Biotext Python Package

The Biotext Python Package bridges Bioinformatics and Natural Language Processing (NLP) by adapting biological sequence analysis techniques for Text Mining. At its core, Biotext utilizes the SWeeP algorithm — designed initially for biomolecular data — to introduce SWeePtex (SWeeP for text), a method for large-scale text representation and analysis. The package provides two text-to-BSL (Biological Sequence-Like) encoding schemes: AMINOcode, which models text in an amino acid-inspired (AAL) format, and DNAbits, which employs a nucleotide-like (NTL) representation. By transforming text into BSL, Biotext ensures compatibility with SWeeP, enabling applications in text similarity, clustering, and machine learning while maintaining computational efficiency.

Features

• aminocode: Implements AMINOcode. Encodes and decodes text using amino acid representations.
• dnabits: Implements DNAbits. Encodes and decodes text using DNA binary representations.
• sweeptex: Implements SWeePtex. Generates fixed-length vector representations of text using the SWeeP algorithm.
• sweeptex_emb: Implements Biotext Embedding. Processes text data through a pipeline to generate word and document embeddings.

Installation

pip install biotext

Modules

aminocode

Encode and decode text using amino acid representations.

from biotext import aminocode

# Encode a string
encoded = aminocode.encode_string("Hello world!", 'dp')
print(encoded)  # Output: 'HYELLYQYSYWYQRLDYPW'

# Decode a string
decoded = aminocode.decode_string("HYELLYQYSYWYQRLDYPW", 'dp')
print(decoded)  # Output: 'hello world!'

dnabits

Encode and decode text using DNA binary representations.

from biotext import dnabits

# Encode a string
encoded = dnabits.encode_string("Hello world!")
print(encoded)  # Output: 'AGACCCGCATGCATGCTTGCAAGATCTCTTGCGATCATGCACGCCAGA'

# Decode a string
decoded = dnabits.decode_string("AGACCCGCATGCATGCTTGCAAGATCTCTTGCGATCATGCACGCCAGA")
print(decoded)  # Output: 'Hello world!'

sweeptex

Generate fixed-length vector representations of text using the SWeePtex.

from biotext import sweeptex

corpus = ["This is a sample text", "Another text example"]
embeddings = sweeptex(corpus, emb_size=1200)
print(embeddings.shape)  # Output: (2, 1200)

sweeptex_emb

Process text data through a pipeline to generate word and document embeddings.

from biotext import sweeptex_emb

corpus = ["First document", "Second document text", "Third example"]
results = sweeptex_emb(corpus, return_doc_emb=True, return_word_emb=True)

print(results['doc_emb'].shape)  # Document embeddings
print(results['word_emb'].shape)  # Word embeddings

Authors

• Diogo de Jesus Soares Machado
• Roberto Tadeu Raittz

License

This project is licensed under a non-commercial license. See the LICENSE.txt file for details.