<jupyter_start><jupyter_text>Biofilm Inhibitory PeptidesResearch article: https://pubs.acs.org/doi/10.1021/acsomega.9b04119Supporting Link: https://pubs.acs.org/doi/suppl/10.1021/acsomega.9b04119/suppl_file/ao9b04119_si_001.pdf Issues* Positive and negative datasets are not available, links not working (SOLVED)* Data Construction (Need Clarification)* Dataset cannot be replicated due to unavailable raw data, raw data doesn't match* A total of 1024 features were obtained for the peptides of both groups from the PyDPI package and IAMPE tool* pydpi -  Data & Documentation?* IAMPE tool - not found Workflow![bip.png](bip.png) Dataset Links* Antimicrobial Peptide Database (APD) Information: http://aps.unmc.edu/AP/* APD Dataset: http://aps.unmc.edu/AP/APD3_update2020_release.fasta* dPABBs: http://ab-openlab.csir.res.in/abp/antibiofilm/down.php* Biofilm-active AMPs database: http://www.baamps.it/browse<jupyter_code># !pip install pydpi<jupyter_output><empty_output><jupyter_text>BAAMPS Dataset<jupyter_code>import pandas as pd

baamps=pd.read_csv('BAAMPs_data.csv')
baamps.head()
baamps.groupby(['PeptideID','PeptideName','PeptideSequence']).count()
len(baamps)<jupyter_output><empty_output><jupyter_text>A total of 202 unique peptides were downloaded from the BaAMP database, a comprehensive database of BIPs and APD3, another database of BIPs.  Negative Data<jupyter_code>with open('negative_data.txt','r') as file1:
    neg_read=file1.read()
    
neg_data=[]
for each in neg_read.split('\n'):
    neg_data.append(each[1:].split('\t'))
    
neg_df=pd.DataFrame(neg_data)
neg_df.head()
len(neg_df)<jupyter_output><empty_output><jupyter_text>Positive Data<jupyter_code>with open('positive_data.txt','r') as file2:
    pos_read=file2.read()
    
pos_data=[]
for each in pos_read.split('\n'):
    pos_data.append(each[1:].split('\t'))
    
pos_df=pd.DataFrame(pos_data)
pos_df.head()
len(pos_df)<jupyter_output><empty_output><jupyter_text>Independent Data<jupyter_code>data=pd.read_csv('independent_data.csv')
data
len(data)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12876579203109814, 'fraction_numerical': 0.025267249757045675, 'mean_word_length': 6.654275092936803, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '163125', 'license': 'MIT', 'n_tokens_mistral': 805, 'n_tokens_neox': 736, 'n_words': 147, 'path': 'Biofilm.ipynb', 'repo_name': 'jamwine/BioMed', 'size': '2058'}