MOTIVATION: Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is n sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred. RESULTS: We have developed improved Generative Adversarial Networks-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images. AVAILABILITY AND IMPLEMENTATION: http://murphylab.cbd.cmu.edu/Software/2022_insilico. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the rapid development of 5G commercialization on a large scale and edge computing, cloud-edge collaboration technology has been widely used in various industries, and how to achieve high efficiency cloud-edge network environment has become a research hotspot. In this paper, we propose a network efficiency evaluation model based on analytic hierarchy process (AHP) and logistic regression (LR) algorithm in cloud-edge collaborative environment. Using AHP to calculate the weight matrix of indicators and formulating multiple discrete parameter measures into the same dimensional area to obtain the final comprehensive efficiency value of the network. It describes the efficiency value in cloud-edge collaborative environment as a qualitative concept and realizes the qualitative evaluation in cloud-edge collaborative environment. On this basis, comparative experiments are carried out to evaluate the comprehensive efficiency of the network by using the traditional method and the method proposed in this paper, which verifies the practicability and effectiveness of the efficiency evaluation method.

Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. Meanwhile, vConv could be readily integrated into multi-layer neural networks as an 'in-place replacement' of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.