Results 21 to 30 of about 851,309 (264)
Molecular Oncology, EarlyView.Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley +1 more source
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets [PDF]
, 1997 This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a ...
Lee, M. S., Moore, A.
core +3 more sources
Machine Learning in Official Statistics
, 2018 In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Beck, Martin +2 more
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Online Learning for Statistical Machine Translation [PDF]
Computational Linguistics, 2016 We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings.
openaire +3 more sources
Molecular Oncology, EarlyView.There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes +20 more
wiley +1 more source
Return of Frustratingly Easy Domain Adaptation
, 2015 Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning ...
Feng, Jiashi, Saenko, Kate, Sun, Baochen
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Machine learning, statistical learning and the future of biological research in psychiatry [PDF]
Psychological Medicine, 2016 Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may ...
Iniesta, R.; Stahl, D.; McGuffin, P.
openaire +6 more sources
Molecular Oncology, EarlyView.This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh +8 more
wiley +1 more source
Optimaztion of Fantasy Basketball Lineups via Machine Learning [PDF]
, 2019 Machine learning is providing a way to glean never before known insights from the data that gets recorded every day. This paper examines the application of machine learning to the novel field of Daily Fantasy Basketball.
Earl, James
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Molecular Oncology, EarlyView.In patients treated with atezolizumab as a part of the MyPathway (NCT02091141) trial, pre‐treatment ctDNA tumor fraction at high levels was associated with poor outcomes (radiographic response, progression‐free survival, and overall survival) but better sensitivity for blood tumor mutational burden (bTMB).
Charles Swanton +17 more
wiley +1 more source