Deep learning papers on sequence classification

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DNA/RNA Chapter

  1. iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks

    2. ▲ Authors: Quang H. Nguyen, Thanh-Hoang Nguyen-Vo, Nguyen Quoc Khanh Le, Trang T.T. Do, Susanto Rahardja* and Binh P. Nguyen*

    ▲ Periodical: BMC Genomics, 2019

    ▲ Classification target：enhancers

    ▲ Samples：742 strong enhancers, 742 weak enhancers, 1484 non- enhancers.

    Data Download (240KB)

  2. Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

    3. ▲ Authors: Nguyen Quoc Khanh Le*, Edward Kien Yee Yapp, N. Nagasundaram and Hui-Yuan Yeh*

    ▲ Periodical: Frontiers in Bioengineering and Biotechnology, 2019

    ▲ Classification target：promoter

    ▲ Samples：1591 strong core promoters, 1791 weak core promoters, 3382 non-promoter sequences.

    Data Download (242KB)

  3. Deep neural networks for human microRNA precursor detection

    4. ▲ Authors: Xueming Zheng, Xingli Fu, Kaicheng Wang and Meng Wang*

    ▲ Periodical: BMC Bioinformatics, 2020

    ▲ Classification target： human microRNA precursor

    ▲ Samples：1881 positive samples, 8492 negative samples.

    Data Download (516KB)

  4. BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone

    5. ▲ Authors: Bite Yang, Feng Liu, Chao Ren, Zhangyi Ouyang, Ziwei Xie, Xiaochen Bo* and Wenjie Shu*

    ▲ Periodical: Bioinformatics, 2017

    ▲ Classification target：enhancers

    ▲ Samples：datasets of human and mouse.

    Data Download (5.45GB), network disk extraction code is jhl7

Proteins/Peptide Chapter

  1. On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach

    2. ▲ Authors: Yu-Hui Qu, Hua Yu, Xiu-Jun Gong*, Jia-Hui Xu, Hong-Shun Lee

    ▲ Periodical: PLOS ONE, 2017

    ▲ Classification target：DNA-binding proteins

    ▲ Samples：42,257 DNA-binding, 341,481 non-DNA-binding.

    Data Download (80.7MB)

  2. SNARE-CNN: a 2D convolutional neural network architecture to identif SNARE proteins from high-throughput sequencing data

    3. ▲ Authors: Nguyen Quoc Khanh Le and Van-Nui Nguyen

    ▲ Periodical: PeerJ Computer Science, 2019

    ▲ Classification target：SNARE proteins

    ▲ Samples：682 SNARE proteins, 2583 non-SNARE proteins.

    Data Download (5.35MB)

  3. Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture

    4. ▲ Authors: Nguyen Quoc Khanh Le*, Edward Kien Yee Yapp, N. Nagasundaram, Matthew Chin Heng Chua, Hui-Yuan Yeh*

    ▲ Periodical: Computational and Structural Biotechnology Journal, 2019

    ▲ Classification target：vesicular transport proteins

    ▲ Samples：2533 vesicular transport proteins and 9086 non-vesicular transport proteins.

    Data Download (14.2MB)

  4. Predicting RNA–protein binding sites and motifs through combining local and global deep convolutional neural networks

    5. ▲ Authors: Xiaoyong Pan* and Hong-Bin Shen*

    ▲ Periodical: Bioinformatics, 2018

    ▲ Prediction target：RNA–protein binding sites and motifs

    ▲ Samples：24 experiments of 21 RBPs and 47 RBPs with over 2000 binding sites.

    Data Download (141MB)

  5. Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning

    6. ▲ Authors: Lei Guo, Shunfang Wang*, Mingyuan Li and Zicheng Cao

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target：membrane protein types

    ▲ Samples：two datasets with 8 membrane protein types

    Data Download (4.47 MB)

  6. DeepEP: a deep learning framework for identifying essential proteins

    7. ▲ Authors: Min Zeng, Min Li*, Fang-Xiang Wu, Yaohang Li and Yi Pan

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target：essential proteins

    ▲ Samples： three kinds of biological datasets

    Data Download (19.6 MB)

  7. Classification of adaptor proteins using recurrent neural networks and PSSM profiles

    8. ▲ Authors: Nguyen Quoc Khanh Le, Quang H. Nguyen, Xuan Chen, Susanto Rahardja* and Binh P. Nguyen*

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target：adaptor proteins

    ▲ Samples： 1,224 adaptor proteins and 11,078 non-adaptor proteins

    Data Download (246 MB) (PSSM profiles)

  8. DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks

    9. ▲ Authors:Castrense Savojardo, Niccolò Bruciaferri, Giacomo Tartari, Pier Luigi Martelli*, Rita Casadio

    ▲ Periodical: Bioinformatics, 2019

    ▲ Prediction target：protein submitochondrial cellular localization

    ▲ Samples： three datasets

    Data and Code Download (18.8 MB)

  9. Antimicrobial peptide identification using multi-scale convolutional network

    10. ▲ Authors: Xin Su, Jing Xu, Yanbin Yin, Xiongwen Quan and Han Zhang*

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target：Antimicrobial peptide

    ▲ Samples：four datasets with positive samples and negative samples

    Data Download (488KB)

  10. MiPepid: MicroPeptide identification tool using machine learning

    11. ▲ Authors:Mengmeng Zhu and Michael Gribskov*

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target： MicroPeptide

    ▲ Samples：4017 positive samples, 2936 negative samples

    Data Download (0.99MB)

  11. DeepSig: deep learning improves signal peptide detection in proteins

    12. ▲ Authors:Castrense Savojardo, Pier Luigi Martelli*, Piero Fariselli, Rita Casadio

    ▲ Periodical: Bioinformatics, 2018

    ▲ Classification target：signal peptide

    ▲ Samples：three datasets

    Data Download (1.40MB)

  12. DeepAVP: a dual-channel deep neural network for identifying variable-length antiviral peptides

    13. ▲ Authors:Jiawei Li, Yuqian Pu, Jijun Tang*, Quan Zou*, Fei Guo*

    ▲ Periodical: IEEE journal of biomedical and health informatics, 2020

    ▲ Classification target：variable-length antiviral peptides

    ▲ Samples：two datasets，and the new datasets obtain 413 AVPs and 348 non-AVPs

    Data Download (98KB)

Interactions Chapter

  1. iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

    2. ▲ Authors: Binh P. Nguyen*, Quang H. Nguyen, Giang-Nam Doan-Ngoc, Thanh-Hoang Nguyen-Vo and Susanto Rahardja*

    ▲ Periodical: BMC Bioinformatics, 2019

    ▲ Classification target：protein-DNA binding residues

    ▲ Samples：10,283 positive samples, 149,016 negative samples.

    Data Download (155KB)

  2. RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

    3. ▲ Authors: Cheng Peng, Siyu Han, Hui Zhang and Ying Li*

    ▲ Periodical: International Journal of Molecular Sciences, 2019

    ▲ Prediction target：ncRNA–Protein Interaction

    ▲ Samples：four datasets with interaction paris and non-interaction paris

    Data Download (5.12MB)

  3. RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

    4. ▲ Authors: Hai-Cheng Yi, Zhu-Hong You* , Mei-Neng Wang, Zhen-Hao Guo, Yan-Bin Wang and Ji-Ren Zhou

    ▲ Periodical: BMC Bioinformatics, 2020

    ▲ Prediction target：ncRNA-protein interactions

    ▲ Samples：three datasets with interaction paris and non-interaction paris

    Data Download (2.34 MB)

  4. A multimodal deep learning framework for predicting drug-drug interaction events

    5. ▲ Authors: Deng, Yifan; Xu, Xinran; Qiu, Yang; Xia, Jingbo; Zhang, Wen*; Liu, Shichao*

    ▲ Periodical: Bioinformatics, 2020

    ▲ Prediction target：drug-drug interaction events

    ▲ Samples：The dataset consists of 4 tables include drug, event, extraction, event_numer

    Data Download (4.87MB)

last update: 2020-6-1

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