kmer2vec: A Novel Method for Comparing DNA Sequences by word2vec Embedding

Ren, Ruohan; Yin, Changchuan; Yau, Stephen S.-T.

doi:10.1089/cmb.2021.0536

Cited by 19 publications

(6 citation statements)

References 16 publications

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“…However, current mainstream feature coding methods typically encode DNA sequences in the form of individual vectors, which does not meet the input format requirements of our network framework. Therefore, we proposed three simple feature matrix coding methods and one complex feature matrix coding method, including One-hot coding, NCP coding, One-hot + NCP coding and Word2Vec coding [ 34 ]. Simple feature codes have only two numbers 0 and 1, they are very simple and efficient.…”

Section: Resultsmentioning

confidence: 99%

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

Jia,

Lei,

Qin

et al. 2024

BMC Genomics

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Background 5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites. Results Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively. Conclusions The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA.

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Section: Resultsmentioning

confidence: 99%

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

Jia,

Lei,

Qin

et al. 2024

BMC Genomics

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“…For example, researchers have adapted it for the construction of universal feature vectors for small molecules [9,27]. Also, it has been employed to create meaningful representations of nucleic acids for phylogenetic analysis [23], predicting drug-miRNA associations [8] and RNA degradation prediction [13]. Additionally, word2vec embeddings have been utilized for proteins in tasks such as drug-target interaction [30], drug-target affinity [32], protein-protein interaction [31], and others.…”

Section: Theorymentioning

confidence: 99%

A reagent-driven visual method for analyzing chemical reaction data

Andronov,

Andronova,

Wand

et al. 2024

Preprint

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The increasing use of machine learning and artificial intelligence in chemical reaction studies demands high-quality reaction data, necessitating specialized tools enabling data understanding and cura- tion. Our work introduces a novel methodology for reaction data exploration centered on reagents — essential molecules in reactions that do not contribute atoms to products. We propose an intu- itive tool for creating interactive reagent space maps using distributed vector representations, akin to word2vec in Natural Language Processing, capturing the statistics of reagent usage within datasets. Our approach enables swift assessment of reaction type distributions, identification of alternative reagents, and detection of errors, which we demonstrate using the USPTO dataset. Our contributions include an open-source web application for visual reagent pattern analysis and a table cataloging around six hundred of the most frequent reagents in USPTO annotated with detailed roles. Accessible via GitHub at https://github.com/Academich/reagent_emb_vis, our method supports organic chemists and cheminformatics experts in navigating extensive reaction datasets efficiently.

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“…Subsequently, we applied a novel method firstly introduced in this paper, referred to as merged CGR, to convert the promoter sequence into image data capturing evolutionary information. Alongside image information, we applied the kmer2vec method [34] to extract textual information from the promoter sequences.…”

Section: Overview Of the Model Architecturementioning

confidence: 99%

“…To better extract textual information from promoter sequences, we employed the word2vec method to obtain k-mer word embeddings in promoter sequences, following the specific strategies of the kmer2vec method [34]. Initially, we divided all promoter sequences in dataset_pro into a series of 3-mers using an overlapping division, treating them as complete text.…”

Section: Word2vec Word Embeddingmentioning

confidence: 99%

Evolution is All You Need in Promoter Design and Optimization

Ren,

Yu,

Teng

et al. 2023

Preprint

Self Cite

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Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using chaos game representation and process the overall information with DenseNet and Transformer. Our model achieves state-of-the-art results on two kinds of distinct tasks. The incorporation of evolutionary information enhances the model’s accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE’s efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters.

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kmer2vec: A Novel Method for Comparing DNA Sequences by word2vec Embedding

Cited by 19 publications

References 16 publications

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

A reagent-driven visual method for analyzing chemical reaction data

Evolution is All You Need in Promoter Design and Optimization

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