Dissertation/ Thesis
語意生物醫學智能暨系統生物學模型比較
| Τίτλος: | 語意生物醫學智能暨系統生物學模型比較 |
|---|---|
| Συγγραφείς: | Wang, Chao-Neng |
| Συνεισφορές: | 生物與醫學資訊學系 |
| Στοιχεία εκδότη: | 亞洲大學 |
| Έτος έκδοσης: | 2015 |
| Συλλογή: | ASIA University, Taiwan: Instiutional Repository (ASIAIR) / 歡迎光臨亞洲大學全球資訊網 |
| Θεματικοί όροι: | Semantic Computing, Systems Biology, Structured Natural Language, Biological and medical intelligence |
| Περιγραφή: | Biological and medical intelligence (BMI) has been studied in solos, lacking a systematic methodology. Biological intelligence involves the development and application of novel informatics techniques in the biological (especially genomic) sciences. It is a young, successful discipline, which already has its own professional societies, meetings and scientific journals focused on a clear research agenda, having contributed to the successes of the human and other genome projects. On the other hand, medical intelligence designates a type of application software designed to retrieve, analyze, transform and report data for medical intelligence. It is a more established field that has pioneered the development and introduction of informatics methods in clinical medicine and biomedical research, but it has recently found itself increasingly challenged by the emergence of bioinformatics. Medical intelligence tools include On-Line Analytical Processing (OLAP) and data mining tools that both allow an analyst to discover novel knowledge about the data stored in a medical data set. This dissertation first introduces how semantic computing can enhance biological and medical intelligence. Specifically, we show how Structured Natural Language (SNL) can express many problems in BMI with a finite number of sentence patterns, and how biological analysis tools, systems biology, OLAP, data mining tools and statistical analysis tools may be linked to solve problems with a uniform interface. A very important emerging subject in biological intelligence is computational systems biology that aims to inference gene regulatory networks from time course data. In recent years, technological advances have driven the development of systems biology in experimental methods that generate in vivo time course data to characterize regulatory interactions. However, different mathematical models have been explored to solve this challenging task, but difficulty remains to prove one model is superior to the others. To better understand computational ... |
| Τύπος εγγράφου: | thesis |
| Περιγραφή αρχείου: | 121 bytes; text/html |
| Γλώσσα: | English |
| Relation: | http://asiair.asia.edu.tw/ir/handle/310904400/89824; http://asiair.asia.edu.tw/ir/bitstream/310904400/89824/2/index.html |
| Διαθεσιμότητα: | http://asiair.asia.edu.tw/ir/handle/310904400/89824 http://asiair.asia.edu.tw/ir/bitstream/310904400/89824/2/index.html |
| Αριθμός Καταχώρησης: | edsbas.90628A7D |
| Βάση Δεδομένων: | BASE |
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