In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces the discriminativeness of the learned feature representation for facial ages. Motivated by the fact that age labels are usually correlated in real-world scenarios, we carefully develop a similarity-aware function to well measure the distance of each face pair based on the age value gaps. Consequently, the age-difference information is exploited in the synthetic feature space for robust age estimation. During the learning process, we jointly optimize both procedures of generating hard negatives and learning discriminative age ranker via a sequence of adversarial-game iterations. Another major issue lies on that existing methods only enforce the indiscriminativeness within each class, which is probably trapped into model overfitting and thus the generation capacity is limited particularly on unseen age classes with many individuals. To circumvent this problem, we propose a variational deep adversarial learning (VDAL) paradigm, which learns to encode each face sample in two factorized parts, i.e., the intra-class variance distribution and the intra-class invariant class center. Moreover, our VDAL principally optimizes the variational confidence lower bound on the variational factorized feature representation. To better enhance the discriminativeness of the age representation, our VDAL further learns to encode the ordinal relationship among age labels in the reconstructed subspace. Experimental results on folds of widely-evaluated benchmarking datasets demonstrate that our approach achieves promising performance in contrast to most state-of-the-art age estimation methods.

Accurate protein contact map prediction is essential for de novo protein structure prediction. Over the past few years, deep learning has brought a significant breakthrough in protein contact map prediction and optimized deep learning architectures are highly desired for performance improvement. As an emerging deep learning architecture, the generative adversarial network (GAN) has shown the powerful capability of learning intrinsic patterns, which inspires us to comprehensively exploit GAN for predicting accurate protein contact maps. In this study, we present GANcon, a novel GAN-based deep learning architecture for protein contact map prediction, which to the best of our knowledge is the first GAN-based approach in this field. Instead of using a single neural network, GANcon is composed of two competitive networks that are evolving through adversarial learning. The generator network employs a dedicated encoder-decoder architecture that can efficiently capture the underlying contact information from versatile protein features to generate contact maps, while the discriminator network learns the differences between generated contact maps and real ones and promotes the generator network to produce more accurate contact maps. Moreover, to deal with the imbalance problem and take into account the symmetry of contact maps, we also propose a novel symmetrical focal loss, which can further enhance the effectiveness of adversarial learning for better performance. The experimental results on several datasets demonstrate that GANcon outperforms many state-of-the-art methods, indicating the effectiveness of our method for predicting protein contact maps. GANcon is freely available at https://github.com/melissaya/GANcon.

Genetic intra-tumor heterogeneity (ITH) characterizes the differences in genomic variations between tumor clones, and accurately unmasking ITH is important for personalized cancer therapy. Single-cell DNA sequencing now emerges as a powerful means for deciphering underlying ITH based on point mutations of single cells. However, detecting tumor clones from single-cell mutation data remains challenging due to the error-prone and discrete nature of the data. We introduce bmVAE, a bioinformatics tool for learning low-dimensional latent representation of single cell based on a variational autoencoder and then clustering cells into subpopulations in the latent space. bmVAE takes single-cell binary mutation data as inputs, and outputs inferred cell subpopulations as well as their genotypes. To achieve this, the bmVAE framework is designed to consist of three modules including dimensionality reduction, cell clustering and genotype estimation. We assess the method on various synthetic datasets where different factors including false negative rate, data size and data heterogeneity are considered in simulation, and further demonstrate its effectiveness on two real datasets. The results suggest bmVAE is highly effective in reasoning ITH, and performs competitive to existing methods. bmVAE is freely available at https://github.com/zhyu-lab/bmvae. Supplementary data are available at Bioinformatics online.

The fractal patterns are recursive patterns and are self-similar in nature. The fractal geometry provides better understanding of natural patterns as compared to the Euclidean geometry. The fractal designs have been used extensively in the fields of applied sciences due to the systematic methods used for their generation. These methods provide benchmarks to analyze the roughness, narrow/broad vision of the objects. In the fields of architecture and design, computational methods for fractal generation can prove to be more reliable tools. The fractal patterns can be simulated and the architecture can be modeled, with several options, before implementing it practically. During this research, this strategy is opted to design novel fractal tile designs. Several designs are selected from a series of simulations, based on the final visionary evaluation, according to the requirement of the walls of different zones in modern buildings. Four fractal patterns are simulated with several orientations and final designs are documented with corresponding geometrical evaluation.

Background: A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required. Results: Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools. Conclusions: SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.

Allele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency. SCSsim is freely available at https://github.com/qasimyu/scssim. Supplementary data are available at Bioinformatics online.