Novel Context Rich LoCo and GloCo Features with Local and Global Shape Constraints for Segmentation of 3D Echocardiograms with Random Forests

2013 ◽  
pp. 59-69 ◽  
Author(s):  
Kiryl Chykeyuk ◽  
Mohammad Yaqub ◽  
J. Alison Noble
Keyword(s):  
Random Forests ◽  
Global Shape ◽  
Shape Constraints

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

A Diverse Assimilation of Sequence and Structure Dependent Features for Amyloid Plaque Prediction Using Random Forests

2013 ◽  
Vol 10 (1) ◽  
pp. 38-44
Author(s):  
Smitha Sunil Nair ◽  
N. V. Reddy ◽  
K. Hareesha ◽  
S. Balaji
Keyword(s):  
Random Forests ◽  
Amyloid Plaque

scholarly journals Prediction of unconventional protein secretion by exosomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alvaro Ras-Carmona ◽  
Marta Gomez-Perosanz ◽  
Pedro A. Reche
Keyword(s):  
Protein Secretion ◽  
Random Forests ◽  
Signal Peptide ◽  
Area Under The Curve ◽  
Dipeptide Composition ◽  
Web Based ◽  
Independent Dataset ◽  
Link Type ◽  
Tenfold Cross Validation ◽  
Dependent Pathway

Abstract Motivation In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. Results Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. Conclusion ExoPred is available for free public use at http://imath.med.ucm.es/exopred/. Datasets are available at http://imath.med.ucm.es/exopred/datasets/.

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