13 papers · ranked by Valyu relevance
Naoual El aboudi, Laila Benhlima
The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures…
Lorraine Buis, Richard Matovu, Lei Guo, Bengie L Ortiz + 8 more
Background Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing…
Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie + 12 more
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed…
Ioannis K. Gallos, Dimitrios Tryfonopoulos, Gidi Shani, Angelos Amditis + 4 more
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient…
Davide Chicco, Luca Oneto, Erica Tavazzi, Francis Ouellette
Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the…
Joseph C. Mellor, Michael A. Stone, John Keane
Principles and Potential Authors: ['Joseph C. Mellor' 'Michael A. Stone' 'John Keane'] The ubiquity and cheapness of miniature low-power sensors, digital processing, and large amounts of storage contained in small packages has heralded the ability to acquire large amounts of data about systems during their course of…
S. M. Kamruzzaman, A. M. Jehad Sarkar
Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be…
Greg Finak, Bryan T. Mayer, William Fulp, Paul Obrecht + 4 more
A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code…
Amaryllis Mavragani, Rohan Alexander, Hyo Jung Kim, Manping Guo + 11 more
'Yiming Wang' 'Qiaoning Yang' 'Rui Li' 'Yang Zhao' 'Chenfei Li' 'Mingbo Zhu' 'Yao Cui' 'Xin Jiang' 'Song Sheng' 'Qingna Li' 'Rui Gao'] With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research…
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas + 3 more
The increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now…
Martin A. Lindquist, Stephan Geuter, Tor D. Wager, Brian S. Caffo
The preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper…
Gökmen Altay, Jose Zapardiel-Gonzalo, Bjoern Peters
Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…