scholarly journals Biological features between miRNAs and their targets are unveiled from deep learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tongjun Gu ◽  
Mingyi Xie ◽  
W. Brad Barbazuk ◽  
Ji-Hyun Lee
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Deep Learning ◽  
Seed Region ◽  
Learning Models ◽  
Cellular Processes ◽  
Biological Features ◽  
Mirna Targets ◽  
Black Boxes ◽  
Better Than

AbstractMicroRNAs (miRNAs) are ~ 22 nucleotide ubiquitous gene regulators. They modulate a broad range of essential cellular processes linked to human health and diseases. Consequently, identifying miRNA targets and understanding how they function are critical for treating miRNA associated diseases. In our earlier work, a hybrid deep learning-based approach (miTAR) was developed for predicting miRNA targets. It performs substantially better than the existing methods. The approach integrates two major types of deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the features in miRNA:target interactions learned by miTAR have not been investigated. In the current study, we demonstrated that miTAR captures known features, including the involvement of seed region and the free energy, as well as multiple novel features, in the miRNA:target interactions. Interestingly, the CNN and RNN layers of the model perform differently at capturing the free energy feature: the units in RNN layer is more unique at capturing the feature but collectively the CNN layer is more efficient at capturing the feature. Although deep learning models are commonly thought “black-boxes”, our discoveries support that the biological features in miRNA:target can be unveiled from deep learning models, which will be beneficial to the understanding of the mechanisms in miRNA:target interactions.

scholarly journals Biological features between miRNA and their targets are unveiled from deep learning models

2021 ◽  
Author(s):  
Tongjun Gu ◽  
Mingyi Xie ◽  
W. Brad Barbazuk ◽  
Ji-Hyun Lee
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Deep Learning ◽  
Seed Region ◽  
Learning Models ◽  
Feature Maps ◽  
Cellular Processes ◽  
Biological Features ◽  
Mirna Targets ◽  
Black Boxes

Abstract MicroRNAs (miRNAs) are ~22 nucleotide ubiquitous gene regulators. They modulate a broad range of essential cellular processes linked to human health and diseases. Consequently, identifying miRNA targets and understanding how they function are critical for treating miRNA associated diseases. In our earlier work, we developed a hybrid deep learning-based approach (miTAR) for predicting miRNA targets at significantly higher accuracy compared to existing methods. It integrates two major types of deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the features in miRNA:target interactions learned by miTAR have not been investigated. In the current study, we demonstrated that miTAR captures known features, including the involvement of seed region and the free energy, as well as multiple novel features, in the miRNA:target interactions. Interestingly, the CNN and RNN layers of the model behave differently at capturing the free energy feature: the feature captured by the CNN layer units, but not the RNN layer units, is overlapped within and across feature maps. Although deep learning models are commonly thought “black-boxes”, our discoveries support that the biological features in miRNA:target can be unveiled from deep learning models, which will be beneficial to the understanding of the mechanisms in miRNA:target interactions.

scholarly journals Application of Deep Learning Methods to Forecasting Changes in Short-Term Currency Trends

2020 ◽  
Vol 7 (2) ◽  
pp. 75-86
Author(s):  
Vasily D. Derbentsev ◽  
Vitalii S. Bezkorovainyi ◽  
Iryna V. Luniak
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Better Than

This study investigates the issues of forecasting changes in short-term currency trends using deep learning models, which is relevant for both the scientific community and for traders and investors. The purpose of this study is to build a model for forecasting the direction of change in the prices of currency quotes based on deep neural networks. The developed architecture was based on the model of valve recurrent node, which is a modification of the model of “Long Short-Term Memory”, but is simpler in terms of the number of parameters and learning time. The forecast calculations of the dynamics of quotations of the currency pair euro/dollar and the most capitalised cryptocurrency Bitcoin/dollar were performed using daily, four-hour and hourly datasets. The obtained results of binary classification (forecast of the direction of trend change) when applying daily and hourly quotations turned out to be generally better than those of time series models or models of neural networks of other architecture (in particular, multilayer perceptron or “Long Short-Term Memory” models). According to the study results, the highest accuracy of classification was for the model of daily quotations for both euro/dollar – about 72%, and for Bitcoin/ dollar – about 69%. For four-hour and hourly time series, the accuracy of classification decreased, which can be explained both by the increase in the impact of “market noise” and the probable overfitting. Computer simulation has demonstrated that models predict a rising trend better than a declining one. The study confirmed the prospects for the application of deep learning models for short-term forecasting of time series of currency quotes. The use of the developed models proved to be effective for both fiat and cryptocurrencies. The proposed system of models based on deep neural networks can be used as a basis for developing an automated trading system in the foreign exchange market

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures

2022 ◽  
pp. 1-27
Author(s):  
Clifford Bohm ◽  
Douglas Kirkpatrick ◽  
Arend Hintze
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mental Representations ◽  
Network Architectures ◽  
Information Theoretic ◽  
Time Points ◽  
The Past ◽  
Black Boxes ◽  
Past Experiences ◽  
Computational Systems

Abstract Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

Train Deep Learning Models using subsurface geological images datasets

2021 ◽  
Author(s):  
Ramy Abdallah ◽  
Clare E. Bond ◽  
Robert W.H. Butler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Models ◽  
Structural Interpretation ◽  
Performance Accuracy ◽  
Learning Techniques ◽  
Machine Learning Models

&lt;p&gt;Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&amp;#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.&lt;/p&gt;

A Review of Recent Deep Learning Models in COVID-19 Diagnosis

2021 ◽  
Vol 6 (5) ◽  
pp. 10-15
Author(s):  
Ela Bhattacharya ◽  
D. Bhattacharya
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Test Results ◽  
Learning Models ◽  
Future Directions ◽  
Human Contact ◽  
The World ◽  
Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

scholarly journals An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

2020 ◽  
Vol 14 ◽  
Author(s):  
Yaqing Zhang ◽  
Jinling Chen ◽  
Jen Hong Tan ◽  
Yuxuan Chen ◽  
Yunyi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Real Life ◽  
Learning Rate ◽  
Learning Models ◽  
Learning Methods ◽  
Time Frequency ◽  
Comparison Results

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

Sign in / Sign up

Export Citation Format

Share Document