machine classifier matlab bioinformatics toolbox code Search Results


fitcsvm  (MathWorks Inc)
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MathWorks Inc fitcsvm
Fitcsvm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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machine learning package  (MathWorks Inc)
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MathWorks Inc machine learning package
Machine Learning Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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linear svm classifier  (MathWorks Inc)
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MathWorks Inc linear svm classifier
Linear Svm Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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classification learner r2018b  (MathWorks Inc)
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MathWorks Inc classification learner r2018b
Classification Learner R2018b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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non-linear classifier nearest-neighbor model  (MathWorks Inc)
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MathWorks Inc non-linear classifier nearest-neighbor model
Non Linear Classifier Nearest Neighbor Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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multilayer artificial neural network classifiers  (MathWorks Inc)
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MathWorks Inc multilayer artificial neural network classifiers
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Multilayer Artificial Neural Network Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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learning algorithm (svm) implemented in the machine learning matlab toolbox  (MathWorks Inc)
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MathWorks Inc learning algorithm (svm) implemented in the machine learning matlab toolbox
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Learning Algorithm (Svm) Implemented In The Machine Learning Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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learning algorithm (svm) implemented in the machine learning matlab toolbox - by Bioz Stars, 2026-04
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support vector machine svm classifier  (MathWorks Inc)
96
MathWorks Inc support vector machine svm classifier
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Support Vector Machine Svm Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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linear svm classifiers  (MathWorks Inc)
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MathWorks Inc linear svm classifiers
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Linear Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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statistics and machine learning toolbox  (MathWorks Inc)
90
MathWorks Inc statistics and machine learning toolbox
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Statistics And Machine Learning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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statistics and machine learning toolbox - by Bioz Stars, 2026-04
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classification learner machine learning tool  (MathWorks Inc)
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MathWorks Inc classification learner machine learning tool
(a) Comparison of mean accuracy with standard deviations for the different <t>machine</t> <t>learning</t> <t>classification</t> methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
Classification Learner Machine Learning Tool, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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bioinformatics toolbox  (MathWorks Inc)
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MathWorks Inc bioinformatics toolbox
(a) Comparison of mean accuracy with standard deviations for the different <t>machine</t> <t>learning</t> <t>classification</t> methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
Bioinformatics Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

(a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.

Journal: Journal of Electrical Bioimpedance

Article Title: Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning

doi: 10.2478/joeb-2021-0005

Figure Lengend Snippet: (a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.

Article Snippet: The validity of utilizing impedance for tissue differentiation was evaluated using the MATLAB classification learner machine learning tool.

Techniques: Comparison, Generated, Biomarker Discovery, Plasmid Preparation