Ensemble learning has been proven effective in enhancing classification accuracy by aggregating predictions from multiple base classifiers. This paper introduces a novel approach to augmenting weak projection-based classifiers using a Neural Network within a stacking ensemble framework. The proposed method capitalizes on the diverse strengths of both linear and complex models, harnessing the interpretability of projection-based classifiers, while leveraging the pattern recognition capabilities of Neural Networks. We present a comprehensive algorithm involving dataset selection, preprocessing, base model training, meta-feature generation, and Neural Network architecture design and training. Extensive experiments demonstrate the efficiency of our approach on a variety of high-dimensional biomedical datasets. Our results showcase significant accuracy improvements over standalone projection-based classifiers and conventional ensemble methods. We analyze the interpretability of the hybrid ensemble, shedding light on the insights drawn from its Neural Network component. This work not only advances the field of ensemble learning, but also underscores the potential of combining disparate classifier paradigms to achieve superior predictive performance. The code for this study is available1.1.https://github.com/panagiotisanagnostou/NNv-MRPV

In recent years, feature selection has become an increasingly active field of data science and machine learning research. Most of the datasets that are being used nowadays for various machine learning tasks consist of thousands of features (columns), which make them extremely complex and difficult to work with. In this paper, we propose a feature selection methodological pipeline that can be used to reduce the complexity of high dimensional datasets through the elimination of redundant and/or non-informative features as well as to improve the performance of machine learning models which are trained on high dimensional datasets. The proposed method has been applied to high-dimensional biomedical data and compared against a classic filter-based feature selection algorithm. Specifically, the method was applied to gene expression profiles of a single-cell RNA-seq dataset from healthy and infected by covid-19 human samples.

We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.

Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which are generated by emerged medical imaging, sensor technologies and high-throughput technologies. An indicative example is the single cell sequencing technology which concerns the genome sequencing examination of hundreds of separate cells in a single tumor. Consequently, open challenges arising from this emerged technology and generally from the evolution of biomedical technologies under the big data perspective. Also, given the fact that approaches based on high-performance computing require high computing resources and advanced developers, solutions that reduce the problem complexity remain very attractive. Following this direction, in this paper a classification scheme based on Multiple Random Projections and Voting is presented. Random Projections offer a platform not only for a low computational time analysis by significantly reducing the data dimensionality, but also for an accurate analysis which may well exceed classical classification approaches. The proposed method was applied on real biomedical high dimensional data and compared against well-known classification schemes as to Random Projection-based cutting-edge methods. Specifically, we applied it on expression profiles for single-cell RNA-seq data from non-diabetic and type 2 diabetic human samples. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for biomedical Big Data analysis and knowledge discovery.

Recent sequencing technology breakthroughs have resulted in a dramatic increase in the amount of available sequencing data, enabling major scientific advances in biology and medicine. Nowadays, sequencing transcriptome data of single cells (scRNA-seq) are growing rapidly, posing new challenges in their analysis, mostly due to their high dimensionality. In this paper, we study the problem of visualizing such high-dimensional scRNA-seq data. A new visualization scheme is presented based on a customized distance matrix retrieved by applying independently Nearest Neighbors search through multiple Random Projections. The proposed method is compared against well-known dimensionality reduction and visualization techniques showing its capabilities and performance.

The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high dimensional this is indispensable, since the mobile device has to balance its processing functionalities to additional services. However, developing efficient algorithms could allow various types of analysis to be performed locally, avoiding the necessity of a constantly connected device. In this work, we present a methodology that combines lightweight dimensionality reduction and change detection techniques. The experimental results justify its impressive performance and subsequently its usefulness in several tasks.