Efficient Examination of Soil Bacteria Using Probabilistic Graphical Models
| Autor: | John Stavrinides, Jhonatan de S. Oliveira, André E. dos Santos, Cory J. Butz |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Soil bacteria business.industry Computer science Deep learning 030106 microbiology Posterior probability Bayesian network Inference Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology Order (business) Graphical model Artificial intelligence business Time complexity computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319920573 IEA/AIE |
| DOI: | 10.1007/978-3-319-92058-0_30 |
| Popis: | This paper describes a novel approach to study bacterial relationships in soil datasets using probabilistic graphical models. We demonstrate how to access and reformat publicly available datasets in order to apply machine learning techniques. We first learn a Bayesian network in order to read independencies in linear time between bacterial community characteristics. These independencies are useful in understanding the semantic relationships between bacteria within communities. Next, we learn a Sum-Product network in order to perform inference in linear time. Here, inference can be conducted to answer traditional queries, involving posterior probabilities, or MPE queries, requesting the most likely values of the non-evidence variables given evidence. Our results extend the literature by showing that known relationships between soil bacteria holding in one or a few datasets in fact hold across at least 3500 diverse datasets. This study paves the way for future large-scale studies of agricultural, health, and environmental applications, for which data are publicly available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|