教学课程资源库 更多>>

关键词： Artificial Intelligence   Biological Sciences   Cancer   Computer Science   Omics   Transcriptomics  

摘要： Acute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy, We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches-in which multivariate signatures are learned directly from genome-wide data with no prior knowledge-to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow. Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omits approach wherein risk prediction, differential diagnosis, and subclassification of AML are achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning.

关键词： American Civil Liberties Union   capital punishment   crime   death penalty   domestic politics   general news   government bodies   international relations   judicial branch   Ledell Lee   legal action   manslaughter   murder   political   politics  

关键词： capital punishment   crime   death penalty   domestic politics   general news   government bodies   international relations   judicial branch   Ledell Lee   legal action   manslaughter   murder   political   politics  

关键词： American Civil Liberties Union   capital punishment   crime   death penalty   domestic politics   general news   government bodies   international relations   judicial branch   Ledell Lee   legal action   manslaughter   murder   political   politics  

关键词： artificial intelligence   computer science   corporate   e-government   general news   government policy   humanities   industrial news   information technology policy   international relations   internet search engines   machine learning   online service providers   political   politics   regulation   sciences   Sundar Pichai   tech   technology  

关键词： artificial intelligence   computer science   corporate   e-government   general news   government policy   humanities   industrial news   information technology policy   international relations   internet search engines   machine learning   online service providers   political   politics   regulation   sciences   Sundar Pichai   tech   technology  

关键词： artificial intelligence   computer science   corporate   e-government   general news   government policy   humanities   industrial news   information technology policy   international relations   internet search engines   machine learning   online service providers   political   politics   regulation   sciences   Sundar Pichai   tech   technology  

关键词： artificial intelligence   computer science   corporate   e-government   general news   government policy   humanities   industrial news   information technology policy   international relations   internet search engines   machine learning   online service providers   political   politics   regulation   sciences   Sundar Pichai   tech   technology  

关键词： Applied physics   Computer science   Electrical and electronic engineering   Electronic devices   Electronic properties and materials  

摘要： Classification is an important task at which both biological and artificial neural networks excel1,2. In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable3,4, simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density5, inherent parallelism and energy efficiency6,7. However, existing approaches either rely on the systems' time dynamics, which requires sequential data processing and therefore hinders parallel computation5,6,8, or employ large materials systems that are difficult to scale up7. Here we use a parallel, nanoscale approach inspired by filters in the brain1 and artificial neural networks2 to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction9, 10-11 through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates12 up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters2 that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data13. Our results establish a paradigm of silicon-based electronics for small-footprint and energy-efficient computation14.

关键词： Computational biology and bioinformatics   Computer science   Information technology  

摘要： Big data are difficult to handle. These tips and tricks can smooth the way.