Bootstrap Aggregating of Alternating Decision Trees to Detect Sets of <scp>SNP</scp>s That Associate With Disease

Guy, Richard T.; Santago, Peter; Langefeld, Carl D.

doi:10.1002/gepi.21608

Cited by 21 publications

(10 citation statements)

References 16 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recent development of the machine learning and GIS has introduced many new machine learning techniques that have been recognized as having better overall performance (Witten et al, 2011). Some state-of-the art advanced machine learning methods such as the KLR and ADT have been used in other fields with high accuracy (Cheng et al, 2010;Guy et al, 2012;Liu et al, 2005;, however, the exploration of these methods for landslide susceptibility mapping has seldom been carried out. We addressed this issue in this paper with the investigation and comparison of the KLR and ADT methods for landslide susceptibility modeling.…”

Section: Discussionmentioning

confidence: 99%

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Hong

Pradhan

et al. 2015

CATENA

386

147

View full text Add to dashboard Cite

Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, landuse, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of-fits are 92.5% (the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%, 84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for landuse planning and decision making in landslide prone areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Hong

Pradhan

et al. 2015

CATENA

386

147

View full text Add to dashboard Cite

show abstract

“…59 Decision trees: outputs are learned from inputs via a series of yes/no questions that successively divide the predictor space into discrete piece. 175 Random forest: a simple ensemble method that grows a large number of decision trees, each of which see only a subset of the data, and learns output from input by combining the predictions. 79 k nearest neighbours: learns output from input by comparing the identity of each data point to its (k) nearest neighbours.…”

Section: Box 1 Popular Supervised Machine Learning Methodsmentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

View full text Add to dashboard Cite

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

show abstract

“…Instead, they use machine learning–based methods to simultaneously investigate multi-SNP associations and predictions using many SNPs that do not individually reach statistical significance. In addition, these approaches allow for correlations or interactions among significant SNPs, something not often accounted for in single-SNP association tests due to statistical power limitations 44,45 . Several machine learning-based methods have been proposed to design predictive multi-SNP models for biological traits, and these can be applied to radiation response.…”

Section: Novel Machine Learning Approaches To Building Multi-snp Predmentioning

confidence: 99%

The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism−Based Models: A Step Toward Prevention

Kerns

Kundu

et al. 2015

Seminars in Radiation Oncology

View full text Add to dashboard Cite

Radiotherapy is a mainstay of cancer treatment, used in either a curative or palliative manner to treat approximately 50% of cancer patients. Normal tissue toxicity limits the doses used in standard radiation therapy protocols and impedes improvements in radiotherapy efficacy. Damage to surrounding normal tissues can produce reactions ranging from bothersome symptoms that negatively affect quality of life to severe life-threatening complications. Improved ways of predicting, prior to treatment, the risk for development of normal tissue toxicity may allow for more personalized treatment and reduce the incidence and severity of late effects. There is increasing recognition that the cause of normal tissue toxicity is multifactorial and includes genetic factors in addition to radiation dose and volume of exposure, underlying co-morbidities, age, concomitant chemotherapy or hormonal therapy and use of other medications. An understanding of the specific genetic risk factors for normal tissue response to radiation has the potential to enhance our ability to predict adverse outcomes at the treatment planning stage. Therefore, the field of radiogenomics has focused upon the identification of genetic variants associated with normal tissue toxicity resulting from radiotherapy. Innovative analytic methods are being applied to the discovery of risk variants and development of integrative predictive models that build on traditional normal tissue complication probability models by incorporating genetic information. Results from initial studies provide promising evidence that genetic-based risk models could play an important role in the implementation of precision medicine for radiation oncology through enhancing the ability to predict normal tissue reactions and thereby improve cancer treatment.

show abstract

Bootstrap Aggregating of Alternating Decision Trees to Detect Sets of SNPs That Associate With Disease

Cited by 21 publications

References 16 publications

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

The Prediction of Radiotherapy Toxicity Using Single Nucleotide Polymorphism−Based Models: A Step Toward Prevention

Contact Info

Product

Resources

About