SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective

The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO methods coequally treat all candidates, ignoring individual characteristics, thus frequently deteriorating performance with comparatively more predictors. Here, we propose a wavelet-based deep learning method in variable selection and prognosis formulation for PAAD with small samples and multisource information. With the genomic, epigenomic, and clinical cohort information from The Cancer Genome Atlas, the constructed five-molecule model is validated via Kaplan-Meier survival estimate, rendering significant prognosis capability on high- and low-risk subcohorts (p value < 0.0001), together with three predictors manifesting the individual prognosis significance (p value: 0.0012~0.024). Moreover, the performance of the prognosis model has been benchmarked against the traditional LASSO and wavelet-based methods in the 3- and 5-year prediction AUC items, respectively. Specifically, the proposed model with discrete stationary wavelet base (bior1.5) overwhelmingly outperformed traditional LASSO and wavelet-based methods (AUC: 0.787 vs. 0.782 and 0.721 for the 3-year case; AUC: 0.937 vs. 0.802 and 0.859 for the 5-year case). Thus, the proposed model provides a more accurate perspective, but with less predictor burden for clinical prognosis in the pancreatic carcinoma study.

Clustering and classification of virus sequence through music communication protocol and wavelet transform

The coronavirus pandemic became a major risk in global public health. The outbreak is caused by SARS-CoV-2, a member of the coronavirus family. Though the images of the virus are familiar to us, in the present study, an attempt is made to hear the coronavirus by translating its protein spike into audio sequences. The musical features such as pitch, timbre, volume and duration are mapped based on the coronavirus protein sequence. Three different viruses Influenza, Ebola and Coronavirus were studied and compared through their auditory virus sequences by implementing Haar wavelet transform. The sonification of the coronavirus benefits in understanding the protein structures by enhancing the hidden features. Further, it makes a clear difference in the representation of coronavirus compared with other viruses, which will help in various research works related to virus sequence. This evolves as a simplified and novel way of representing the conventional computational methods.