TOOL BOX
Biomedical Data Science is an interdisciplinary field that combines principles and methods
from computer science, statistics,
and biology to analyze and interpret large
and complex biomedical datasets.
It involves the use of computational and analytical tools to extract insights
from vast amounts of data generated by various biomedical technologies
such as genomics, proteomics, and imaging.
Understanding the genetic basis of diseases:
Biomedical Data Science can help identify the genetic variations associated with different diseases,
which can inform the development of new therapies and drugs.
Biomedical Data Science can help identify the genetic variations associated with different diseases,
which can inform the development of new therapies and drugs.
Personalized medicine:
Biomedical Data Science can help identify the genetic and environmental factors
that contribute to individual variations in disease risk, response to treatments,
and drug metabolism, which can inform personalized treatment plans.
Biomedical Data Science can help identify the genetic and environmental factors
that contribute to individual variations in disease risk, response to treatments,
and drug metabolism, which can inform personalized treatment plans.
Disease diagnosis
& prognosis
Biomedical Data Science can help develop & prognosis
diagnostic tools and predict disease outcomes
based on various clinical and molecular factors.
Drug discovery
Biomedical Data Science can help identify drug targets, evaluate drug efficacy, and predict adverse
drug reactions.
Public health
Biomedical Data Science can help identify disease patterns, track disease outbreaks,
and inform public health policies
Some of the key methods and techniques
used in Biomedical Data Science include machine learning, statistical modeling, data visualization, and network analysis.
Biomedical Data Science is a rapidly growing field with numerous opportunities for innovation and discovery,
and has the potential to transform the way we approach biomedical research and healthcare.
used in Biomedical Data Science include machine learning, statistical modeling, data visualization, and network analysis.
Biomedical Data Science is a rapidly growing field with numerous opportunities for innovation and discovery,
and has the potential to transform the way we approach biomedical research and healthcare.
Prof. Yen-Wei Chu / 朱彥煒老師
自然計算與生物資訊實驗室
Natural Computing & Bioinformatics Laboratory
- Office:04-22840338#7041
- Lab:04-22840338#7061
- E-mail: ywchu@nchu.edu.tw
- Address:
Institute of Genomics and Bioinformatics,
National Chung Hsing University
中興大學基因體暨生物資訊學研究所
Dr. Kuei-Yang Hsiao / 蕭貴陽 老師
The Lab of X-omics
- Phone: 04-22840468 #226
- E-mail: ky.hsiao@nchu.edu.tw
- Address:
Institute of Biochemistry,
National Chung Hsing University
中興大學生物化學研究所