Motivation: By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.Results: In this article, we propose a novel sparse Bayesian model for joint network and node selection. This model integrates information from networks (e.g. pathways) and nodes (e.g. genes) by a hybrid of conditional and generative components. For the conditional component, we propose a sparse prior based on graph Laplacian matrices, each of which encodes detailed correlation structures between network nodes. For the generative component, we use a spike and slab prior over network nodes. The integration of these two components, coupled with efficient variational inference, enables the selection of networks as well as correlated network nodes in the selected networks.Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods. Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature. In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.Availability: The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html.Contact: alanqi@purdue.edu
Localizing small damages often requires sensors be mounted in the proximity of damage to obtain high Signal-to-Noise Ratio in system frequency response to input excitation. The proximity requirement limits the applicability of existing schemes for low-severity damage detection as an estimate of damage location may not be known a priori. In this work it is shown that spatial locality is not a fundamental impediment; multiple small damages can still be detected with high accuracy provided that the frequency range beyond the first five natural frequencies is utilized in the Frequency response functions (FRF) curvature method. The proposed method presented in this paper applies sensitivity analysis to systematically unearth frequency ranges capable of elevating damage index peak at correct damage locations. It is a baseline-free method that employs a smoothing polynomial to emulate reference curvatures for the undamaged structure. Numerical simulation of steel-beam shows that small multiple damages of severity as low as 5% can be reliably detected by including frequency range covering 5–10th natural frequencies. The efficacy of the scheme is also experimentally validated for the same beam. It is also found that a simple noise filtration scheme such as a Gaussian moving average filter can adequately remove false peaks from the damage index profile.
Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses' hospital administration's top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients' LOS to resolve these issues. For this purpose, a very large-sized data (more than 2.3 million patients' data) related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow, Tuberculosis, Intestinal Transplant, Mental illness, Leukaemia, Spinal cord injury, Trauma, Rehabilitation, Kidney and Alcoholic Patients, HIV Patients, Malignant Breast disorder, Asthma, Respiratory distress syndrome, etc. have been analyzed to predict the LOS. We selected six Machine learning (ML) models named: Multiple linear regression (MLR), Lasso regression (LR), Ridge regression (RR), Decision tree regression (DTR), Extreme gradient boosting regression (XGBR), and Random Forest regression (RFR). The selected models' predictive performance was checked using R square and Mean square error (MSE) as the performance evaluation criteria. Our results revealed the superior predictive performance of the RFR model, both in terms of RS score (92%) and MSE score (5), among all selected models. By Exploratory data analysis (EDA), we conclude that maximum stay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital. Based on the average LOS, results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases. This finding could help predict the
High error-rates preclude the preparation of fully error-corrected logical qubit state on noisy intermediate scale quantum (NISQ) computers. When operand logical qubits inherit large state-preparation noise, it is difficult to show that subsequent logical gate fails less frequently than its physical (unprotected) version. We articulate a scheme of decoupling transversal logical gate errors from state-preparation noise and experimentally validate its use-case for IBM Q quantum processors. We find that in the absence of state preparation noise, the IBM Q processors significantly raise the likelihood of certain two-qubit errors in the operand(s) of [[7, 1, 3]] transversal gates. Yet, encoding can still be shown to improve the gate fidelity provided that the gate operands are strategically decoded/corrected for the likely two-qubit errors in lieu of their less likely single-qubit counterparts. This trade-off enables quantum CSS code to principally correct longer strings of errors without increasing the codeword size and paves new avenues of investigating fault-tolerance in NISQ computers.
We investigate the efficacy of topological quantum error-correction in correlated noise model which permits collective coupling of all the codeword qubits to the same non-Markovian environment. In this noise model, the probability distribution over set of phase-flipped qubits, decays sub-exponentially in the size of the set and carries non-trivial likelihood of the occurring large numbers of qubits errors. We find that in the presence of noise correlation, one cannot guarantee arbitrary high computational accuracy simply by incrementing the codeword size while retaining constant noise level per qubit operation. However, if instead, per-operation qubit error probability in an n-qubits long codeword is reduced O(\sqrt{n}) times below the accuracy threshold, arbitrarily accurate quantum computation becomes feasible with acceptable scaling of the codeword size. Our results suggest that progressively reducing noise level in qubits and gates is as important as continuously integrating more qubits to realize scalable and reliable quantum computer.
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