Background: DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques.
Results: Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies.
Conclusion: We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.
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http://dx.doi.org/10.1186/1471-2105-15-137 | DOI Listing |
Small
January 2025
Key Laboratory of Aerospace Materials and Performance (Ministry of Education) School of Materials Science and Engineering, Beihang University, No.37 Xueyuan Road, Beijing, 100191, P. R. China.
A reasonable construction of hollow structures to obtain high-performance absorbers is widely studied, but it is still a challenge to select suitable materials to improve the low-frequency attenuation performance. Here, the FeO@C@NiO nanoprisms with unique tip shapes, asymmetric multi-path hollow cavity, and core-shell heteroepitaxy structure are designed and synthesized based on anisotropy and intrinsic physical characteristics. Impressively, by changing the load of NiO, the composites achieve strong absorption, broadband, low-frequency absorption: the reflection loss of -55.
View Article and Find Full Text PDFNMR Biomed
March 2025
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
The purpose of this study was to measure T and T relaxation times of NAD proton resonances in the downfield H MRS spectrum in human brain at 7 T in vivo and to assess the propagation of relaxation time uncertainty in NAD quantification. Downfield spectra from eight healthy volunteers were acquired at multiple echo times to measure T relaxation times, and saturation recovery data were acquired to measure T relaxation times. The downfield acquisition used a spectrally selective 90° sinc pulse for excitation centered at 9.
View Article and Find Full Text PDFMicrosyst Nanoeng
January 2025
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Bulk Acoustic Wave (BAW) filters find applications in radio frequency (RF) communication systems for Wi-Fi, 3G, 4G, and 5G networks. In the beyond-5G (potential 6G) era, high-frequency bands (>8 GHz) are expected to require resonators with high-quality factor (Q) and electromechanical coupling ( ) to form filters with low insertion loss and high selectivity. However, both the Q and of resonator devices formed in traditional uniform polarization piezoelectric films of aluminum nitride (AlN) and aluminum scandium nitride (AlScN) decrease when scaled beyond 8 GHz.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Microsystems Group, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process.
View Article and Find Full Text PDFNeurocomputing (Amst)
January 2025
Department of Electrical and Computer Engineering, University of Maryland at College Park, 8223 Paint Branch Dr, College Park, MD, 20740, USA.
Inference using deep neural networks on mobile devices has been an active area of research in recent years. The design of a deep learning inference framework targeted for mobile devices needs to consider various factors, such as the limited computational capacity of the devices, low power budget, varied memory access methods, and I/O bus bandwidth governed by the underlying processor's architecture. Furthermore, integrating an inference framework with time-sensitive applications - such as games and video-based software to perform tasks like ray tracing denoising and video processing - introduces the need to minimize data movement between processors and increase data locality in the target processor.
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