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DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks
BackgroundIn recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to...
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Association filtering and generative adversarial networks for predicting lncRNA-associated disease
BackgroundLong non-coding RNA (lncRNA) closely associates with numerous biological processes, and with many diseases. Therefore, lncRNA-disease...
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Electronic medical records imputation by temporal Generative Adversarial Network
The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research...
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Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural networks
The main challenge in deep learning related to the identification of grape leaf diseases is how to achieve good performance in the case of available...
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CropPainter: an effective and precise tool for trait-to-image crop visualization based on generative adversarial networks
BackgroundVirtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and...
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Red-green-blue to normalized difference vegetation index translation: a robust and inexpensive approach for vegetation monitoring using machine vision and generative adversarial networks
High-resolution multispectral imaging of agricultural fields is expensive but helpful in detecting subtle variations in plant health and stress...
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Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks
Brain cancer ranks tenth on the list of leading causes of death in both men and women. Biopsy is one of the most used methods for diagnosing cancer....
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Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks
BackgroundImage-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time,...
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Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data
BackgroundIt remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as...
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Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level
Goal-directed actions frequently require a balance between antagonistic processes (e.g., executing and inhibiting a response), often showing an...
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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional...
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Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model
BackgroundThe investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the...
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Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks
BackgroundProtein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development...
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AI-PotatoGuard: Leveraging Generative Models for Early Detection of Potato Diseases
This paper introduces AI-PotatoGuard, an artificial intelligence (AI) tool which enhances the management of diseases in potatoes through the use of...
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Computational scoring and experimental evaluation of enzymes generated by neural networks
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins...
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Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations
MotivationComputer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable...
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BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network
BackgroundAn increasing number of studies have shown that lncRNAs are crucial for the control of hormones and the regulation of various physiological...
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Aspect-based sentiment analysis for fish diseases using a feature interaction model based on adversarial strategy
Aspect-based sentiment analysis has achieved many results in recent years, but most of the research focuses on goods, services, and topics. The...
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Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular...
