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A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.
Heliyon
February 2024
Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge.
View Article and Find Full Text PDFHarnessing the Deluge of Rhythm-Monitoring Data for the Prevention of Stroke.
N Engl J Med
September 2023
From the Division of Cardiovascular Medicine, Brigham and Women's Hospital, and the Division of Cardiovascular Medicine, Harvard Medical School - both in Boston.
Improving the quality of predictive models in small data GSDOT: A new algorithm for generating synthetic data.
PLoS One
April 2022
NOVA Information Management School, Campus de Campolide, Lisboa, Portugal.
In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve problems through the use of supervised learning methods can have a significant impact in many important areas. The insufficient size of training data usually results in unsatisfactory performance of machine learning algorithms.
View Article and Find Full Text PDFAn evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials.
J Clin Epidemiol
May 2021
EPPI-Centre, Department of Social Science, University College London, 18 Woburn Square, London, WC1H 0NR, UK.
Background And Objectives: Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a "crowd" of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to health care.
View Article and Find Full Text PDFBackground: At a time when research output is expanding exponentially, citizen science, the process of engaging willing volunteers in scientific research activities, has an important role to play in helping to manage the information overload. It also creates a model of contribution that enables anyone with an interest in health to contribute meaningfully and in a way that is flexible. Citizen science models have been shown to be extremely effective in other domains such as astronomy and ecology.
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