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Associative knowledge graphs for efficient sequence storage and retrieval
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DOI：10.1016/j.cmpb.2025.108865.png)
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Abstract
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• This paper presents a new method for building associative knowledge graphs (AKG). • The AKGs excel at storing and identifying sequential information. • Presented AKGs are highly effective for storing and recognizing sequences. • To recover sequences, we utilize contextual information. • Providing a portion of the sequence triggers the retrieval of the complete sequence. • We confirmed these relationships with various sequence experiments. • The method efficiently identifies miRNA sequences, as an example demonstrates. • We provide access to the SSAKG package for Python. • It allows to test the presented concept and use it in other projects. • We provide application examples on GitHub (user-friendly Jupyter notebooks).
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NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveShareSaveShareSaveHow does generalization and creativity come into being in neural associative systems and how does it form human-like knowledge?NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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