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pca technique  (GraphPad Software Inc)


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    GraphPad Software Inc pca technique
    Pca Technique, supplied by GraphPad Software 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/pca+technique/pca+technique/pmc12110445-119-23-25
    Average 90 stars, based on 1 article reviews
    pca technique - by Bioz Stars, 2026-09
    90/100 stars

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    Construct:

    Article Title: Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms.
    Article Snippet: Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems.. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents.. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate.

    Article Title: Comparing in vitro cytotoxic drug sensitivity in colon and pancreatic cancer using 2D and 3D cell models: Contrasting viability and growth inhibition in clinically relevant dose and repeated drug cycles
    Article Snippet: PCA was performed in GraphPad Prism (Version 9) using 14 variables and selection of the two principal components with the largest eigenvalues (Figure , Data ).

    Article Title: Unfolded protein response and angiogenesis in malignancies
    Article Snippet: The PCA process performed by GraphPad Prism includes standardization of the data such that each variable has a mean of zero and standard deviation of 1, and a variance of 1.

    Article Title: Integration of molecular testing with clinical criteria and histopathology improves diagnostic precision in immune-mediated liver diseases.
    Article Snippet: The PCA was constructed in GraphPad Prism v.10 (GraphPad Software, San Diego, CA, USA).

    Article Title: Transcriptomic Profile of Early Antral Follicles: Predictive Somatic Gene Markers of Oocyte Maturation Outcome
    Article Snippet: First, 12 centrality coefficients supported by CytoHubba ( ) were calculated for each DEG, providing each DEG with a score, and plotted using PCA (from GraphPad 10.1.1 Prism; https://www.graphpad.com/ , accessed on 2 May 2024).

    Article Title: Single cell analysis of Chinese hamster ovary cells during a bioprocess using a novel dynamic imaging system.
    Article Snippet: To better visualize this data, PCA was performed in GraphPad.

    Article Title: Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia.
    Article Snippet: ⁎ Corresponding author.. E-mail address: r.niven@adfa.edu.au (R.K. Niven). http://dx.doi.org/10.1016/j.scitotenv.2022.159945 Received 29 July 2022; Received in revised form 23 O Available online 5 November 2022 0048-9697/Crown Copyright © 2022 Published by E • There is an increasing need to rigorously assess groundwater vulnerability considering the level of intensity of different

    Article Title: Dose imbalance of DYRK1A kinase causes systemic progeroid status in Down syndrome by increasing the un-repaired DNA damage and reducing LaminB1 levels.
    Article Snippet: PCA was applied on directly measured IgG glycan peaks (GP1–GP24) using GraphPad Prism v9.2.0 PCA with standardised scale.

    Gene Expression:

    Article Title: Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms.
    Article Snippet: Incidences of disease, dieback, decline or mortality, some of which induced or enhanced by climate change, threaten the sustainability of forest stands in many ecosystems.. Spatially explicit prediction of disease onset remains challenging, however, due to the involvement of several causative agents.. In this paper, we developed a generic framework based on machine-learning algorithms and spatial analyses for landscape-level prediction of oak disease outbreaks caused by the charcoal fungus Biscogniauxia mediterranea in a mixed-oak forest of Mediterranean climate.

    Article Title: Comparing in vitro cytotoxic drug sensitivity in colon and pancreatic cancer using 2D and 3D cell models: Contrasting viability and growth inhibition in clinically relevant dose and repeated drug cycles
    Article Snippet: PCA was performed in GraphPad Prism (Version 9) using 14 variables and selection of the two principal components with the largest eigenvalues (Figure , Data ).

    Article Title: Unfolded protein response and angiogenesis in malignancies
    Article Snippet: The PCA process performed by GraphPad Prism includes standardization of the data such that each variable has a mean of zero and standard deviation of 1, and a variance of 1.

    Article Title: Integration of molecular testing with clinical criteria and histopathology improves diagnostic precision in immune-mediated liver diseases.
    Article Snippet: The PCA was constructed in GraphPad Prism v.10 (GraphPad Software, San Diego, CA, USA).

    Article Title: Transcriptomic Profile of Early Antral Follicles: Predictive Somatic Gene Markers of Oocyte Maturation Outcome
    Article Snippet: First, 12 centrality coefficients supported by CytoHubba ( ) were calculated for each DEG, providing each DEG with a score, and plotted using PCA (from GraphPad 10.1.1 Prism; https://www.graphpad.com/ , accessed on 2 May 2024).

    Article Title: Single cell analysis of Chinese hamster ovary cells during a bioprocess using a novel dynamic imaging system.
    Article Snippet: To better visualize this data, PCA was performed in GraphPad.

    Article Title: Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia.
    Article Snippet: ⁎ Corresponding author.. E-mail address: r.niven@adfa.edu.au (R.K. Niven). http://dx.doi.org/10.1016/j.scitotenv.2022.159945 Received 29 July 2022; Received in revised form 23 O Available online 5 November 2022 0048-9697/Crown Copyright © 2022 Published by E • There is an increasing need to rigorously assess groundwater vulnerability considering the level of intensity of different

    Article Title: Dose imbalance of DYRK1A kinase causes systemic progeroid status in Down syndrome by increasing the un-repaired DNA damage and reducing LaminB1 levels.
    Article Snippet: PCA was applied on directly measured IgG glycan peaks (GP1–GP24) using GraphPad Prism v9.2.0 PCA with standardised scale.



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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and <t>PCA</t> process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and <t>the</t> <t>amygdala</t> (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.
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    Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Journal: Human Brain Mapping

    Article Title: Dynamic Functional Connectivity Between Amygdala and Cognitive Control Network Predicts Delay Discounting in Older Adolescents

    doi: 10.1002/hbm.70270

    Figure Lengend Snippet: Dynamic functional connectivity and PCA process. (A) Spheres inside the brain show regions of the cognitive control network (blue) and the amygdala (orange). (B) Signals are extracted from all regions, and sliding time windows are created. The figure shows an example of three CC regions with one amygdala region. The orange A represents a value from the amygdala, while the blue numbers 1–3 represent regions from the CC network. (C) Within each window, correlations are computed between each amygdala and all CC regions. These values are entered into a Principal Component Analysis (PCA). (D) Results from each PCA provide scores for participants (right side, in red) and coefficient values for the regions across windows in the dFC (left side in blue). The different shades of colors represent different values. The decreasing saturation in color across the Principal Components (PCs) indicates the amount of variance in dFC they account for, with the first PC having the highest. W = window. dFC = dynamic functional connectivity.

    Article Snippet: A PCA was performed for each amygdala's dFC in MATLAB (2018b).

    Techniques: Functional Assay, Control