DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites

Abstract DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.

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BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies

10.1101/336719 ◽

2018 ◽

Author(s):

Andre Lamurias ◽

Luka A. Clarke ◽

Francisco M. Couto

Keyword(s):

Deep Learning ◽

Text Mining ◽

Drug Interactions ◽

Short Term Memory ◽

Biomedical Ontologies ◽

Short Term ◽

Term Memory ◽

Domain Specific ◽

Learning Techniques ◽

Long Short Term Memory

AbstractRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.We propose a new model, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using an open biomedical ontology, which in our case-study was Chemical Entities of Biological Interest (ChEBI). We assessed the performance of BO-LSTM on detecting and classifying drug-drug interactions in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved both the F1-score of the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.Author summaryA high quantity of biomedical information is only available in documents such as scientific articles and patents. Due to the rate at which new documents are produced, we need automatic methods to extract useful information from them. Text mining is a subfield of information retrieval which aims at extracting relevant information from text. Scientific literature is a challenge to text mining because of the complexity and specificity of the topics approached. In recent years, deep learning has obtained promising results in various text mining tasks by exploring large datasets. On the other hand, ontologies provide a detailed and sound representation of a domain and have been developed to diverse biomedical domains. We propose a model that combines deep learning algorithms with biomedical ontologies to identify relations between concepts in text. We demonstrate the potential of this model to extract drug-drug interactions from abstracts and drug descriptions. This model can be applied to other biomedical domains using an annotated corpus of documents and an ontology related to that domain to train a new classifier.

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Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4321131 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Kazi Nabiul Alam ◽

Md Shakib Khan ◽

Abdur Rab Dhruba ◽

Mohammad Monirujjaman Khan ◽

Jehad F. Al-Amri ◽

...

Keyword(s):

Deep Learning ◽

Language Processing ◽

Performance Metrics ◽

Short Term Memory ◽

Confusion Matrix ◽

Short Term ◽

Learning Techniques ◽

The World ◽

Long Short Term Memory ◽

Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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Predictive Analysis of Cryptocurrency Price Using Deep Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17889 ◽

2018 ◽

Vol 7 (3.27) ◽

pp. 258 ◽

Cited By ~ 4

Author(s):

Yecheng Yao ◽

Jungho Yi ◽

Shengjun Zhai ◽

Yuwen Lin ◽

Taekseung Kim ◽

...

Keyword(s):

Deep Learning ◽

International Relations ◽

Short Term Memory ◽

Training Data ◽

Short Term ◽

Effective Learning ◽

Learning Techniques ◽

Benchmark Datasets ◽

Novel Method ◽

Long Short Term Memory

The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

DYNA INGENIERIA E INDUSTRIA ◽

10.6036/10007 ◽

2021 ◽

Vol 96 (5) ◽

pp. 528-533

Author(s):

XAVIER LARRIVA NOVO ◽

MARIO VEGA BARBAS ◽

VICTOR VILLAGRA ◽

JULIO BERROCAL

Keyword(s):

Machine Learning ◽

Deep Learning ◽

High Performance ◽

New Technologies ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

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Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v5i4.6760 ◽

2021 ◽

Vol 5 (4) ◽

pp. 544

Author(s):

Antonius Angga Kurniawan ◽

Metty Mustikasari

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Data Input ◽

Fake News ◽

Accuracy Test ◽

Learning Techniques ◽

Data Word ◽

Wrong Prediction ◽

Long Short Term Memory ◽

Better Than

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

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Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES ◽

10.46912/napas.227 ◽

2021 ◽

Vol 4 (1) ◽

pp. 121-128

Author(s):

A Iorliam ◽

S Agber ◽

MP Dzungwe ◽

DK Kwaghtyo ◽

S Bum

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Hate Speech ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Learning Techniques ◽

Or Groups ◽

Long Short Term Memory

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.

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HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

Journal of Military Science and Technology ◽

10.54939/1859-1043.j.mst.72a.2021.1-12 ◽

2021 ◽

pp. 1-12

Author(s):

Thang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

The Neural Network ◽

Learning Techniques ◽

Long Short Term Memory ◽

Deep Learning Neural Network

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

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Classification of Chromosomal DNA Sequence Using A Hybrid Deep Learning Architecture

Current Bioinformatics ◽

10.2174/1574893615666200224095531 ◽

2020 ◽

Vol 15 ◽

Author(s):

Zhihua Du ◽

Xiangdong Xiao ◽

Vladimir N. Uversky

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Dna Sequence ◽

Convolutional Neural Networks ◽

Dna Sequences ◽

Short Term Memory ◽

Chromosomal Dna ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

: Chromosomal DNA contains most of the genetic information of eukaryotes and plays an important role in the growth, development and reproduction of living organisms. Most chromosomal DNA sequences are known to wrap around histones, and distinguishing these DNA sequences from ordinary DNA sequences is important for understanding the genetic code of life. The main difficulty behind this problem is the feature selection process. DNA sequences have no explicit features, and the common representation methods, such as one-hot coding, introduced the major drawback of high dimensionality. Recently, deep learning models have been proved to be able to automatically extract useful features from input patterns. In this paper, we present four different deep learning architectures using convolutional neural networks and long short-term memory networks for the purpose of chromosomal DNA sequence classification. Natural language model（Word2vec）was used to generate word embedding of sequence and learn features from it by deep learning. The comparison of these four architectures is carried out on 10 chromosomal DNA datasets. The results show that the architecture of convolutional neural networks combined with long short-term memory networks is superior to other methods in accuracy of chromosomal DNA prediction.

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Image Caption Generator Using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38652 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1554-1564

Author(s):

A. V. N. Kameswari

Keyword(s):

Deep Learning ◽

Visually Impaired ◽

Short Term Memory ◽

Image Understanding ◽

Research Area ◽

Short Term ◽

Visually Impaired People ◽

Learning Techniques ◽

Long Short Term Memory ◽

Image Caption

Abstract: When humans see an image, their brain can easily tell what the image is about, but a computer cannot do it easily. Computer vision researchers worked on this a lot and they considered it impossible until now! With the advancement in Deep learning techniques, availability of huge datasets and computer power, we can build models that can generate captions for an image. Image Caption Generator is a popular research area of Deep Learning that deals with image understanding and a language description for that image. Generating well-formed sentences requires both syntactic and semantic understanding of the language. Being able to describe the content of an image using accurately formed sentences is a very challenging task, but it could also have a great impact, by helping visually impaired people better understand the content of images. The biggest challenge is most definitely being able to create a description that must capture not only the objects contained in an image, but also express how these objects relate to each other. This paper uses Flickr_8K dataset and Flickr8k_text folder that contains Flickr8k.token which is the main file of our dataset that contains image name and their respective caption separated by newline(“\n”). CNN is used for extracting features from the image. We will use the pre-trained model Xception. LSTM will use the information from CNN to help generate a description of the image. In our Flickr8k_text folder, we have Flickr_8k.trainImages.txt file that contains a list of 6000 images names that we will use for training. After CNN-LSTM model is defined we give an image file as parameter through command prompt for testing image caption generator and it generates the caption of an image and its accuracy is observed by calculating bleu score for generated and reference captions. Keywords: Image Caption Generator, Convolutional Neural Network, Long Short-Term Memory, Bleu score, Flickr_8K

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