| Chapter title |
Machine Learning Methods in Computational Toxicology
|
|---|---|
| Chapter number | 5 |
| Book title |
Computational Toxicology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7899-1_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7898-4, 978-1-4939-7899-1
|
| Authors |
Igor I. Baskin, Baskin, Igor I. |
| Abstract |
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen's self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Nigeria | 1 | 10% |
| Canada | 1 | 10% |
| United States | 1 | 10% |
| United Kingdom | 1 | 10% |
| Russia | 1 | 10% |
| Unknown | 5 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 4 | 40% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 59 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 20% |
| Student > Bachelor | 6 | 10% |
| Student > Master | 4 | 7% |
| Researcher | 4 | 7% |
| Student > Doctoral Student | 3 | 5% |
| Other | 5 | 8% |
| Unknown | 25 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 10 | 17% |
| Computer Science | 7 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Agricultural and Biological Sciences | 2 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Other | 9 | 15% |
| Unknown | 26 | 44% |