Lstm dna
201223361
DeepSite: bidirectional LSTM and CNN models for predicting DNA protein binding
Transcription factors are cis regulatory molecules that bind to specific sub regions of DNA promoters and initiate transcription, the process that regulates the conversion of genetic information from DNA to RNA. Several computational methods have been developed to predict DNA protein binding sites in DNA sequence using convolutional neural network (CNN) However, these techniques could indicate the dependency information of DNA sequence information in the framework of CNN. In addition, these methods are not accurate enough in prediction of the DNA protein binding sites from the DNA sequence. In this study, we employ the bidirectional long short term memory (BLSTM) and CNN to capture long term dependencies between the sequence motifs in DNA, which is called DeepSite. Apart from traditional CNN, which includes six layers: input layer, BLSTM layer, CNN layer, pooling layer, full connection layer and output layer, DeepSite approach can predict DNA protein binding sites with 87.12% sensitivity, 91.06% specificity, 89.19% accuracy and 0.783 MCC when tested on the 690 Chip seq experiments from ENCODE. Lastly, we conclude that our proposed method can also be applied to find DNA protein binding sites in different DNA sequences.
Int. J. Mach. Learn. Cybern.
Yongqing Zhang Shaojie Qiao Shengjie Ji Yizhou Li
54069161
Feature assisted stacked attentive shortest dependency path based Bi LSTM model for protein protein interaction
Abstract Knowledge about protein protein interactions is essential for understanding the biological processes such as metabolic pathways, DNA replication, and transcription etc. However, a majority of the existing Protein Protein Interaction (PPI) systems are dependent primarily on the scientific literature, which is not yet accessible as a structured database. Thus, efficient information extraction systems are required for identifying PPI information from the large collection of biomedical texts. In this paper, we present a novel method based on attentive deep recurrent neural network, which combines multiple levels of representations exploiting word sequences and dependency path related information to identify protein protein interaction (PPI) information from the text. We use the stacked attentive bi directional long short term memory (Bi LSTM) as our recurrent neural network to solve the PPI identification problem. This model leverages joint modeling of proteins and relations in a single unified framework, which is named as the 'Attentive Shortest Dependency Path LSTM' (Att sdpLSTM) model. Experimentation of the proposed technique was conducted on five popular benchmark PPI datasets, namely AiMed, BioInfer, HPRD50, IEPA, and LLL. The evaluation shows the F1 score values of 93 29 81 68 78 73 76 25 83 92 on AiMed, BioInfer, HPRD50, IEPA, and LLL dataset, respectively. Comparisons with the existing systems show that our proposed approach attains state of the art performance.
Knowl. Based Syst.
Shweta Yadav Asif Ekbal Sriparna Saha Ankith Jain Rakesh Kumar Pushpak Bhattacharyya
174810241
Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data
DNA base modifications, such as C5 methylcytosine (5mC) and N6 methyldeoxyadenosine (6mA) are important types of epigenetic regulations. Short read bisulfite sequencing and long read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes) For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species.DNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing data
Nature Communications
Qian Liu Li Fang Guoliang Yu Depeng Wang Chuan-Le Xiao Kai Wang
49656887
Feature Assisted bi directional LSTM Model for Protein Protein Interaction Identification from Biomedical Texts
Knowledge about protein protein interactions is essential in understanding the biological processes such as metabolic pathways, DNA replication, and transcription etc. However, a majority of the existing Protein Protein Interaction (PPI) systems are dependent primarily on the scientific literature, which is yet not accessible as a structured database. Thus, efficient information extraction systems are required for identifying PPI information from the large collection of biomedical texts. Most of the existing systems model the PPI extraction task as a classification problem and are tailored to the handcrafted feature set including domain dependent features. In this paper, we present a novel method based on deep bidirectional long short term memory (B LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text. This model leverages joint modeling of proteins and relations in a single unified framework, which we name as Shortest Dependency Path B LSTM (sdpLSTM) model. We perform experiments on two popular benchmark PPI datasets, namely AiMed BioInfer. The evaluation shows the F1 score values of 86.45% and 77.35% on AiMed and BioInfer, respectively. Comparisons with the existing systems show that our proposed approach attains state of the art performance.
ArXiv
Shweta Yadav Ankith Jain Rakesh Kumar Asif Ekbal Sriparna Saha Pushpak Bhattacharyya
208017415
DeepDRBP 2L: A New Genome Annotation Predictor for Identifying DNA Binding Proteins and RNA Binding Proteins Using Convolutional Neural Network and Long Short Term Memory
DNA binding proteins (DBPs) and RNA binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical research and real world application. Existing sequence based DBP predictors can accurately identify DBPs but incorrectly predict many RBPs as DBPs, and vice versa, resulting in low prediction precision. Moreover, some proteins (DRBPs) interacting with both DNA and RNA play important roles in gene expression and cannot be identified by existing computational methods. In this study, a two level predictor named DeepDRBP 2L was proposed by combining Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM) It is the first computational method that is able to identify DBPs, RBPs and DRBPs. Rigorous cross validations and independent tests showed that DeepDRBP 2L is able to overcome the shortcoming of the existing methods and can go one further step to identify DRBPs. Application of DeepDRBP 2L to tomato genome further demonstrated its performance. The webserver of DeepDRBP 2L is freely available at http:/bliulab.net/DeepDRBP 2L.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jun Zhang Qingcai Chen B Liu
211061119
D2VCB: A Hybrid Deep Neural Network for the Prediction of in vivo Protein DNA Binding from Combined DNA Sequence
Prediction of in vivo protein DNA binding is an important, but challenging task in the broad field of computational biology. Although some methods based on deep learning have succeed in modeling in vivo protein DNA binding, they often simply extract the sequence features from the original DNA sequence without consideration of other sequence features, such as their reverse, complementary and reverse complementary sequences. Also, one hot encoding of DNA sequence is vulnerable to the curse of dimensionality, which leads to unwanted equidistance of pairwise sequences. To address these problems, we propose D2VCB (dna2vec, convolution, bi LSTM) a novel hybrid deep neural network framework using dna2vec to predict in vivo protein DNA binding events. We extract input features from DNA original sequences, reverse sequences, complementary and complementary reverse sequences, and then use dna2vec to compute a distributed representation of k mer. In our D2VCB model, the convolution layer captures motif features, while the recurrent layer captures long term dependencies among motif features so as to improve prediction accuracy. Our performance comparison experiments show that D2VCB outperforms significantly other existing methods in terms of multiple performance metrics.
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Lei Deng Hui Wu Hanying Liu
219029791
Radio Frequency Signal Identification Using Transfer Learning Based on LSTM
Radio frequency distinct native attribute (RF DNA) technology is very important in distinguishing RF devices. This paper presents a new method for distinguishing RF devices by combining transfer learning and long short term memory (TL LSTM) The main purpose of this paper is to identify which RF device sent the unknown RF signals. The data were collected from almost the same eight RF devices produced in 2011, 2014 or 2016. These RF devices emitted unintended signals at 2.4G bandwidth with frequency shift keying. The proposed method first used late production RF devices in 2011 or 2014 as source domain and transferred the trained model to target domain produced in 2016 and then used neural network LSTM model to identify the RF signals. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi domain feature space. The results reveal that the proposed method TL LSTM can solve the problem of small sample training very well.
Circuits Syst. Signal Process.
Xueli Wang Yufeng Zhang Hongxin Zhang Yixuan Li Xiaofeng Wei
220500062
EvoLSTM: context dependent models of sequence evolution using a sequence to sequence LSTM
Abstract Motivation Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the core of many benchmarking strategies. Yet, mutational processes have complex context dependencies that remain poorly modeled and understood. Results We introduce EvoLSTM, a recurrent neural network based evolution simulator that captures mutational context dependencies. EvoLSTM uses a sequence to sequence long short term memory model trained to predict mutation probabilities at each position of a given sequence, taking into consideration the 14 flanking nucleotides. EvoLSTM can realistically simulate mammalian and plant DNA sequence evolution and reveals unexpectedly strong long range context dependencies in mutation probabilities. EvoLSTM brings modern machine learning approaches to bear on sequence evolution. It will serve as a useful tool to study and simulate complex mutational processes. Availability and implementation Code and dataset are available at https:/github.com/DongjoonLim/EvoLSTM. Supplementary information Supplementary data are available at Bioinformatics online.
Bioinform.
Dong-Hee Lim Mathieu Blanchette
51718081
EL_LSTM: Prediction of DNA Binding Residue from Protein Sequence by Combining Long Short Term Memory and Ensemble Learning
Most past works for DNA binding residue prediction did not consider the relationships between residues. In this paper, we propose a novel approach for DNA binding residue prediction, referred to as EL_LSTM, which includes two main components. The first component is the Long Short Term Memory (LSTM) which learns pairwise relationships between residues through a bi gram model and then learns feature vectors for all residues. The second component is an ensemble learning based classifier introduced to tackle the data imbalance problem in binding residue predictions. We use a variant of the bagging strategy in ensemble learning to achieve balanced samples. Evaluations on PDNA 224 and DBP 123 show that adding feature relationships performs better than classifiers without feature relationships by at least 0.028 on MCC, 1.18 percent on ST and 0.012 on AUC. This indicates the usefulness of feature relationships for DNA binding residue predictions. Evaluation on using ensemble learning indicates that the improvement can reach at least 0.021 on MCC, 1.32 percent on ST, and 0.018 on AUC compared to the use of a single LSTM classifier. Comparisons with the state of the art predictors show that our proposed EL_LSTM outperforms them significantly. Further feature analysis validates the effectiveness of LSTM for the prediction of DNA binding residues.
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jiyun Zhou Qin Lu Ruifeng Xu Lin Gui Hongpeng Wang
221076098
DNA6mA MINT: DNA 6mA Modification Identification Neural Tool
DNA N6 methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou's 5 steps rule. The Neural Network (NN) model uses convolution layers to extract the high level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state of the art techniques. The proposed model is evaluated on Mus musculus, Rice, and "Combined species" genomes with 5 and 10 fold cross validation. Further, with access to a user friendly web server, publicly available can be accessed freely.
Genes
Mobeen Ur Rehman Kil to Chong