Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation (e.g., Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset (e.g., training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches.

UNASSIGNED: Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI).
UNASSIGNED: We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics.
UNASSIGNED: Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics.
UNASSIGNED: Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.

Latent variables analysis is an important part of psychometric research. In this context, factor analysis and other related techniques have been widely applied for the investigation of the internal structure of psychometric tests. However, these methods perform a linear dimensionality reduction under a series of assumptions that could not always be verified in psychological data. Predictive techniques, such as artificial neural networks, could complement and improve the exploration of latent space, overcoming the limits of traditional methods. In this study, we explore the latent space generated by a particular artificial neural network: the variational autoencoder. This autoencoder could perform a nonlinear dimensionality reduction and encourage the latent features to follow a predefined distribution (usually a normal distribution) by learning the most important relationships hidden in data. In this study, we investigate the capacity of autoencoders to model item-factor relationships in simulated data, which encompasses linear and nonlinear associations. We also extend our investigation to a real dataset. Results on simulated data show that the variational autoencoder performs similarly to factor analysis when the relationships among observed and latent variables are linear, and it is able to reproduce the factor scores. Moreover, results on nonlinear data show that, differently than factor analysis, it can also learn to reproduce nonlinear relationships among observed variables and factors. The factor score estimates are also more accurate with respect to factor analysis. The real case results confirm the potential of the autoencoder in reducing dimensionality with mild assumptions on input data and in recognizing the function that links observed and latent variables.

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent space in exploring complex realities such as image space. We focus on DaVinciFace, an AI-based system that explores the StyleGAN2 space to create a high-quality portrait for anyone in the style of the Renaissance genius Leonardo da Vinci. The user enters one of their portraits and receives the corresponding Da Vinci-style portrait as an output. Since most of Da Vinci\'s artworks depict young and beautiful women (e.g., \"La Belle Ferroniere\", \"Beatrice de\' Benci\"), we investigate the ability of DaVinciFace to account for other social categorizations, including gender, race, and age. The experimental results evaluate the effectiveness of our methodology on 1158 portraits acting on the vector representations of the latent space to produce high-quality portraits that retain the facial features of the subject\'s social categories, and conclude that sparser vectors have a greater effect on these features. To objectively evaluate and quantify our results, we solicited human feedback via a crowd-sourcing campaign. Analysis of the human feedback showed a high tolerance for the loss of important identity features in the resulting portraits when the Da Vinci style is more pronounced, with some exceptions, including Africanized individuals.

Alzheimer\'s disease (AD) is affecting a growing number of individuals. As a result, there is a pressing need for accurate and early diagnosis methods. This study aims to achieve this goal by developing an optimal data analysis strategy to enhance computational diagnosis. Although various modalities of AD diagnostic data are collected, past research on computational methods of AD diagnosis has mainly focused on using single-modal inputs. We hypothesize that integrating, or \"fusing,\" various data modalities as inputs to prediction models could enhance diagnostic accuracy by offering a more comprehensive view of an individual\'s health profile. However, a potential challenge arises as this fusion of multiple modalities may result in significantly higher dimensional data. We hypothesize that employing suitable dimensionality reduction methods across heterogeneous modalities would not only help diagnosis models extract latent information but also enhance accuracy. Therefore, it is imperative to identify optimal strategies for both data fusion and dimensionality reduction. In this paper, we have conducted a comprehensive comparison of over 80 statistical machine learning methods, considering various classifiers, dimensionality reduction techniques, and data fusion strategies to assess our hypotheses. Specifically, we have explored three primary strategies: (1) Simple data fusion, which involves straightforward concatenation (fusion) of datasets before inputting them into a classifier; (2) Early data fusion, in which datasets are concatenated first, and then a dimensionality reduction technique is applied before feeding the resulting data into a classifier; and (3) Intermediate data fusion, in which dimensionality reduction methods are applied individually to each dataset before concatenating them to construct a classifier. For dimensionality reduction, we have explored several commonly-used techniques such as principal component analysis (PCA), autoencoder (AE), and LASSO. Additionally, we have implemented a new dimensionality-reduction method called the supervised encoder (SE), which involves slight modifications to standard deep neural networks. Our results show that SE substantially improves prediction accuracy compared to PCA, AE, and LASSO, especially in combination with intermediate fusion for multiclass diagnosis prediction.

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. The 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 factorisation (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 across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. 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.

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R-CNN) to accurately localize ablation areas. The two-stage detection process of Faster R-CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R-CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems.

Associations between brain structure and body mass index (BMI) are increasingly gaining attention. Although BMI-related regional alterations in brain morphology have been previously reported, the effect of BMI on the microstructural profiles, which provide information on the proxy of neuronal density within the cortex, is unexplored. In this study, we investigated the links between cortical layer-specific microstructural profiles and BMI in 302 neurologically healthy young adults. Using the microstructure-sensitive proxy based on the T1-and T2-weighted ratio, we estimated microstructural profile covariance (MPC) by calculating linear correlations of cortical depth-wise intensity profiles between different brain regions. Then, low-dimensional gradients of the MPC matrix were estimated using dimensionality reduction techniques, and the gradients were associated with BMI. Significant effects in the heteromodal association areas were observed. The BMI-gradient association map was related to the geodesic distance along the cortical surface, curvature, and sulcal depth, suggesting that the microstructural alterations occurred along the cortical topology. The BMI-gradient association map was further linked to cognitive states related to negative emotions. Our findings may provide insights into understanding the atypical cortical microstructure associated with BMI.