Machine learning papers on predict DNA post-replication modification



N4-methylcytosine

  1. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

    2. ⭕ Samples：C. elegans (2304 positive and 2304 negative), D. melanogaster (2769 positive and 2769 negative), A. thaliana (3228 positive and 3228 negative), E. coli (522 positive and 522 negative), G. subterraneus (1256 positive and 1256 negative), G. pickeringii (769 positive and 769 negative).

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  2. Iterative feature representations improve N4-methylcytosine site prediction

    3. ⭕ Samples：C. elegans (1554 positive and 1554 negative), D. melanogaster (1769 positive and 1769 negative), A. thaliana (1978 positive and 1978 negative), E. coli (388 positive and 388 negative), G. subterraneus (905 positive and 905 negative), G. pickeringii (569 positive and 569 negative).

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  3. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome

    4. ⭕ Samples：F. vesca (6471 positive and 6471 negative), R. chinensis (3116 positive and 3116 negative)

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  4. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

    5. ⭕ Samples：C. elegans (1554 positive and 1554 negative), D. melanogaster (1769 positive and 1769 negative), A. thaliana (1978 positive and 1978 negative), E. coli (388 positive and 388 negative), G. subterraneus (906 positive and 906 negative), G. pickeringii (569 positive and 569 negative).

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  5. 4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome

    6. ⭕ Samples：980 positive samples, 980 negative samples.

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  6. 4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction

    7. ⭕ Samples：C. elegans (1554 positive and 1554 negative), D. melanogaster (1769 positive and 1769 negative), A. thaliana (1978 positive and 1978 negative), E. coli (388 positive and 388 negative), G. subterraneus (905 positive and 906 negative), G. pickeringii (569 positive and 569 negative).

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  7. SOMM4mC: a second-order Markov model for DNA N4-methylcytosine site prediction in six species

    8. ⭕ Samples：C. elegans (1554 positive and 1554 negative), D. melanogaster (1769 positive and 1769 negative), A. thaliana (1978 positive and 1978 negative), E. coli (388 positive and 388 negative), G. subterraneus (906 positive and 906 negative), G. pickeringii (569 positive and 569 negative).

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  8. M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

    9. ⭕ Samples：906 positive samples, 906 negative samples.

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  9. DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species

    10. ⭕ Samples：C. elegans (750 positive and 750 negative), D. melanogaster (1000 positive and 1000 negative), A. thaliana (1250 positive and 1250 negative), E. coli (134 positive and 134 negative), G. subterraneus (350 positive and 350 negative), G. pickeringii (300 positive and 300 negative), M.musculus (180 positive and 180 negative) .

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  10. Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications

    11. ⭕ Samples：A. thaliana (20000 positive and 20000 negative), C. elegans (20000 positive and 20000 negative), D. melanogaster (20000 positive and 20000 negative).

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  11. A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

    12. ⭕ Samples：11173 positive samples and 6635 negative samples.

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N6-methyladenine

  1. SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome

    2. ⭕ Samples：154880 positive samples and 154880 negative samples.

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  2. SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome

    3. ⭕ Samples：2518 positive samples and 2518 negative samples.

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  3. iN6‑methylat (5‑step): identifying DNA N6‑methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5‑step rule

    4. ⭕ Samples：880 positive samples and 880 negative samples.

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  4. iIM-CNN: Intelligent Identifier of 6mA Sites on Different Species by Using Convolution Neural Network

    5. ⭕ Samples：cross-species (2768 positive and 2716 negative), rice (880 positive and 880 negative), M. musculus (1934 positive and 1934 negative).

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  5. iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice

    6. ⭕ Samples：154000 positive samples and 154000 negative samples.

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  6. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC

    7. ⭕ Samples：1934 positive samples and 1934 negative samples.

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  7. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

    8. ⭕ Samples：880 positive samples and 880 negative samples.

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  8. csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou's 5-step rule

    9. ⭕ Samples：cross-species (2768 positive and 2716 negative), rice (880 positive and 880 negative), M. musculus (1934 positive and 1934 negative).

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  9. 6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes

    10. ⭕ Samples：2768 positive samples and 2716 negative samples.

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  10. 6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

    11. ⭕ Samples：154880 positive samples and 154880 negative samples.

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  11. Identification of DNA N6‑methyladenine sites by integration of sequence features

    12. ⭕ Samples：3040 positive samples and 3040 negative samples.

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  12. MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model

    13. ⭕ Samples：880 positive samples and 880 negative samples.

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5-methylcytosine

  1. iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition

    2. ⭕ Samples：787 positive samples, 1639 negative samples.

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last update: 2020-6-30

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