The architecture of the brain is too complex to be intuitively surveyable
without the use of compressed representations that project its variation into a
compact, navigable space. The task is especially challenging with
high-dimensional data, such as gene expression, where the joint complexity of
anatomical and transcriptional patterns demands maximum compression.
Established practice is to use standard principal component analysis (PCA),
whose computational felicity is offset by limited expressivity, especially at
great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas
transcription data, here we systematically compare compressed representations
based on the most widely supported linear and non-linear methods-PCA, kernel
PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding
(t-SNE), uniform manifold approximation and projection (UMAP), and deep
auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and
predictive utility with respect to signalling, microstructural, and metabolic
targets. We show that deep auto-encoders yield superior representations across
all metrics of performance and target domains, supporting their use as the
reference standard for representing transcription patterns in the human brain.

脑的体系结构过于复杂，需要使用压缩表示来将其变化投影到一个紧凑且可导航的空间中。通过比较基于线性和非线性方法的压缩表示，我们发现深度自编码器在性能和目标领域的各个指标上都表现出卓越的性能，从而支持将其用作代表人脑转录模式的参考标准。

脑基因转录的压缩表示

Compressed representation of brain genetic transcription

One of the most important properties of deep auto-encoders (DAEs) is their
capability to extract high level features from row data. Hence, especially
recently, the autoencoders are preferred to be used in various classification
problems such as image and voice recognition, computer security, medical data
analysis, etc. Despite, its popularity and high performance, the training phase
of autoencoders is still a challenging task, involving to select best
parameters that let the model to approach optimal results. Different training
approaches are applied to train sparse autoencoders. Previous studies and
preliminary experiments reveal that those approaches may present remarkable
results in same problems but also disappointing results can be obtained in
other complex problems. Metaheuristic algorithms have emerged over the last two
decades and are becoming an essential part of contemporary optimization
techniques. Gray wolf optimization (GWO) is one of the current of those
algorithms and is applied to train sparse auto-encoders for this study. This
model is validated by employing several popular Gene expression databases.
Results are compared with previous state-of-the art methods studied with the
same data sets and also are compared with other popular metaheuristic
algorithms, namely, Genetic Algorithms (GA), Particle Swarm Optimization (PSO)
and Artificial Bee Colony (ABC). Results reveal that the performance of the
trained model using GWO outperforms on both conventional models and models
trained with most popular metaheuristic algorithms.

本文通过灰狼优化算法来训练深度自动编码器，提高在分类问题中表现的效果和性能。与其他元启发式算法相比，结果表明使用 GWO 方法能够明显提高模型表现和性能。