The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

Comparative Analysis of Methods for Estimation of Undirected Coupling from Time Series of Intracranial Eegs of Cortex of Rats-Genetic Models of Absence Epilepsy

Studying coupling between brain areas from its electromagnetic activity is one of the key approaches in epilepsy research now, since epileptic activity has been considered to be a result of pathological synchronization in the brain. Often, research is conducted on animal models, because this allows to perform intracranial measurement, and to get rid of interference caused by the skull and to receive signals from deeper regions of the brain such as thalamus or hippocampus. In this study, the intracranial recordings from the frontal and parietal areas of cortex are investigated with a nonlinear correlation coefficient and a mutual information function in a sliding time window. The coupling estimates obtained were subjected for statistical analysis for significance using surrogate data. The dynamics of connectivity between the frontal cortex and the parietal cortex was shown to vary from seizure to seizure and from animal to animal. Therefore, estimates of the significant change in connectivity associated with initiation of the absense seizure, found previously based on averaging over a large number of animals and a large number of seizures for an each animal, can be a result of contribution of a relatively small number of seizures (less than a half of considered), for which the changes are significant.

Auditory and Nociceptive Stimuli Responses in the Electroencephalogram

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Neural Signals and Images“.Objectives: An efficient way to investigate the neural basis of nociceptive responses is the analysis of the event-related brain potentials (ERPs). The main objective of this work was to study how adaptation and fatigue affect the ERPs to stimuli of different modalities, by characterizing the responses to infrequent and frequent stimulation in different recording periods.Methods: In this work, series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation were analyzed with time-frequency representation and non-linear measures as spectral entropy and auto-mutual information function. The study was performed by considering the traditional EEG frequency bands.Results: The defined measures presented a statistical significance p-value < 0.01 and accuracy higher than 60% by differentiating windows of response to infrequent (I) and frequent (F) stimuli between the start and end of the EEG recording.Conclusions: These measures permitted to observe some aspects of the subject’s adaptation and the nociceptive response.

Periodic Distribution of a Putative Nucleosome Positioning Motif in Human, Nonhuman Primates, and Archaea: Mutual Information Analysis

Recently, Trifonov's group proposed a 10-mer DNA motif YYYYYRRRRR as a solution of the long-standing problem of sequence-based nucleosome positioning. To test whether this generic decamer represents a biological meaningful signal, we compare the distribution of this motif in primates and Archaea, which are known to contain nucleosomes, and in Eubacteria, which do not possess nucleosomes. The distribution of the motif is analyzed by the mutual information function (MIF) with a shifted version of itself (MIF profile). We found common features in the patterns of this generic decamer on MIF profiles among primate species, and interestingly we found conspicuous but dissimilar MIF profiles for each Archaea tested. The overall MIF profiles for each chromosome in each primate species also follow a similar pattern. Trifonov’s generic decamer may be a highly conserved motif for the nucleosome positioning, but we argue that this is not the only motif. The distribution of this generic decamer exhibits previously unidentified periodicities, which are associated to highly repetitive sequences in the genome.Alurepetitive elements contribute to the most fundamental structure of nucleosome positioning in higher Eukaryotes. In some regions of primate chromosomes, the distribution of the decamer shows symmetrical patterns including inverted repeats.

Robustness Analysis of QIM Watermarking against Additional Noise

A mutual information function was defined as a criterion measuring the robustness of watermarking algorithm. Considering QIM scheme, error probability of watermarking can be calculated to validate the measurement of mutual information function. By mean of numerical computation, mutual information under Gaussian noise and uniform noise is calculated with change of noise standard deviation. In the experiment, an audio section is selected as the host and their third lever wavelet detail coefficients are quantified according to watermark bit series. Experiment results show that statistic Bit Error Rate (BER) is matched with evaluation conclusion of mutual information method when step is on the small side. Mutual information function can be selected as a cost function to evaluate the robustness of watermarking algorithm, and predict the BER.