The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs' testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.

Author identification aims to uncover the individuals responsible for creating texts, and it is a burgeoning field of research with diverse applications in literary analysis, cybersecurity, forensics, and social media investigations. The primary goal of this paper is to perform an analysis on author identification. We introduce two main elements within this study. The initial element utilizes six machine learning (ML) techniques: Decision Trees (DT), Logistic Regression (LR), k Nearest Neighbors (K-NN), Random Forests (RF), Support Vector Machines (SVM), and Naive Bayes (NB), with the application of the TF-IDF method for feature extraction. The second part involves the experimentation with two variations of Deep Learning (DL) models—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—employing word embedding for the input vector. To validate our approach, we conducted an experimental study using the Reuters 50_50 dataset, employing two learning modes: Hold-out and 10-fold cross validation. The obtained results, measured in terms of Accuracy (ACC), Precision (PREC), Recall (REC), and F1-score (F1), demonstrate the superior performance of DL techniques when employing a 10-fold cross-validation strategy compared to the current state-of-the-art methods. The experiments detailed in this paper showcase the efficacy of our proposed DL models, yielding the best results for author identification.

— the perfect alignment between three or more sequences of protein, RNA or DNA is a very difficult task in Bioinformatics. There are many techniques for alignment of multiple sequences. Many techniques enlarge speed and do not have a concern with the accuracy of the resulting alignment. However, other techniques heighten accuracy and do not have a concern with the speed. The vital goals of any technique are (a) reducing memory and execution time requirements, and (b) increasing the accuracy of multiple sequence alignment on large-scale datasets. PROBCONS is a multiple protein sequence alignment (MPSA) tool that achieves the most expected accuracy, but it has a time-consuming problem. To solve this problem and enlarging the accuracy of the MPSA, E-PROBCONS is proposed to enhance PROBCONS tool. E- PROBCONS cluster the large multiple protein sequences into structurally similar protein sequences. Then PROBCONS MPSA tool will be performed in parallel on the Amazon Elastic Cloud (EC2). The proposed approaches are more suitable for large-scale data sets and short sequences. Comparing with algorithms (e.g., PROBCONS, KALIGN, and HALIGN I), provided more than 50% improvement in terms of average sum of pairs alignment scores (SPscores) and reduce the execution time for producing the alignment result. The proposed approaches are implemented on big data framework Hadoop Map-Reduce platform in order to improve the scalability with different protein datasets.