11 papers · ranked by Valyu relevance
Christopher M. Wilson, Kaiqiao Li, Pei-Fen Kuan, Xuefeng Wang
Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations…
Jérôme Mariette, Nathalie Villa-Vialaneix
Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a…
Nisar Wani, Khalid Raza
Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high…
Christopher M. Wilson, Kaiqiao Li, Qiang Sun, Pei Fen Kuan + 1 more
The Cox proportional hazard model is the most widely used method in modeling time-to-event data in the health sciences. A common form of the loss function in machine learning for survival data is also mainly based on Cox partial likelihood function, due to its simplicity. However, the optimization problem becomes…
Sikta Das Adhikari, Yuehua Cui, Jianrong Wang
GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS…
Hyunwook Koh
In high-dimensional omics studies, researchers often conduct kernel association testing to power-fully detect the relationship of the genetic or microbial composition with human health or disease. Especially, in human microbiome studies, its dimension reduction analysis follows to visually represent complex microbiome…
Parisa Shahnazari, Kaveh Kavousi, Hamid Reza Khorram Khorshid, Bahram Goliaei + 1 more
Integrating multiple omics modalities is a crucial strategy in cancer research, particularly in metabolomics, enabling early detection and detailed exploration of cancer biomarker signatures. This study evaluates five strategies for integrating metabolomics data from liquid chromatography-mass spectrometry, gas…
Hyunwook Koh
There are numerous potential confounders, including genetic, environmental, technical, and demographic factors. These factors may be known or unknown, measured or unmeasured; hence, it is extremely challenging to capture them in downstream data analysis. However, randomized block design is an efficient design technique…
Chaitanya Chintaluri, Marta Kowalska, Władysław Średniawa, Michał Czerwiński + 3 more
Kernel Current Source Density (kCSD), which we introduced in 2012, is a kernel-based method to estimate current source density (CSD) from extracellular potentials recorded with arbitrarily placed electrodes. Estimating reconstruction errors in CSD has been an outstanding challenge. To address it, here we revisit kCSD…
Subba Reddy Oota, Archi Yadav, Arpita Dash, Raju S. Bapi + 1 more
Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the restingstate as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a…
Anqi Wu, Samuel A. Nastase, Christopher A. Baldassano, Nicholas B. Turk-Browne + 3 more
A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at…