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Background: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement., Objective: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis., Methods: The study focused on the extraction of features from the phonatory subsystem-jitter, shimmer, harmonics-to-noise ratio, and pitch-from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained., Results: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement., Conclusions: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement., (©Alberto Tena, Francec Claria, Francesc Solsona, Einar Meister, Monica Povedano. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 10.03.2021.)

A current problem in deoxyribonucleic acid (DNA) sequence analysis is to determine the exact locations of the genes and also in eukaryotes, the protein-coding regions in the mRNA primary transcript (pre-mRNA).The conversion into discrete numerical values of the symbols associated to the nucleotides of these sequences allows for a signal to address the problems related to localization and annotation of genes. In this work, thermodynamic data of free energy changes (ΔG°) on the formation of a duplex structure of DNA or RNA are used to convert the symbols into numerical values associated with the nucleotide sequence pre-mRNA. This study presents an analysis, based on techniques of time-frequency representation of a large number of gene sequences, in order to find variables related to pre-mRNA that could best characterize and discriminate coding regions from non-coding regions. It has been found that instantaneous frequency variables and instantaneous spectral energy variables in different frequency bands, allowed exons and introns to be correctly classified with more than 85%.

Regulatory sequence detection is a fundamental challenge in computational biology. One key process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can show binding to the same factor. This variability found in binding sequences increases the difficulty of their detection using computational algorithms. In this manuscript, a method for the detection of binding sites is proposed, based on the correlation between binding sequence positions through information theoretical measures. Efficiency values of the method are reported in the form of Receiver Operating Characteristic curves on the detection of different transcription factors of the Saccharomyces cerevisiae organism. We compare our results with other known motif detection Motif Discovery scan (MDscan).

In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measures such as Rényi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency-based Rényi measures. Results are reported in this work comparing transition frequencies (i.e., dinucleotides) and base frequencies for Shannon and parametric Rényi entropies for a number of binding sites found in E. Coli, lambda and T7 organisms. We observe that the information provided by both approaches is not redundant. Furthermore, under the presence of noise in the binding site matrix we observe overall improved robustness of nucleotide transition-based algorithms when compared with nucleotide frequency-based method.

In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measure such as Renyi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency based Renyi measures. Results are reported in this manuscript comparing transition frequencies (i.e. dinucelotides) and base frequencies for Shannon and parametric Renyi for a number of binding sites found in E. Coli, lambda and T7 organisms. We observe that, for the evaluated datasets, the information provided by both approaches is not redundant, as they evolve differently under increasing Renyi orders.