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näïve bayes multi-class algorithm scitegic pipeline pilot  (SciTegic Inc)

 
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    SciTegic Inc näïve bayes multi-class algorithm scitegic pipeline pilot
    A Chemical Genetic Matrix (CGM). (A) Data generation and analysis workflow. 4915 unique molecules from 4 different chemical libraries were screened against a panel of 195 S. cerevisiae deletion strains (termed sentinels) to identify compounds that inhibit the growth of specific deletion strains (termed cryptagens). Pairwise combinations of 128 structurally diverse cryptagens from the CGM were screened in a 128×128 cryptagen matrix (CM) to identify synergistic compound pairs. The CGM dataset and chemical structural features were used to build a <t>Naïve</t> <t>Bayes</t> multi-class learner (NBL) to predict compound activity likelihoods for each sentinel strain. A graph-based algorithm was used to integrate chemical-genetic and genetic interactions to predict compound targets, based on either CGM interaction data (SONARG) or NBL likelihood scores (SONARGN). A random forest-based machine learning algorithm was used to enhance synergy prediction using the CM as training data, based on NBL likelihoods with (SONARGNR) or without (SONARNR) genetic interaction constraints. Predicted synergistic combinations were tested in S. cerevisiae, fungal pathogens and human cell lines. (B) GO SLIM categories represented by sentinel deletion strains. Color indicates significance of gene enrichment based on hypergeometric test. Numbers indicate genes in each category. (C) Heatmap of chemical-genetic interactions in the CGM. Compound activities versus sentinel strains are shown for each individual library screened in this study. (D) Compound activities (Zscore < −4) across sentinel strains. 1221 cryptagens that sensitized > 4 and < 2/3 of all deletion strains are indicated. Inset: Median growth inhibition across all sentinel screens for each compound. See also Figures S1, S2.
    Näïve Bayes Multi Class Algorithm Scitegic Pipeline Pilot, supplied by SciTegic 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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    näïve bayes multi-class algorithm scitegic pipeline pilot - by Bioz Stars, 2026-10
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    1) Product Images from "Prediction of compound synergism from chemical-genetic interactions by machine learning"

    Article Title: Prediction of compound synergism from chemical-genetic interactions by machine learning

    Journal: Cell systems

    doi: 10.1016/j.cels.2015.12.003

    A Chemical Genetic Matrix (CGM). (A) Data generation and analysis workflow. 4915 unique molecules from 4 different chemical libraries were screened against a panel of 195 S. cerevisiae deletion strains (termed sentinels) to identify compounds that inhibit the growth of specific deletion strains (termed cryptagens). Pairwise combinations of 128 structurally diverse cryptagens from the CGM were screened in a 128×128 cryptagen matrix (CM) to identify synergistic compound pairs. The CGM dataset and chemical structural features were used to build a Naïve Bayes multi-class learner (NBL) to predict compound activity likelihoods for each sentinel strain. A graph-based algorithm was used to integrate chemical-genetic and genetic interactions to predict compound targets, based on either CGM interaction data (SONARG) or NBL likelihood scores (SONARGN). A random forest-based machine learning algorithm was used to enhance synergy prediction using the CM as training data, based on NBL likelihoods with (SONARGNR) or without (SONARNR) genetic interaction constraints. Predicted synergistic combinations were tested in S. cerevisiae, fungal pathogens and human cell lines. (B) GO SLIM categories represented by sentinel deletion strains. Color indicates significance of gene enrichment based on hypergeometric test. Numbers indicate genes in each category. (C) Heatmap of chemical-genetic interactions in the CGM. Compound activities versus sentinel strains are shown for each individual library screened in this study. (D) Compound activities (Zscore < −4) across sentinel strains. 1221 cryptagens that sensitized > 4 and < 2/3 of all deletion strains are indicated. Inset: Median growth inhibition across all sentinel screens for each compound. See also Figures S1, S2.
    Figure Legend Snippet: A Chemical Genetic Matrix (CGM). (A) Data generation and analysis workflow. 4915 unique molecules from 4 different chemical libraries were screened against a panel of 195 S. cerevisiae deletion strains (termed sentinels) to identify compounds that inhibit the growth of specific deletion strains (termed cryptagens). Pairwise combinations of 128 structurally diverse cryptagens from the CGM were screened in a 128×128 cryptagen matrix (CM) to identify synergistic compound pairs. The CGM dataset and chemical structural features were used to build a Naïve Bayes multi-class learner (NBL) to predict compound activity likelihoods for each sentinel strain. A graph-based algorithm was used to integrate chemical-genetic and genetic interactions to predict compound targets, based on either CGM interaction data (SONARG) or NBL likelihood scores (SONARGN). A random forest-based machine learning algorithm was used to enhance synergy prediction using the CM as training data, based on NBL likelihoods with (SONARGNR) or without (SONARNR) genetic interaction constraints. Predicted synergistic combinations were tested in S. cerevisiae, fungal pathogens and human cell lines. (B) GO SLIM categories represented by sentinel deletion strains. Color indicates significance of gene enrichment based on hypergeometric test. Numbers indicate genes in each category. (C) Heatmap of chemical-genetic interactions in the CGM. Compound activities versus sentinel strains are shown for each individual library screened in this study. (D) Compound activities (Zscore < −4) across sentinel strains. 1221 cryptagens that sensitized > 4 and < 2/3 of all deletion strains are indicated. Inset: Median growth inhibition across all sentinel screens for each compound. See also Figures S1, S2.

    Techniques Used: Activity Assay, Inhibition

    Synergy Prediction Based on Chemical-Genetic and Genetic Interactions. (A) Deletion strains are sensitized to specific cryptagens. (B) Underlying genetic interaction network. (C) SONARG integrates chemical-genetic and genetic interactions to predict chemical synergies. Sentinel strains sensitive to cryptagen c represent first order connections s. Second order connections t are inferred from genetic interactions of sentinel strains and ranked by interactions with sentinel strains in s. Edge weights between target spaces ti and tj are based on genetic interaction counts. See Methods for details. (D) PCA biplot of loadings for 7 SONARG parameters in comparison to Bliss independence values from the CM. Abbreviations: sgi, shared genetic interactions between deletion strains for each compound pair; pval, p-value; hs, high sum on V vertices for x and y and E edges between x and y. (E) Naïve Bayes multi-class likelihoods from the CGM. ECFP4 fingerprints for all compounds and activity probabilities for each feature are calculated for all sentinel strains. The integrated probability for compound activity across all features and classes is represented as a likelihood score. (F) Heatmap of CGM based on NBL likelihoods. (G) PCA biplot for SONARGN parameters. (H) Receiver-operator characteristics (ROC) for the single property Exy (AUC = 0.64) and for synergy scores based on SONARGNR parameters (AUC = 0.87). Inset: Precision-recall plot for SONARGNR model. (I) Distribution of SONARGNR scores for synergistic and non-synergistic pairs based on CM data. See also Figures S3–S6.
    Figure Legend Snippet: Synergy Prediction Based on Chemical-Genetic and Genetic Interactions. (A) Deletion strains are sensitized to specific cryptagens. (B) Underlying genetic interaction network. (C) SONARG integrates chemical-genetic and genetic interactions to predict chemical synergies. Sentinel strains sensitive to cryptagen c represent first order connections s. Second order connections t are inferred from genetic interactions of sentinel strains and ranked by interactions with sentinel strains in s. Edge weights between target spaces ti and tj are based on genetic interaction counts. See Methods for details. (D) PCA biplot of loadings for 7 SONARG parameters in comparison to Bliss independence values from the CM. Abbreviations: sgi, shared genetic interactions between deletion strains for each compound pair; pval, p-value; hs, high sum on V vertices for x and y and E edges between x and y. (E) Naïve Bayes multi-class likelihoods from the CGM. ECFP4 fingerprints for all compounds and activity probabilities for each feature are calculated for all sentinel strains. The integrated probability for compound activity across all features and classes is represented as a likelihood score. (F) Heatmap of CGM based on NBL likelihoods. (G) PCA biplot for SONARGN parameters. (H) Receiver-operator characteristics (ROC) for the single property Exy (AUC = 0.64) and for synergy scores based on SONARGNR parameters (AUC = 0.87). Inset: Precision-recall plot for SONARGNR model. (I) Distribution of SONARGNR scores for synergistic and non-synergistic pairs based on CM data. See also Figures S3–S6.

    Techniques Used: Comparison, Activity Assay

    Random Forest-Based Learner for Synergy Prediction Based on Chemical-Genetic Interactions and Chemical Structural Features. (A) ROC for synergy prediction with SONARNR model. Inset: Precision-recall plot. (B) Scatterplot of Bliss independence values and SONARNR synergy scores. (C) Naïve Bayes classes of top-ranked deletion strains that predict synergistic interactions. Mean decrease in Gini represents the influence of variables in partitioning the data into defined classes. (D) Sentinel strains associated with synergistic interactions predicted by SONARNR. Genes are grouped by biological processes. Edge weights are determined by NBL likelihood of two genes being among the top three sensitive genes for synergistic pairs, corrected by subtraction of weights for the same graph generated from 730 non-synergistic pairs. (E) Corresponding edge weights for genetic interactions between strains for graph in panel D. See also Figures S8, S9.
    Figure Legend Snippet: Random Forest-Based Learner for Synergy Prediction Based on Chemical-Genetic Interactions and Chemical Structural Features. (A) ROC for synergy prediction with SONARNR model. Inset: Precision-recall plot. (B) Scatterplot of Bliss independence values and SONARNR synergy scores. (C) Naïve Bayes classes of top-ranked deletion strains that predict synergistic interactions. Mean decrease in Gini represents the influence of variables in partitioning the data into defined classes. (D) Sentinel strains associated with synergistic interactions predicted by SONARNR. Genes are grouped by biological processes. Edge weights are determined by NBL likelihood of two genes being among the top three sensitive genes for synergistic pairs, corrected by subtraction of weights for the same graph generated from 730 non-synergistic pairs. (E) Corresponding edge weights for genetic interactions between strains for graph in panel D. See also Figures S8, S9.

    Techniques Used: Generated

    Related Articles

    Activity Assay:

    Article Title: Prediction of compound synergism from chemical-genetic interactions by machine learning
    Article Snippet: Structural characteristics of each cryptagen were represented by Extended-Connectivity Fingerprints (ECFP4, see Methods) and combined with CGM data using a Naïve Bayes multi-class algorithm (SciTegic Pipeline Pilot, see Methods and Fig. S5A ) to predict compound activities towards each sentinel strain.

    Inhibition:

    Article Title: Prediction of compound synergism from chemical-genetic interactions by machine learning
    Article Snippet: Structural characteristics of each cryptagen were represented by Extended-Connectivity Fingerprints (ECFP4, see Methods) and combined with CGM data using a Naïve Bayes multi-class algorithm (SciTegic Pipeline Pilot, see Methods and Fig. S5A ) to predict compound activities towards each sentinel strain.

    Comparison:

    Article Title: Prediction of compound synergism from chemical-genetic interactions by machine learning
    Article Snippet: Structural characteristics of each cryptagen were represented by Extended-Connectivity Fingerprints (ECFP4, see Methods) and combined with CGM data using a Naïve Bayes multi-class algorithm (SciTegic Pipeline Pilot, see Methods and Fig. S5A ) to predict compound activities towards each sentinel strain.

    Generated:

    Article Title: Prediction of compound synergism from chemical-genetic interactions by machine learning
    Article Snippet: Structural characteristics of each cryptagen were represented by Extended-Connectivity Fingerprints (ECFP4, see Methods) and combined with CGM data using a Naïve Bayes multi-class algorithm (SciTegic Pipeline Pilot, see Methods and Fig. S5A ) to predict compound activities towards each sentinel strain.



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