Top 10 Real-World Bayesian Network Applications

R Quiz

1. Objective

This tutorial is all about Bayesian Network Applications. Here we will discuss the Best 10 real-world applications of Bayesian Network is different domains such as Gene Regulatory Networks, System Biology, Turbo Code, Spam Filter, Image Processing, Semantic Search, Medicine, Biomonitoring, Document Classification, Information Retrieval etc. We will discuss all these Bayesian network Applications in detail in this tutorial.

Top 10 Real-World Bayesian Network Applications

Top 10 Real-World Bayesian Network Applications

2. 10 Real-Life Bayesian Network Applications

Let’s discuss Some most important Bayesian Network Applications one by one:

2.1. Gene Regulatory Networks

GRN is Gene Regulatory Network or Genetic Regulatory Network. GRN is a collection of DNA segments in a cell. It interacts with other substances in the cell and with each other indirectly. Indirectly means through their protein and RNA expression products. Thus, it governs the expression levels of mRNA and proteins. GRNs reproduce the behavior of the system using Mathematical models. In some cases corresponding with experimental observations, it generates predictions.

2.2. Medicine

It is the science or practice of diagnosis. For treatment, and prevention of disease we use medicines. We are using medicine from ancient times. Medicines evolved for a variety of health care practices. To maintain and restore health we use the machine for the prevention and treatment of illness.

2.3. Biomonitoring

It is a term which is generally used to refer the measurement of concentrations of chemicals. It measures the concentration in blood and tissue of humans etc. Hence, It is the measurement of the body burden in analytical chemistry. Biomonitoring involves the use of indicators. These measurements are done in blood and urine often. To determine the levels of many ECCs in humans DTSC scientist is conducting biometric studies.

2.4. Document Classification

It is a problem in library science, computer science, and information science. To assign a document to one or more classes or categories its main task is. We can also do it manually or algorithmically. Manual classification is intellectual classification. The manual classification of documents has been the province of library science. We use the algorithmic classification of documents in information science and computer science.

2.5. Information Retrieval

It is the activity of obtaining information resources. Information Retrieval concerns retrieving the information from databases. It is a continuous process. During the process, we can consider, reconsider and refine our research problem. Metadata or on full-text indexing is the basis of searching. To reduce “information overload” we use automated information retrieval systems.

2.6. Semantic Search

By understanding searcher intent and the contextual meaning of terms, it improves search accuracy. They improve the accuracy in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.

2.7. Image Processing

It is the processing of images by using mathematical operations. We can also use Image Processing to convert images into digital perform. After converting the images we can also apply some operations on it to enhance the image. Image processing is any form of signal processing. In this input can be formed as an image, such as a photograph or video frame. The output of image processing may be either a set of characteristics or parameters related to the image or an image. Hence, in image processing techniques we generally treat the image as a two-dimensional signal. After that, we apply standard signal processing on it.

2.8. Spam Filter

The spam filter is a program. We use a spam filter to detect unsolicited email and unwanted email. Bayesian spam filter calculates the probability of a message being spam. The Bayesian spam filter is more robust than other spam filters. Since its filtering learns from the spam and from good emails.

2.9. Turbo Code

The Turbo codes are a class of high-performance Forward Error correction codes. Thus, Turbo code uses Bayesian Network. Turbo codes are the state of the art of codecs. 3G and 4G mobile telephony standards use these codes. Hence the Bayesian network represents turbo coding and decoding process.

2.10. System Biology

We can also use BN to infer different types of the biological network from Bayesian structure learning. In this, the main output is the qualitative structure of the learned network.
If you found more Bayesian Network Applications, so, please let us know by leaving a comment in a section given below.
See Also-

No Responses

  1. Imad Ahmad says:

    It would have been better, had you explained how Bayesian Network helps in certain fields (which you have listed as Application). Hope you come up with a brief description on the same.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.