PandoraGAN: Generating antiviral peptides using Generative Adversarial Network

AbstractMotivationThe continuous increase in pathogenic viruses and the intensive laboratory research for development of novel antiviral therapies often poses challenge in terms of cost and time efficient drug design. This accelerates research for alternate drug candidates and contributes to recent rise in research of antiviral peptides against many of the viruses. With limited information regarding these peptides and their activity, modifying the existing peptide backbone or developing a novel peptide is very time consuming and a tedious process. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful for wet lab scientist to screen potential antiviral candidates of interest and expedite the initial stage of peptide drug development. To our knowledge this is the first ever use of GAN models for antiviral peptides across the viral spectrum.ResultsIn this study, we develop PandoraGAN that utilizes GAN to design bio active antiviral peptides. Available antiviral peptide data was manually curated for preparing highly active peptides data set to include peptides with lower IC50 values. We further validated the generated sequences comparing the physico-chemical properties of generated antiviral peptides with manually curated highly active training data.AvailabilityAntiviral sequences generated by PandoraGAN are available on PandoraGAN server. https://pandora-gan.herokuapp.com/. The code is available at https://gitlab.com/shraddha.surana/antiviral-peptide-predictions-using-ganContactparora@thoughtworks.com

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Adaptation and Selection Techniques Based on Deep Learning for Human Activity Recognition Using Inertial Sensors

Engineering Proceedings ◽

10.3390/ecsa-7-08159 ◽

2020 ◽

Vol 2 (1) ◽

pp. 22

Author(s):

Manuel Gil-Martín ◽

José Antúnez-Durango ◽

Rubén San-Segundo

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Inertial Sensors ◽

Human Activity Recognition ◽

Training Data ◽

Generative Adversarial Networks ◽

Data Set ◽

Physical Activity Monitoring ◽

Training Data Selection

Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the Physical Activity Monitoring Data Set (PAMAP2) dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs, or ironing. The evaluation has been performed using a Leave-One-Subject-Out (LOSO) cross-validation: all recordings from a subject are used as testing subset and recordings from the rest of the subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to testing data improve some users’ performance. Moreover, training data selection algorithms with autoencoders provide significant improvements. The GAN approach, using the generator or discriminator module, also provides improvement in selection experiments.

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3D Design Using Generative Adversarial Networks and Physics-Based Validation

Journal of Mechanical Design ◽

10.1115/1.4045419 ◽

2019 ◽

Vol 142 (7) ◽

Cited By ~ 2

Author(s):

Dule Shu ◽

James Cunningham ◽

Gary Stump ◽

Simon W. Miller ◽

Michael A. Yukish ◽

...

Keyword(s):

Evaluation Process ◽

3D Models ◽

Training Data ◽

Generative Adversarial Networks ◽

3D Design ◽

Generative Adversarial Network ◽

Data Set ◽

High Performing ◽

Adversarial Network

Abstract The authors present a generative adversarial network (GAN) model that demonstrates how to generate 3D models in their native format so that they can be either evaluated using complex simulation environments or realized using methods such as additive manufacturing. Once initially trained, the GAN can create additional training data itself by generating new designs, evaluating them in a physics-based virtual environment, and adding the high performing ones to the training set. A case study involving a GAN model that is initially trained on 4045 3D aircraft models is used for demonstration, where a training data set that has been updated with GAN-generated and evaluated designs results in enhanced model generation, in both the geometric feasibility and performance of the designs. Z-tests on the performance scores of the generated aircraft models indicate a statistically significant improvement in the functionality of the generated models after three iterations of the training-evaluation process. In the case study, a number of techniques are explored to structure the generate-evaluate process in order to balance the need to generate feasible designs with the need for innovative designs.

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Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Diagnostic Pathology ◽

10.1186/s13000-021-01126-y ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Marlen Runz ◽

Daniel Rusche ◽

Stefan Schmidt ◽

Martin R. Weihrauch ◽

Jürgen Hesser ◽

...

Keyword(s):

Image Acquisition ◽

Similarity Measures ◽

Staining Intensity ◽

Generative Adversarial Networks ◽

Target Domain ◽

Inverse Mapping ◽

Generative Adversarial Network ◽

Data Set ◽

Link Type ◽

Histological Images

Abstract Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. Methods In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB:XA→XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (GA,DA), for the inverse mapping GA:XB→XA. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (GA(GB(XA))≈XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. Results Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. Conclusions CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The data set supporting the solutions is available at 10.11588/data/8LKEZF.

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Ground-roll attenuation using generative adversarial networks

Geophysics ◽

10.1190/geo2019-0414.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA255-WA267 ◽

Cited By ~ 1

Author(s):

Yijun Yuan ◽

Xu Si ◽

Yue Zheng

Keyword(s):

Seismic Data ◽

Signal To Noise Ratio ◽

Training Data ◽

Generative Adversarial Networks ◽

Coherent Noise ◽

Traditional Methods ◽

Generative Adversarial Network ◽

Data Set ◽

Persistent Problem ◽

Ground Roll

Ground roll is a persistent problem in land seismic data. This type of coherent noise often contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data. A variety of methods for addressing ground-roll attenuation have been developed. However, existing methods are limited, especially when using real land seismic data. For example, when ground roll and reflections overlap in the time or frequency domains, traditional methods cannot completely separate them and they often distort the signals during the suppression process. We have developed a generative adversarial network (GAN) to attenuate ground roll in seismic data. Unlike traditional methods for ground-roll attenuation dependent on various filters, the GAN method is based on a large training data set that includes pairs of data with and without ground roll. After training the neural network with the training data, the network can identify and filter out any noise in the data. To fulfill this purpose, the proposed method uses a generator and a discriminator. Through network training, the generator learns to create the data that can fool the discriminator, and the discriminator can then distinguish between the data produced by the generator and the training data. As a result of the competition between generators and discriminators, generators produce better images whereas discriminators accurately recognize targets. Tests on synthetic and real land seismic data show that the proposed method effectively reveals reﬂections masked by the ground roll and obtains better results in the attenuation of ground roll and in the preservation of signals compared to the three other methods.

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Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

Remote Sensing ◽

10.3390/rs12244162 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4162

Author(s):

Anna Hu ◽

Zhong Xie ◽

Yongyang Xu ◽

Mingyu Xie ◽

Liang Wu ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Training Data ◽

Generative Adversarial Networks ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Data Set ◽

Adversarial Networks ◽

Ground Object ◽

Edge Sharpening

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.

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Generating Video from Images using GANs

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j7560.0891020 ◽

2020 ◽

Vol 9 (10) ◽

pp. 377-380

Keyword(s):

Random Noise ◽

Generative Models ◽

Training Data ◽

Generative Adversarial Networks ◽

External Information ◽

Data Set ◽

Adversarial Networks ◽

Wide Range ◽

Realistic Images ◽

New Framework

Generative adversarial networks are a category of neural networks used extensively for the generation of a wide range of content. The generative models are trained through an adversarial process that offers a lot of potential in the world of deep learning. GANs are a popular approach to generate new data from random noise vector that are similar or have the same distribution as that in the training data set. The Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images. An extension of GANs is the conditional GANs which allows the model to condition external information. Conditional GANs have seen increasing uses and more implications than ever. We also propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models, a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Our work aims at highlighting the uses of conditional GANs specifically with Generating images. We present some of the use cases of conditional GANs with images specifically in video generation.

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Real-time super-resolution mapping of locally anisotropic grain orientations for ultrasonic non-destructive evaluation of crystalline material

Neural Computing and Applications ◽

10.1007/s00521-021-06670-8 ◽

2021 ◽

Author(s):

Jonathan Singh ◽

Katherine Tant ◽

Andrew Curtis ◽

Anthony Mulholland

Keyword(s):

Real Time ◽

Computational Cost ◽

Super Resolution ◽

Structural Similarity ◽

Image Resolution ◽

Training Data ◽

Generative Adversarial Networks ◽

Desktop Computer ◽

Data Set ◽

Grain Orientations

AbstractEstimating the spatially varying microstructures of heterogeneous and locally anisotropic media non-destructively is necessary for the accurate detection of flaws and reliable monitoring of manufacturing processes. Conventional algorithms used for solving this inverse problem come with significant computational cost, particularly in the case of high-dimensional, nonlinear tomographic problems, and are thus not suitable for near-real-time applications. In this paper, for the first time, we propose a framework which uses deep neural networks (DNNs) with full aperture, pitch-catch and pulse-echo transducer configurations, to reconstruct material maps of crystallographic orientation. We also present the first application of generative adversarial networks (GANs) to achieve super-resolution of ultrasonic tomographic images, providing a factor-four increase in image resolution and up to a 50% increase in structural similarity. The importance of including appropriate prior knowledge in the GAN training data set to increase inversion accuracy is demonstrated: known information about the material’s structure should be represented in the training data. We show that after a computationally expensive training process, the DNNs and GANs can be used in less than 1 second (0.9 s on a standard desktop computer) to provide a high-resolution map of the material’s grain orientations, addressing the challenge of significant computational cost faced by conventional tomography algorithms.

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Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

Recent Patents on Computer Science ◽

10.2174/2213275911666181106160002 ◽

2019 ◽

Vol 12 (2) ◽

pp. 120-127 ◽

Cited By ~ 5

Author(s):

Wael Farag

Keyword(s):

Gradient Descent ◽

Autonomous Driving ◽

Driving Behavior ◽

Training Data ◽

Stochastic Gradient Descent ◽

Data Set ◽

Safe Driving ◽

Processing Pipeline ◽

Self Driving Cars ◽

And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Applied Sciences ◽

10.3390/app9061128 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1128 ◽

Cited By ~ 12

Author(s):

Yundong Li ◽

Wei Hu ◽

Han Dong ◽

Xueyan Zhang

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Hurricane Sandy ◽

Training Data ◽

Aerial Images ◽

Detection Methods ◽

Single Shot ◽

Data Set ◽

Augmentation Strategies ◽

Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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