Purpose: The main objective of this study is to propose a Keystroke dynamics (KD) based Parkinson’s disease (PD) indicator using both fixed-text and free-text typing habits on conventional keyboards and homogeneous ensemble learning with several fully balanced bootstrapped training sets for more accurate and robust eHealth applications. Furthermore, this study addresses the six key hypotheses related to our main objective of understanding the risks in this screening process that were previously unknown but important for further improvement. Methods: For a predetermined window length, the Colin Bannard and neuroQWERTY MIT-CSXPD datasets were used to extract a series of key hold times and the time gaps between two consecutive presses and releases as a feature set. A wide range of statistical tools were employed to create a machine learning-ready feature arrangement for continuously generated patterns. A homogeneous ensemble learning approach was then developed using bootstrapping and under-sampling while retaining any rare samples. This model was validated using fifteen fixed-text and free-text inputs obtained from early-stage PD patients, De-novo PD patients, and healthy controls. Results: In the leave-one-user-out cross-validation (LOUOCV) evaluation, the maximum observed area under curve (AUC) is 85.99% ± 0.41 for fixed-text input. However, for free-text inputs, AUC is 78.3% ± 0.86, with a sensitivity/specificity of 74.46%/82.13%. The AUC for detecting De-novo patients is 79.83% ± 1.26, which is somewhat lower than the AUC for determining the early stage of the disease, which is 83.81% ± 0.83. Conclusion: The proposed model is more robust, usable (covert way of data acquisition), fast, and has ease of integration into conventional desktops/laptops suitable for real-life eHealth that could help for better diagnosis, early detection in a home environment, future reference, and treatment or therapy management. However, the subject size, severity levels of the disease, typing duration, feature composition, error in typing, and machine learning (ML) method selection influence the performance of this model. Therefore, careful attention is necessary while designing PD indicators using the proposed approach.

MOTIVATION: A rigorous yet general mathematical approach to mutagenesis, especially one capable of delivering systems-level perspectives would be invaluable. Such systems-level understanding of phage resistance is also highly desirable for phage-bacteria interactions and phage therapy research. Independently, the ability to distinguish between two graphs with a set of common or identical nodes and identify the implications thereof, is important in network science. RESULTS: Herein, we propose a measure called shortest path alteration fraction (SPAF) to compare any two networks by shortest paths, using sets. When SPAF is one, it can identify node pairs connected by at least one shortest path, which are present in either network but not both. Similarly, SPAF equalling zero identifies identical shortest paths, which are simultaneously present between a node pair in both networks. We study the utility of our measure theoretically in five diverse microbial species, to capture reported effects of well-studied mutations and predict new ones. We also scrutinize the effectiveness of our procedure through theoretical and experimental tests on Mycobacterium smegmatis mc2155 and by generating a mutant of mc2155, which is resistant to mycobacteriophage D29. This mutant of mc2155, which is resistant to D29 exhibits significant phenotypic alterations. Whole-genome sequencing identifies mutations, which cannot readily explain the observed phenotypes. Exhaustive analyses of protein-protein interaction network of the mutant and wild-type, using the machinery of topological metrics and differential networks does not yield a clear picture. However, SPAF coherently identifies pairs of proteins at the end of a subset of shortest paths, from amongst hundreds of thousands of viable shortest paths in the networks. The altered functions associated with the protein pairs are strongly correlated with the observed phenotypes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In optogenetics, designing modules of long or short signaling state lifetime is necessary for control over precise cellular events. A critical parameter for designing artificial or synthetic photoreceptors is the signaling state lifetime of photosensor modules. Design and engineering of biologically relevant artificial photoreceptors is based on signaling mechanisms characteristic of naturally occurring photoreceptors. Therefore identifying residues important for light-dark transition is a definite first step towards rational design of synthetic photoreceptors. A thorough grasp of detailed mechanisms of photo induced signaling process would be immensely helpful in understanding the behaviour of organisms. RESULTS: Herein, we introduce the technique of differential networks. We identify key biological interactions, using light-oxygen-voltage domains of all organisms whose dark and light state crystal structures are simultaneously available. Even though structural differences between dark and light states are subtle (other than the covalent bond formation between flavin chromophore and active site Cysteine), our results successfully capture functionally relevant residues and are in complete agreement with experimental findings from literature. Additionally, using sequence-structure alignments, we predict functional significance of interactions found to be important from network perspective yet awaiting experimental validation. Our approach would not only help in minimizing extensive photo-cycle kinetics procedure but is also helpful in providing first-hand information on the fundamentals of photo-adaptation and rational design of synthetic photoreceptors in optogenetics. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.