Titlebook: Health Web Science; Social Media Data fo Kerstin Denecke Book 2015 Springer International Publishing Switzerland 2015 Data Integration.Heal

Diatribe

Kerstin Denecke in June 2018.. The 11 full papers presented together with 1 invited paper were carefully reviewed and selected from 20 submissions. They are organized in the following topical sections: Phylogenetics,?Sequence Rearrangement and Analysis,?Systems Biology and Other Biological Processes.?

Indolent

Kerstin Deneckeistent with a binary tree. The containment problem is NP-complete, even if the network input is binary. If the input is restricted to reticulation-visible networks, the TCP has been proved to be solvable in quadratic time. In this paper, we show that there is a linear time TCP algorithm for binary reticulation-visible networks.

refine

SLUMP

Kerstin Deneckeuggest to consider a problem of finding a vertex ranking instead of finding a single module. We also propose two algorithms for solving this problem: one that we consider to be optimal but computationally expensive for real-world networks and one that works close to the optimal in practice and is also able to work with big networks.

使顯得不重要

使殘廢

s iteration to be discussed here is the .. It will be seen that multivariate polynomial computations (such as GCD computation and factorization) can be performed much more efficiently (in most cases) by methods based on the Hensel construction than by methods based on the Chinese remainder algorithms of the preceding chapter

Germinate

instulate

Content and Language in Medical Social Mediapeculiarities of that particular data is crucial when developing tools for language processing and data analysis. In this section, we will have a closer look at the characteristics of the language of medical social media data as well as to the content in comparison to clinical narratives.

debacle

Information Extraction from Medical Social Mediahis chapter, we will assess the extraction quality of such tools through a qualitative study. The mapping quality of two mapping or named entity recognition tools originally designed for processing clinical texts is compared when they are applied to medical social media text.

CT-angiography

Social Media for Health Monitoringention. These developments were summarized by the term “Epidemic Intelligence”. In this chapter, a system will be described that exploits medical social media data for automatically detect public health threats.