [4]. Antifungal peptides (AFPs) represent a class of naturally occurring peptides produced by organisms as a defense mechanism against fungal pathogens [5]. Typically consisting of 10-100 amino acids, AFPs are amphipathic. AFPs have low toxicity and high efficiency. Due to these favorable characteristics, they have emerged as promising alternatives to chemical antifungal agents [6]. In contrast to traditional antifungal drugs, AFPs exhibit diverse modes of action, such as disrupting fungal cell membranes or inducing the production of reactive oxygen species (ROS) [7]. Identifying AFPs experimentally is time-consuming and costly, especially for the pre-screening of a large number of AFP candidates. Therefore, there is a pressing need for computational models that can rapidly and accurately predict AFPs [8]. In recent years, a wide range of machine-learning-based approaches have been proposed to predict antifungal peptides (AFPs). For instance, [PERSON] et al. introduced a novel computational model called AFP-MFL (multi-view feature learning) for accurately identifying antifungal peptides (AFPs) by integrating different feature groups [9]. Later,Agrawal et al. [10] employed a combination of amino acid composition (AAC), dipeptide composition (DPC), split amino acid composition, and binary profiles to characterize peptides, subsequently utilizing support vector machine (SVM) classifier to construct prediction models [11]. More recently, [PERSON] et al. introduced a feature fusion scheme to integrate diverse peptide features, which were then used to train a deep neural network (DNN) for prediction purposes [12]. Later, [PERSON] et al. proposed another innovative computational model for AFP prediction using sequential and evolutionary information extracted from peptides and employing a minimum redundancy and maximum relevance (mRMR) based method for feature extraction [13]. Most recently, [PERSON] et al. proposed a machine-learning-based approach for accurately identifying and classifying AFPs by developing a comprehensive dataset of known AFPs and applying various feature extraction techniques to represent peptide sequences [14]. Most existing studies heavily rely on expert-knowledge-based handcrafted features to characterize intrinsic peptide properties [15]. These approaches need help with handling short peptide sequences. For instance, descriptors such as AAC, DPC, and reduced amino acid alphabet composition (RAAAC) only consider the frequency of individual amino acid residues, overlooking the sequential order of amino acids in the peptide sequence. The integration of different feature vectors into a high-dimensional feature space has been used to achieve a more expressive feature representation [16]. Nevertheless, this often leads to the curse of dimensionality, introducing redundant information and resulting in heightened computational complexity [17]. To address these issues, we present AFP_MVFL, a new machine learning model based on multi-view feature learning (MVFL) aimed at accurately identifying AFPs. The AFP-MVFL model leverages a diverse range of sequence-based information and physicochemical properties to comprehensively represent peptide characteristics [10]. By incorporating multiple properties of peptides, our model enhances its ability to capture patterns underlying antifungal activity.