Background: Error Correction is an important task in the analysis and manipulations of NGS data. The purpose of error correction is to facilitate data analysis for large projects like de novo assembly project. Here we present a new hybrid algorithm for error correction of long reads using short reads. Our algorithm can be flexibly adapted to different types of errors. Next, we make a de novo assembly for corrected long reads. Objective: We present MiRCA (MinIon Reads Correction Algorithm) a hybrid approach based on the sequences alignments that detects and corrects errors for MinIon long reads using Illumina short reads. Methods: In our approach, we operate in four steps. First, we make a Quality Control and Cleaning data. Second, we use the contig forming for the Pre-Error Correction Step. Third, we use the alignment to align pre-assembled contig to long reads and we use this alignment to correction erroneous long reads. Finally, we do an assembly for the corrected long reads. Results: The results of mapping of S.cerevisaeW303 and E.coli genomes shows that our error correction approach produce a high quality long reads with mapping rate similar to 99% to the reference genome in reasonable time. For denovo assembly, the corrected long reads gives good assembly in a short running time compared to other error correction tools. Conclusion: MiRCA is a new hybrid approach that detects and corrects errors. It uses an alignment-based approach using pre-assembled short reads as a reference to correct nanopore long reads. The experimental evaluation of the corrected long reads on the reference genome of S. cerevisae and E.coli shows that MiRCA ensures best error correction compared to existing related works.

In this paper, we present Refin-Align a new refinement algorithm for a multiple sequence alignment. Refining alignment consists on constructing a new more accurate multiple sequence alignment from an initial one by applying some modifications. Our refinement algorithm Refin-Align uses a new definition of block and also our multiple sequence alignment algorithm Pro-malign. We assess our algorithm Refin-Align on multiple sequence alignment constructed by different algorithms using different benchmarks of protein sequences. In the most cases treated, our algorithm improves the scores of the multiple sequence alignment.

The emergence of 6G networks represents a revolutionary step in wireless communication, embedding artificial intelligence (AI) at its heart to facilitate levels of automation, optimization, and intelligent decision-making that have never been seen before. However, with the increasing dependence on AI comes the serious challenges of transparency, fairness, accountability, and trustworthiness. This chapter delves into the paramount significance of Explainable Artificial Intelligence (XAI) in resolving these issues so that AI-based decisions in 6G networks are transparent, ethical, and value-aligned. The chapter dives into their implementation in crucial areas including network optimization, security, user-centric services, and predictive maintenance. It covers the ethical issues such as bias, privacy, and regulatory compliance. By integrating XAI into the 6G network fabric, this chapter imagines a future where intelligent systems are efficient, scalable yet equitable, transparent, and sustainable, and which promotes trust and inclusivity in a hyper-connected world.

Biclustering is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that have similar behavior under subgroups of conditions. The classified genes can have independent behavior under other subgroups of conditions. Discovering such co-expressed genes, called biclusters, can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: bi-dimensionality which means grouping both genes and conditions simultaneously and overlapping which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms, which continue to be developed at a constant pace, give as output a large number of overlapping biclusters. Visualizing groups of biclusters is still a non-trivial task due to their overlapping. In this paper, we present the most interesting techniques to visualize groups of biclusters and evaluate them.