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KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

ABSTRACT: This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and machine learning techniques for feature extraction. However, the growing volume of sequences poses processing challenges. This study introduces KDeep, employing a CNN-LSTM architecture with a novel encoding method called 2Lk. 2Lk enhances prediction accuracy, reduces memory consumption by up to 84%, reduces trainable parameters, and improves interpretability by approximately 79% compared to state-of-the-art approaches. KDeep offers a promising solution for accurate and efficient binding site prediction.

SUBMITTER: Akbari Rokn Abadi S

PROVIDER: S-EPMC10580661 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

Akbari Rokn Abadi Saeedeh S Tabatabaei SeyedehFatemeh S Koohi Somayyeh S

Journal of translational medicine 20231016 1

This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and machine learning techniques for feature extraction. However, the growing volume of sequences poses processing challenges. This study introduces KDeep, employing a CNN-LSTM architecture with a novel encoding method called 2Lk. 2Lk enhances prediction ...[more]

PMID: 37845681

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