## 2 Materials and Methods
### In Silico Data Preparation  
Creating a new dataset for protein sequence analysis is painstaking as a significant amount of time and resources are required to cultivate and sequence the target organism. It has attracted considerable attention worldwide as a unified public database for researchers to share their sequencing results. The Universal Protein Resource Knowledge-base (UniProtKB) [31] is a centralized database of protein sequences, each labeled with their unique functional description. The database comprises data gathered from the European Bioinformatics Institute (EMBL-EBI), Swiss Institute of Bioinformatics (SIB), and Protein Information Resource (PIR); the database provides both reviewed (Swiss-Prot) and computer-annotated unreviewed protein sequences (TrEMBL). In our previous work, we collected bacterial proteins from UniProt by searching all relevant organisms under the bacteria domain. From the data gathered, we labeled the sequences according to their Gene Ontology (GO) matchings [32]. For the fungi data that we use in this paper, we followed the same technique that we have used in our previous paper and collected all relevant organisms under the kingdom fungi. However, unlike our previous work, we did not label fungal protein sequences with their GO. While GO provides great insights into the properties of a certain protein, our goal was to tag a protein sequence with a comprehensive description such as virulence or toxin. We defined certain keywords and opted to identify every protein sequence with such keywords in their description. The main focus of an in silico model is to determine whether a protein can create toxins or cause virulent activity. Thus, we chose to label protein sequences with \"virulent\"for virulent activity or virulence factors, \"toxic\" for toxic activity or toxins, and \"normal\" for non-virulent activity. The keywords used to find such protein sequences are \"virul\" for virulent activity and \"toxi\" for toxic activity. When labeling the protein sequences, we labeled any sequences with the keyword \"anti-toxi\" as \"normal\" because they are not related to any toxic activity. Algorithm 1 shows the pseudocode for our protein sequence labeling strategy. ```
for protein sequences do if length of protein sequences \(\leq\) 1024 then if description contains \(virul\)then label sequence as \"virulent\" else if description contains \(toxi\)then if description contains \(anti-toxin\)then label sequence as \"normal\" else label sequences as \"toxin\" endif else label sequences as \"normal\" endif endif endfor
```  
**Algorithm 1** Pseudo Code of In Silico Data Collection  
In total, 15,660,390 sequences were collected using this process. Before utilizing the collected data to train the in silico model, we excluded the sequences of 32 species of fungi from the initial training data. The purpose of this study was to use the data of the same 32 species collected during the indoor airborne fungi collection mentioned above in Section 2.1.1 as inference data for a fair comparison of the reliability of the in silico model with the in vitro experiments. As a portion of the collected fungi species are native to the Korean peninsula, half of the species protein data were unknown or unavailable in the UniProtKB database. Therefore, we made predictions for the sequences of the 15 species in which data were available in UniProtKB. Using the trained in silico model, we compared the results of the 15 fungi species data with the in vitro experiments. Table 1 shows the sequences collected for each class of the 15 species. After excluding the above-mentioned data, we collected 15,643,956 of normal sequences, 11,828 of toxin sequences, and 4606 of virulent sequences for each class. However, in this case, the dataset is highly imbalanced. It is well known that imbalanced data can result in the failure of a model to classify protein sequences accurately in the minority class.