Review



t-sne algorithm  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
  • 90

    Structured Review

    MathWorks Inc t-sne algorithm
    T Sne Algorithm, 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
    https://www.bioz.com/product/t-sne+algorithm/pmc09339663__mmc1-464-26-45
    Average 90 stars, based on 1 article reviews
    t-sne algorithm - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    Construct:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Functional Assay:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Sampling:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Western Blot:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Immunoprecipitation:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Sequencing:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    RNA Sequencing:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Knockdown:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Binding Assay:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Expressing:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    shRNA:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Activation Assay:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.

    Transformation Assay:

    Article Title: A large-scale binding and functional map of human RNA-binding proteins
    Article Snippet: To cluster data sets, dimensionality reduction was performed on element-relative information from the combination of both replicates using the t -SNE algorithm in MATLAB (2018a) with correlation distance, ‘exact’ algorithm, and perplexity = 10.

    Article Title: Pressure and stiffness sensing together regulate vascular smooth muscle cell phenotype switching.
    Article Snippet: All measurements were combined, and dimensionality reduction was performed in MATLAB using the t-SNE algorithm.

    Article Title: Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis
    Article Snippet: The t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title: Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images
    Article Snippet: The activation space is transformed into a 3-dimensional representation space by t-SNE algorithm in MATLAB.

    Article Title: Connecting concepts in the brain by mapping cortical representations of semantic relations
    Article Snippet: We use tdistributed Stochastic Neighbor Embedding (t-SNE) 1 algorithm in Matlab to visualize the distribution of all words in the training dataset by reducing the embedding dimension while keeping the relevant pairwise cosine similarity.

    Article Title: Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis
    Article Snippet: Moreover, the t-SNE algorithm was then used in MATLAB software to make the data presentable before and after the batch effect correction [28].

    Article Title:
    Article Snippet: To visualize the random walk in 3 dimensions, the sequence of time- dependent phase-locking matrices, or iPL stream, served as input features into a t- Stochastic Neighborhood Embedding (t-SNE) algorithm (algorithm = exact, distance = Cosine, Perplexity = 50, LearnRate =2000, exaggeration = 4) in MATLAB (MathWorks R2020b) following (Battaglia et al., 2020; Maaten and Hinton, 2008).

    Article Title: Abnormal higher-order network interactions in Parkinson’s disease visual hallucinations
    Article Snippet: The t-SNE algorithm in MATLAB was used to construct 3-dimensional embeddings of each individual functional connectivity matrix.



    Similar Products

    90
    MathWorks Inc t-sne algorithm
    T Sne Algorithm, 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
    https://www.bioz.com/product/t-sne+algorithm/pmc09339663__mmc1-464-26-45
    Average 90 stars, based on 1 article reviews
    t-sne algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    90
    RStudio t-stochastic neighbor embedding (t-sne) algorithm
    T Stochastic Neighbor Embedding (T Sne) Algorithm, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+algorithm/rtsne+package/pmc12217839-270-3-14
    Average 90 stars, based on 1 article reviews
    t-stochastic neighbor embedding (t-sne) algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    90
    BrainScope dual t-sne algorithm
    Dual T Sne Algorithm, supplied by BrainScope, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+algorithm/dual+t+sne+algorithm/pm39838379-123-4-0
    Average 90 stars, based on 1 article reviews
    dual t-sne algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    90
    Broad Institute Inc t-sne algorithm
    T Sne Algorithm, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+algorithm/t+sne+algorithm/ppr0844105-285-18-10
    Average 90 stars, based on 1 article reviews
    t-sne algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    90
    RStudio t-sne algorithm
    T Sne Algorithm, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+algorithm/rtsne+package/10__1029_slash_2023gc011324-250-8-15
    Average 90 stars, based on 1 article reviews
    t-sne algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    86
    Danaher Inc stochastic linear embedding t sne algorithm

    Stochastic Linear Embedding T Sne Algorithm, supplied by Danaher Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+algorithm/pmc10319581-120-42-49
    Average 86 stars, based on 1 article reviews
    stochastic linear embedding t sne algorithm - by Bioz Stars, 2026-09
    86/100 stars
      Buy from Supplier

    90
    MathWorks Inc t-sne using the jaccard distance algorithm

    T Sne Using The Jaccard Distance Algorithm, 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
    https://www.bioz.com/product/t-sne+algorithm/pm36734268-113-23-48
    Average 90 stars, based on 1 article reviews
    t-sne using the jaccard distance algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    90
    MathWorks Inc t-distributed stochastic neighbor embedding (t-sne) using the jaccard distance algorithm
    Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and <t>Jaccard</t> index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.
    T Distributed Stochastic Neighbor Embedding (T Sne) Using The Jaccard Distance Algorithm, 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
    https://www.bioz.com/product/t-sne+algorithm/pmc10233288-104-19-38
    Average 90 stars, based on 1 article reviews
    t-distributed stochastic neighbor embedding (t-sne) using the jaccard distance algorithm - by Bioz Stars, 2026-09
    90/100 stars
      Buy from Supplier

    Image Search Results


    Journal: Frontiers in Molecular Neuroscience

    Article Title: Innate immune activation and aberrant function in the R6/2 mouse model and Huntington’s disease iPSC-derived microglia

    doi: 10.3389/fnmol.2023.1191324

    Figure Lengend Snippet:

    Article Snippet: To visualize all live immune cells or all iMGL in a single two-dimensional map, we applied an unsupervised high-dimensional data analysis on concatenated fcs files pooling cells equally and randomly sampled from all mice or samples in each group, using the t-distributed stochastic linear embedding (t-SNE) algorithm available on Cytobank (viSNE algorithm).

    Techniques: Derivative Assay, Mass Cytometry, RNA Sequencing Assay, Transgenic Assay

    Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and Jaccard index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.

    Journal: Cerebral Cortex (New York, NY)

    Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

    doi: 10.1093/cercor/bhac523

    Figure Lengend Snippet: Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and Jaccard index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: Imaging

    Spatial ICA of wide-field Ca 2+ imaging data produces spatially independent brain regions. A) Schematic showing the ICA workflow of concatenating data for each animal chronologically across days and trials (days are signified by different colored borders; trials are signified by overlapping images) and sending the combined dataset through the JADE ICA algorithm. B) Example template map (ground-truth to which all other ICA solutions are compared) of spatial ICs produced from running ICA on one mouse’s combined dataset (each different colored region is a single IC; scale bar: 1 mm). White lines denote major regions of the Allen Common Coordinate Framework (CCF; see ). C) Example matrix of Jaccard indices comparing the template map to itself (low off-diagonal Jaccard indices indicate good spatial separation; zero values are shown as white indicating no IC overlap). D) Frequency histograms showing the distribution of off-diagonal Jaccard indices (nonself matches) when comparing the template map to itself for each animal (bin-widths = 0.05).

    Journal: Cerebral Cortex (New York, NY)

    Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

    doi: 10.1093/cercor/bhac523

    Figure Lengend Snippet: Spatial ICA of wide-field Ca 2+ imaging data produces spatially independent brain regions. A) Schematic showing the ICA workflow of concatenating data for each animal chronologically across days and trials (days are signified by different colored borders; trials are signified by overlapping images) and sending the combined dataset through the JADE ICA algorithm. B) Example template map (ground-truth to which all other ICA solutions are compared) of spatial ICs produced from running ICA on one mouse’s combined dataset (each different colored region is a single IC; scale bar: 1 mm). White lines denote major regions of the Allen Common Coordinate Framework (CCF; see ). C) Example matrix of Jaccard indices comparing the template map to itself (low off-diagonal Jaccard indices indicate good spatial separation; zero values are shown as white indicating no IC overlap). D) Frequency histograms showing the distribution of off-diagonal Jaccard indices (nonself matches) when comparing the template map to itself for each animal (bin-widths = 0.05).

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: Imaging, Produced

    Cortex-wide maps cover similar areas across timescales. A) Brain maps from all six experimental subjects showing cumulative cortical coverage of template matching ICs across all time-windows within each of the four timescales examined (color scale shows the number of timescales where an area of cortex was covered by a spatial IC). Scale bar: 1 mm. B) Jaccard index matrices for each of the six experimental subjects showing a high degree of overlapping cortical coverage between timescales (off-diagonal comparisons).

    Journal: Cerebral Cortex (New York, NY)

    Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

    doi: 10.1093/cercor/bhac523

    Figure Lengend Snippet: Cortex-wide maps cover similar areas across timescales. A) Brain maps from all six experimental subjects showing cumulative cortical coverage of template matching ICs across all time-windows within each of the four timescales examined (color scale shows the number of timescales where an area of cortex was covered by a spatial IC). Scale bar: 1 mm. B) Jaccard index matrices for each of the six experimental subjects showing a high degree of overlapping cortical coverage between timescales (off-diagonal comparisons).

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: