Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep lear...
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Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.
Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field m...
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Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes ...
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ISBN:
(数字)9783642396786
ISBN:
(纸本)9783642396779
This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes LNCS 7995, LNAI 7996, and CCIS 375 were carefully reviewed and selected from 561 submissions. The papers in this volume (CCIS 375) are organized in topical sections on Neural Networks; Systems Biology and Computational Biology; Computational Genomics and Proteomics; Knowledge Discovery and data Mining; Evolutionary learning and Genetic Algorithms; machinelearning Theory and Methods; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Unsupervised and Reinforcement learning; Intelligent Computing in Bioinformatics; Intelligent Computing in Finance/Banking; Intelligent Computing in Petri Nets/Transportation Systems; Intelligent Computing in Signal Processing; Intelligent Computing in Pattern Recognition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired Computing and Applications; computer Human Interaction using Multiple Visual Cues and Intelligent Computing; Special Session on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as...
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ISBN:
(数字)9783642018053
ISBN:
(纸本)9783642018046
Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.
This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machinelearning, the idea of incorpo...
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ISBN:
(数字)9783031881114
ISBN:
(纸本)9783031881107;9783031881138
This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machinelearning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.
This book constitutes the refereed proceedings of the joint conference on machinelearning and Knowledge Discovery in databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in t...
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ISBN:
(数字)9783642041808
ISBN:
(纸本)9783642041792
This book constitutes the refereed proceedings of the joint conference on machinelearning and Knowledge Discovery in databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the machinelearning Journal and the Knowledge Discovery and databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machinelearning and knowledge discovery in databases. The topics addressed are application of machinelearning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012.The 242 revised full papers presented in the three volumes...
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ISBN:
(数字)9783642318375
ISBN:
(纸本)9783642318368
This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012.
The 242 revised full papers presented in the three volumes LNCS 7389, LNAI 7390, and CCIS 304 were carefully reviewed and selected from 753 submissions. The papers in this volume (CCIS 304) are organized in topical sections on Neural Networks; Particle Swarm Optimization and Niche Technology; Kernel Methods and Supporting Vector machines; Biology Inspired Computing and Optimization; Knowledge Discovery and data Mining; Intelligent Computing in Bioinformatics; Intelligent Computing in Pattern Recognition; Intelligent Computing in Image Processing; Intelligent Computing in computer Vision; Intelligent Control and Automation; Knowledge Representation/Reasoning and Expert Systems; Advances in Information Security; Protein and Gene Bioinformatics; Soft Computing and Bio-Inspired Techiques in Real-World Applications; Bio-Inspired Computing and Applications.
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