scholarly journals Synthetic Promoter Design in Escherichia coli based on Generative Adversarial Network

2019 ◽  
Author(s):  
Ye Wang ◽  
Haochen Wang ◽  
Liyang Liu ◽  
Xiaowo Wang
Keyword(s):  
Escherichia Coli ◽  
Sequence Space ◽  
Promoter Activity ◽  
Promoter Sequence ◽  
Similarity Score ◽  
Generative Models ◽  
Fine Tuning ◽  
Generative Adversarial Network ◽  
Promoter Sequences ◽  
Adversarial Network

ABSTRACTSynthetic promoters are commonly applied elements in circuit design for fine-tuning the protein expression levels. Promoter engineering was mostly focused on the random mutation or combination of regulation elements such as transcription factor binding sites. However, the size of promoter sequence space is still overwhelming and better navigation method is required. On the other hand, the generative adversarial network (GAN) is known for its great ability to reduce the searching space by learning to generate new data on the similar manifold of original data. Here, we applied WGAN-GP model into de novo promoter sequence design to generate entirely new promoter sequences. In total, 83 of model-generated promoter sequences were tested in promoter activity screening by regulating the expression of sfGFP gene in Escherichia coli. As a result, 26 out of 83 newly designed promoters were found functional and successfully expressed with varying activities, with similarity score to natural promoters all less than 0.7. Moreover, 3 of them showed higher promoter strength than the wild type promoters and their highly expression mutants. The much higher successful rate and promoter activity with much lower similarity score in our model-designed novel promoters confirmed the effectiveness of promoter sequence learning. Our work provides insights into an area of navigation of novel functional promoter sequence space automatically, as well as speeding up evolution process of naturally existing promoters, indicating the potential ability for deep generative models to be applied into genetic element designing in the future.

scholarly journals WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Amey Thakur ◽  
Hasan Rizvi ◽  
Mega Satish
Keyword(s):  
Smooth Surface ◽  
Generative Models ◽  
Surface Representation ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Structure Representation ◽  
Texture Representation ◽  
Adversarial Network ◽  
Representation Structure ◽  
New Framework

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

Sound Field Reconstruction in Rooms with Deep Generative Models

2021 ◽  
Vol 263 (5) ◽  
pp. 1527-1538
Author(s):  
Xenofon Karakonstantis ◽  
Efren Fernandez Grande
Keyword(s):  
Plane Waves ◽  
Sound Field ◽  
Real Data ◽  
Generative Models ◽  
Random Wave ◽  
Generative Adversarial Network ◽  
Underlying Distribution ◽  
Adversarial Network ◽  
Reconstruction Methods ◽  
Free Region

The characterization of Room Impulse Responses (RIR) over an extended region in a room by means of measurements requires dense spatial with many microphones. This can often become intractable and time consuming in practice. Well established reconstruction methods such as plane wave regression show that the sound field in a room can be reconstructed from sparsely distributed measurements. However, these reconstructions usually rely on assuming physical sparsity (i.e. few waves compose the sound field) or trait in the measured sound field, making the models less generalizable and problem specific. In this paper we introduce a method to reconstruct a sound field in an enclosure with the use of a Generative Adversarial Network (GAN), which s new variants of the data distributions that it is trained upon. The goal of the proposed GAN model is to estimate the underlying distribution of plane waves in any source free region, and map these distributions from a stochastic, latent representation. A GAN is trained on a large number of synthesized sound fields represented by a random wave field and then tested on both simulated and real data sets, of lightly damped and reverberant rooms.

scholarly journals Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

Peptide nucleic acid restores colistin susceptibility through modulation of MCR-1 expression in Escherichia coli

2020 ◽  
Author(s):  
Xiaoming Wang ◽  
Yao Wang ◽  
Zhuoren Ling ◽  
Chaoyang Zhang ◽  
Mingming Fu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Peptide Nucleic Acid ◽  
Promoter Activity ◽  
Sequence Variation ◽  
Promoter Sequence ◽  
E Coli ◽  
Expression In Escherichia Coli ◽  
Expression Host ◽  
Antisense Approach ◽  
Increased Susceptibility

Abstract Background Plasmid-mediated mechanisms of drug resistance accelerate the spread of polymyxin resistance, leaving clinicians with few or no antibacterial options for the treatment of infections caused by MDR bacteria, especially carbapenemase-producing strains. Objectives To evaluate the associations among promoter sequence variation, mcr-1 expression, host factors and levels of colistin resistance and to propose antisense agents such as peptide nucleic acids (PNAs) targeting mcr-1 as a tool to restore colistin susceptibility through modulation of MCR-1 expression in Escherichia coli. Methods A β-galactosidase assay was performed to study mcr-1 promoter activity. Quantitative real-time PCR and western blot assays were used to identify the expression level of MCR-1 in WT strains and transformants. Three PNAs targeting different regions of mcr-1 were designed and synthesized to determine whether they can effectively inhibit MCR-1 expression. MIC was measured to test colistin susceptibility in the presence or absence of PNA-1 in mcr-1-carrying E. coli. Results Variation in the mcr-1 promoter sequence and host species affect promoter activity, MCR-1 expression levels and colistin MICs. One PNA targeting the ribosome-binding site fully inhibited the expression of mcr-1 at a concentration of 4 μM, resulting in significantly increased susceptibility to colistin. The MIC90 of colistin decreased from 8 to 2 mg/L (P < 0.05) in the presence of 4 μM PNA. Conclusions These findings suggest that the antisense approach is a possible strategy to combat mcr-1-mediated resistance as well as other causes of emerging global resistance.

scholarly journals Vehicle trajectory prediction and generation using LSTM models and GANs

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253868
Author(s):  
Luca Rossi ◽  
Andrea Ajmar ◽  
Marina Paolanti ◽  
Roberto Pierdicca
Keyword(s):  
Traffic Congestion ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Generative Models ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Displacement Error ◽  
Adversarial Network ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

scholarly journals Creativity in Generative Musical Networks: Evidence From Two Case Studies

2021 ◽  
Vol 8 ◽  
Author(s):  
Rodrigo F. Cádiz ◽  
Agustín Macaya ◽  
Manuel Cartagena ◽  
Denis Parra
Keyword(s):  
Case Studies ◽  
Generative Models ◽  
Training Data ◽  
Current Understanding ◽  
Generative Adversarial Network ◽  
Training Time ◽  
Adversarial Network ◽  
The Arts ◽  
Musical Excerpts ◽  
Musical Chords

Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.

scholarly journals SubSpace Capsule Network

2020 ◽  
Vol 34 (07) ◽  
pp. 10745-10753
Author(s):  
Marzieh Edraki ◽  
Nazanin Rahnavard ◽  
Mubarak Shah
Keyword(s):  
Image Classification ◽  
Lower Layer ◽  
Spatial Relation ◽  
Generative Models ◽  
Test Time ◽  
Generative Adversarial Network ◽  
Major Drawback ◽  
Computational Overhead ◽  
Adversarial Network ◽  
Supervised Image Classification

Convolutional neural networks (CNNs) have become a key asset to most of fields in AI. Despite their successful performance, CNNs suffer from a major drawback. They fail to capture the hierarchy of spatial relation among different parts of an entity. As a remedy to this problem, the idea of capsules was proposed by Hinton. In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appearance or implicitly-defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules. A capsule is created by projecting an input feature vector from a lower layer onto the capsule subspace using a learnable transformation. This transformation finds the degree of alignment of the input with the properties modeled by the capsule subspace.We show that SCN is a general capsule network that can successfully be applied to both discriminative and generative models without incurring computational overhead compared to CNN during test time. Effectiveness of SCN is evaluated through a comprehensive set of experiments on supervised image classification, semi-supervised image classification and high-resolution image generation tasks using the generative adversarial network (GAN) framework. SCN significantly improves the performance of the baseline models in all 3 tasks.

PaDGAN: Learning to Generate High-Quality Novel Designs

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Wei Chen ◽  
Faez Ahmed
Keyword(s):  
Loss Function ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Generative Models ◽  
Training Data ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Design Synthesis ◽  
Adversarial Network

Abstract Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: (1) generated designs lack diversity and do not cover all areas of the design space, (2) it is difficult to explicitly improve the overall performance or quality of generated designs, and (3) existing models generally do not generate novel designs, outside the domain of the training data. In this article, we simultaneously address these challenges by proposing a new determinantal point process-based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the generative adversarial network, named “performance augmented diverse generative adversarial network” (PaDGAN), which can generate novel high-quality designs with good coverage of the design space. By using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla generative adversarial network, on average, it generates samples with a 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

scholarly journals Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 319
Author(s):  
Wang Xi ◽  
Guillaume Devineau ◽  
Fabien Moutarde ◽  
Jie Yang
Keyword(s):  
Data Augmentation ◽  
Human Movement ◽  
Generative Models ◽  
Generative Model ◽  
Great Success ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Human Movements ◽  
Action Type ◽  
Skeleton Image

Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The objective of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format. We evaluate our approach on the 3D skeletal data provided in the large NTU_RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of the 60 action classes. We also quantitatively evaluate the performance of our model by computing Fréchet inception distances, which shows strong correlation to human judgement. To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

2018 ◽  
Author(s):  
Oscar Mendez-Lucio ◽  
Benoit Baillif ◽  
Djork-Arné Clevert ◽  
David Rouquié ◽  
Joerg Wichard
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
De Novo ◽  
Molecular Design ◽  
Gene Expression Signature ◽  
Cellular Level ◽  
Generative Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

Sign in / Sign up

Export Citation Format

Share Document