This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-locat...
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ISBN:
(数字)9783642203893
ISBN:
(纸本)9783642203886
This book constitutes the refereed proceedings of the 9th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events. The 12 revised full papers presented together with 7 poster papers were carefully reviewed and selected from numerous submissions. All papers included topics of interest such as biomarker discovery, cell simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, and systems biology.
The recent dramatic advances inbiotechnology have led to an explosion of data in the life sciences at the molecular level as well as more detailed observation and ch- acterization at the cellular and tissue levels. It...
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ISBN:
(数字)9780817644154
The recent dramatic advances inbiotechnology have led to an explosion of data in the life sciences at the molecular level as well as more detailed observation and ch- acterization at the cellular and tissue levels. Itis now absolutely clear that one needs a theoretical framework inwhich to place this data to gain from it as much information as possible. Mathematical and computational modelling approaches are the obvious waytodothis. Heeding lessons from the physical sciences, one might expect that all areas in the life sciences would be actively pursuing quantitative methods to c- solidate the vast bodies of data that exist and to integrate rapidly accumulating new information. Remarkably, with a few notable exceptions, quite the contrary situation exists. However, things are now beginning to change and there is the sense that we are at the beginning of an exciting new era of research inwhich the novel problems posed by biologists will challenge the mathematicians and computer scientists, who, in turn, will use their tools to inform the experimentalists, who will verify model predictions. Only through such a tight interaction among disciplines will we have the opportunity to solve many of the major problems in the life sciences. One such problem, central to developmental biology, is the understanding of how various processes interact to produce spatio-temporal patterns in the embryo.
This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-so...
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ISBN:
(数字)9783031084119
ISBN:
(纸本)9783031084102;9783031084133
This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.
Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language mode...
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Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language models, some research works formulate the NER task into the machine reading comprehension form (MRC-form) to enhance their model generalization capability of commonsense knowledge. However, this transformation still faces the data-hungry issue with limited training data for the specific NER tasks. To address the low-resource issue in NER, we introduce a method named active multi-task-based NER (AMT-NER), which is a two-stage multi-task active learning training model. Specifically, A multi-task learning module is first introduced into AMT-NER to improve its representation capability in low-resource NER tasks. Then, a two-stage training strategy is proposed to optimize AMT-NER multi-task learning. An associated task of Natural Language Inference (NLI) is also employed to enhance its commonsense knowledge further. More importantly, AMT-NER introduces an active learning module, uncertainty selective, to actively filter training data to help the NER model learn efficiently. Besides, we also find different external supportive data under different pipelines improves model performance differently in the NER tasks. Extensive experiments are performed to show the superiority of our method, which also proves our findings that the introduction of external knowledge is significant and effective in the MRC-form NER tasks.
This IMA Volume in Mathematics and its Applications CHAOTIC PROCESSES IN THE GEOLOGICAL SCIENCES is based on the proceedings of a workshop which was an integral part of the 1989- 90 IMA program on "Dynamical Syst...
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ISBN:
(数字)9781468406436
ISBN:
(纸本)9781468406450
This IMA Volume in Mathematics and its Applications CHAOTIC PROCESSES IN THE GEOLOGICAL SCIENCES is based on the proceedings of a workshop which was an integral part of the 1989- 90 IMA program on "Dynamical Systems and their Applications". The workshop was intended to be an arena for scientific exchanges between earth scientists and mathematical researchers, especially with experts in dynamical systems. We thank Shui-Nee Chow, Martin Golubitsky, Richard McGehee, George R. Sell and David Yuen for organizing the meeting. We especially thank David Yuen for editing the proceedings. We also take this opportunity to thank those agencies whose financial support made the workshop possible: the Army Research Office, the Minnesota Supercomputer institute, the National Science Foundation, and the Office of Naval Research. A vner Friedman Willard Miller, Jr. PREFACE The problems in geological sciences have many nonlinearities from the nature of the complicated physical laws which give rise to strongly chaotic behavior. Foremost and most visible are earthquakes and volcanic eruptions, more subtle are the time dependent variations of the Earth's magnetic fields and motions of the surface plates.
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