KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).

Spiking Neural Networks (SNNs) have emerged as a prominent paradigm for brain-inspired computing, capable of processing temporal information and event-driven data in an efficient and biologically plausible manner. However, their revolutionary and complex nature is one of the key reasons why SNNs are not yet a widely used approach in contrast to traditional Artificial Neural Networks (ANNs). In this paper, we present a comprehensive SNN framework that offers user-friendly implementation. It has been designed so that it is compatible with other well-known software tools for data science, being easy to integrate with them. We showcase the versatility of the framework by applying it to various well-known benchmarking datasets, including image processing of handwritten numbers, time-series forecasting and an advance use case for speech recognition, achieving competitive results compared to traditional ANNs. Our SNN framework aims to bridge the gap between neuroscience and artificial intelligence, empowering researchers and practitioners with an accessible tool to explore the potential of neuro-inspired computing in advancing the field of AI.

Sleep analysis is crucial for diagnosing disorders and understanding physiological patterns. However, accurately labeling hypnograms is challenging due to significant interrater variability and resource constraints that limit the use of multiple experts. This study introduces a novel verification tool that assesses biomedical signals, including heart rate and activity, alongside labeled hypnograms against state-of-the-art conditions. The tool was developed to evaluate the quality and reliability of hypnogram annotations, providing feedback on the credibility of labels generated by automated methods and single expert annotations. It cross-references labeled data against physiological signals and identifies discrepancies or anomalies that may indicate errors in the labeling process. For validation, the tool was applied to the MESA dataset, a well-known collection of sleep data. Application of the tool demonstrated its ability to provide objective feedback on hypnogram labels and to identify anomalies in patient data, potentially assisting clinicians in refining their assessments. By offering a user-friendly interface and flexible design, this verification tool enhances the accuracy of sleep stage annotations and serves as a valuable resource for both clinical and research applications.