Quantifying critical states of complex diseases using single-sample dynamic network biomarkers
نویسندگان
چکیده
منابع مشابه
Quantifying critical states of complex diseases using single-sample dynamic network biomarkers
Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DN...
متن کاملassessment of the efficiency of s.p.g.c refineries using network dea
data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...
Identifying critical transitions of complex diseases based on a single sample
MOTIVATION Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the d...
متن کاملQuantifying Semantics using Complex Network Analysis
Though it is generally accepted that language models do not capture all aspects of real language, no adequate measures to quantify their shortcomings have been proposed until now. We will use n-gram models as workhorses to demonstrate that the differences between natural and generated language are indeed quantifiable. More specifically, for two algorithmic approaches, we demonstrate that each o...
متن کاملUnderstanding migraine using dynamic network biomarkers.
BACKGROUND Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding complex interactions of network phenomena, in general, and interactions within the migraine-generator network, in particular. PURPOSE In this study, two recent modeling studies on migraine are set in the context of premonitory symptoms that...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2017
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1005633