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"This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts. Hui Jiang is Professor of Electrical Engineering and Computer Science at York University, where he has been since 2002. His main research interests include machine learning, particularly deep learning, and its applications to speech and audio processing, natural language processing, and computer vision. Over the past 30 years, he has worked on a wide range of research problems from these areas and published hundreds of technical articles and papers in the mainstream journals and top-tier conferences. His works have won the prestigious IEEE Best Paper Award and the ACL Outstanding Paper honor"--
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This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
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Biosensing and bioimaging techniques largely promote the accurate diagnosis of intractable diseases, such as cancers, on the basis of specific molecular targets, or as-called biomarkers. To realize the assays with ultrahigh sensitivity and selectivity, the wide application of molecular biology and nanotechnology are of great necessity. Both directions may offer effective signal amplification strategies, as well as inhibition of cross-reaction interference. Therefore, this Special Issue, “Biosensing and Bioimaging: Trends and Perspective”, highlights the recent developments in intelligent biomolecule/nanostructure-based probes for bioimaging and biosensing applications. It consists of five peer-reviewed papers that cover current hot topics, such as biodegradable materials, DNA assembly, shRNA delivery and chimeric proteins, which will provide a unique perspective of advanced biosensing and bioimaging techniques.
Medicine --- Pharmacology --- Chagas disease --- immunoassays --- chimeric proteins --- stability --- biodegradable materials --- metal-organic framework --- metal ion nodes --- multimode imaging --- theranostic nano-platforms --- MCF-7 cells --- electrochemistry --- 2-D materials --- signal amplification --- DNA assembly --- bio-responsive fluorescent complexes --- shRNA delivery --- LncRNA MALAT1 --- cancer cells bioimaging --- therapeutics --- autophagy --- biosensor --- microfluidics --- organ-on-a-chip --- lung model --- lung-on-a-chip
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In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid–environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq --- n/a --- lipid-environment interaction
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Biosensing and bioimaging techniques largely promote the accurate diagnosis of intractable diseases, such as cancers, on the basis of specific molecular targets, or as-called biomarkers. To realize the assays with ultrahigh sensitivity and selectivity, the wide application of molecular biology and nanotechnology are of great necessity. Both directions may offer effective signal amplification strategies, as well as inhibition of cross-reaction interference. Therefore, this Special Issue, “Biosensing and Bioimaging: Trends and Perspective”, highlights the recent developments in intelligent biomolecule/nanostructure-based probes for bioimaging and biosensing applications. It consists of five peer-reviewed papers that cover current hot topics, such as biodegradable materials, DNA assembly, shRNA delivery and chimeric proteins, which will provide a unique perspective of advanced biosensing and bioimaging techniques.
Chagas disease --- immunoassays --- chimeric proteins --- stability --- biodegradable materials --- metal-organic framework --- metal ion nodes --- multimode imaging --- theranostic nano-platforms --- MCF-7 cells --- electrochemistry --- 2-D materials --- signal amplification --- DNA assembly --- bio-responsive fluorescent complexes --- shRNA delivery --- LncRNA MALAT1 --- cancer cells bioimaging --- therapeutics --- autophagy --- biosensor --- microfluidics --- organ-on-a-chip --- lung model --- lung-on-a-chip
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In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
Research & information: general --- Mathematics & science --- multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid-environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq
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Biosensing and bioimaging techniques largely promote the accurate diagnosis of intractable diseases, such as cancers, on the basis of specific molecular targets, or as-called biomarkers. To realize the assays with ultrahigh sensitivity and selectivity, the wide application of molecular biology and nanotechnology are of great necessity. Both directions may offer effective signal amplification strategies, as well as inhibition of cross-reaction interference. Therefore, this Special Issue, “Biosensing and Bioimaging: Trends and Perspective”, highlights the recent developments in intelligent biomolecule/nanostructure-based probes for bioimaging and biosensing applications. It consists of five peer-reviewed papers that cover current hot topics, such as biodegradable materials, DNA assembly, shRNA delivery and chimeric proteins, which will provide a unique perspective of advanced biosensing and bioimaging techniques.
Medicine --- Pharmacology --- Chagas disease --- immunoassays --- chimeric proteins --- stability --- biodegradable materials --- metal-organic framework --- metal ion nodes --- multimode imaging --- theranostic nano-platforms --- MCF-7 cells --- electrochemistry --- 2-D materials --- signal amplification --- DNA assembly --- bio-responsive fluorescent complexes --- shRNA delivery --- LncRNA MALAT1 --- cancer cells bioimaging --- therapeutics --- autophagy --- biosensor --- microfluidics --- organ-on-a-chip --- lung model --- lung-on-a-chip
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Nederlands-Chinees en Chinees-Nederlands in één band in zakformaat.
Chinese language --- Dictionaries --- Dutch --- Dutch language --- Chinese
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