feedforward network Search Results


self-encoders + migration learning + deep feedforward neural networks  (Transcell Technology Inc)
90
Transcell Technology Inc self-encoders + migration learning + deep feedforward neural networks
Application of artificial intelligence in basic research on tumor drug resistance
Self Encoders + Migration Learning + Deep Feedforward Neural Networks, supplied by Transcell Technology 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/feedforward+network/self+encoders+++migration+learning+++deep+feedforward+neural+networks/pmc12016295-18-0-8
Average 90 stars, based on 1 article reviews
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multi-layer feedforward neural network  (PetroChina Co Ltd)
90
PetroChina Co Ltd multi-layer feedforward neural network
Application of artificial intelligence in basic research on tumor drug resistance
Multi Layer Feedforward Neural Network, supplied by PetroChina Co Ltd, 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/feedforward+network/multi+layer+feedforward+neural+network/10__3389_slash_feart__2023__1114389-12-3-60
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prediction feedforward network  (Transcell Technology Inc)
90
Transcell Technology Inc prediction feedforward network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Prediction Feedforward Network, supplied by Transcell Technology 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/feedforward+network/prediction+feedforward+network/pmc11378636-39-18-0
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prediction feedforward network - by Bioz Stars, 2026-09
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feedforward neural network  (Verlag GmbH)
90
Verlag GmbH feedforward neural network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Neural Network, supplied by Verlag GmbH, 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/feedforward+network/feedforward+neural+network/10__1039_slash_c9me00071b-1623-0-8
Average 90 stars, based on 1 article reviews
feedforward neural network - by Bioz Stars, 2026-09
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multilayer feedforward neural network  (KNIME GmbH)
90
KNIME GmbH multilayer feedforward neural network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Multilayer Feedforward Neural Network, supplied by KNIME GmbH, 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/feedforward+network/multilayer+feedforward+neural+network/pm33043942-200-9-20
Average 90 stars, based on 1 article reviews
multilayer feedforward neural network - by Bioz Stars, 2026-09
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hybrid feedback feedforward neural-network learning control  (BioMimetic Therapeutics)
90
BioMimetic Therapeutics hybrid feedback feedforward neural-network learning control
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Hybrid Feedback Feedforward Neural Network Learning Control, supplied by BioMimetic Therapeutics, 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/feedforward+network/hybrid+feedback+feedforward+neural+network+learning+control/10__1186_slash_s13662___018___1642___7-207-4-4
Average 90 stars, based on 1 article reviews
hybrid feedback feedforward neural-network learning control - by Bioz Stars, 2026-09
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feedforward artificial neural network annff  (ANNAR Diagnostica)
90
ANNAR Diagnostica feedforward artificial neural network annff
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Artificial Neural Network Annff, supplied by ANNAR Diagnostica, 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/feedforward+network/feedforward+artificial+neural+network+annff/10__1016_slash_j__conengprac__2024__106198-210-0-10
Average 90 stars, based on 1 article reviews
feedforward artificial neural network annff - by Bioz Stars, 2026-09
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feedforward neural network  (Biomed Resource Inc)
90
Biomed Resource Inc feedforward neural network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Neural Network, supplied by Biomed Resource 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/feedforward+network/feedforward+neural+network/10__54692_slash_ijeci__2023__0704163-5571-13-27
Average 90 stars, based on 1 article reviews
feedforward neural network - by Bioz Stars, 2026-09
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feedforward neural network  (Muegge GmbH)
90
Muegge GmbH feedforward neural network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Neural Network, supplied by Muegge GmbH, 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/feedforward+network/feedforward+neural+network/pm20063467-33-1-45
Average 90 stars, based on 1 article reviews
feedforward neural network - by Bioz Stars, 2026-09
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feedforward neural networks (fnn)  (CH Instruments)
90
CH Instruments feedforward neural networks (fnn)
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Neural Networks (Fnn), supplied by CH Instruments, 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/feedforward+network/feedforward+neural+networks++fnn+/10__1162_slash_089976604774201668-24-4-21
Average 90 stars, based on 1 article reviews
feedforward neural networks (fnn) - by Bioz Stars, 2026-09
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feedforward neural network  (Maanshan Tianjun Machinery Manufacturing Co LTD)
90
Maanshan Tianjun Machinery Manufacturing Co LTD feedforward neural network
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Feedforward Neural Network, supplied by Maanshan Tianjun Machinery Manufacturing Co LTD, 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/feedforward+network/feedforward+neural+network/10__1007_slash_s10044___024___01315___7-25-2-10
Average 90 stars, based on 1 article reviews
feedforward neural network - by Bioz Stars, 2026-09
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rprop algorithm for multilayer feedforward networks  (KNIME GmbH)
90
KNIME GmbH rprop algorithm for multilayer feedforward networks
An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction <t>feedforward</t> network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.
Rprop Algorithm For Multilayer Feedforward Networks, supplied by KNIME GmbH, 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/feedforward+network/rprop+algorithm+for+multilayer+feedforward+networks/pm23030379-53-9-0
Average 90 stars, based on 1 article reviews
rprop algorithm for multilayer feedforward networks - by Bioz Stars, 2026-09
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Image Search Results


Application of artificial intelligence in basic research on tumor drug resistance

Journal: Molecular Cancer

Article Title: Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

doi: 10.1186/s12943-025-02321-x

Figure Lengend Snippet: Application of artificial intelligence in basic research on tumor drug resistance

Article Snippet: RNA-seq data from DepMap and TCGA datasets , TransCell (Self-Encoders + Migration Learning + Deep Feedforward Neural Networks) , External validation on proteomic data in CellMinerCDB and RNA-seq data in NCI60 cell line , TransCell improved drug susceptibility prediction performance by more than 50% , [ ] .

Techniques: Biomarker Discovery, Binding Assay, Fluorescence, Kinase Assay, Immunohistochemistry, Quantitative RT-PCR, Staining, Knockdown, CRISPR, Mutagenesis, Knock-Out, Activation Assay, Expressing, Reverse Transcription Polymerase Chain Reaction, Migration, Gene Expression, Plasmid Preparation, Injection, Selection, Histone Deacetylase Assay, Imaging, Cytometry

Available databases on tumor drug resistance

Journal: Molecular Cancer

Article Title: Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

doi: 10.1186/s12943-025-02321-x

Figure Lengend Snippet: Available databases on tumor drug resistance

Article Snippet: RNA-seq data from DepMap and TCGA datasets , TransCell (Self-Encoders + Migration Learning + Deep Feedforward Neural Networks) , External validation on proteomic data in CellMinerCDB and RNA-seq data in NCI60 cell line , TransCell improved drug susceptibility prediction performance by more than 50% , [ ] .

Techniques: Expressing, Mutagenesis, Drug discovery, Biomarker Discovery

An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction feedforward network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.

Journal: Genomics, Proteomics & Bioinformatics

Article Title: TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning

doi: 10.1093/gpbjnl/qzad008

Figure Lengend Snippet: An overview of research design A . The number of RNA-seq profiles available per cell line. The numbers were collected from the ARCHS4 website ( https://maayanlab.cloud/archs4/ ). B . Prediction of measurements in six types based on gene expression data of cancer cell lines. The number of cell lines varies across data types. C . Model evaluation process. Due to the high demand for computation power, we started with a small set of measurements for each type and then scaled up to a larger set. D . Schematic of TransCell. The top 5000 features sharing similar distribution between CCLE and TCGA were first selected, followed by the creation of an autoencoder using TCGA pan-cancer tumor transcriptomes. The parameters of the TCGA encoder were then transferred to the second CCLE autoencoder for weight initializations. Afterward, a two-step pre-trained CCLE enc was extracted and linked to a prediction feedforward network. Parameters were tuned automatically (see Method for details). Note that one model is built for each molecular measurement. LASSO, least absolute shrinkage and selection operator; EN, elastic net; RF, random forest; PCA, principal component analysis; DNN, deep neural network; CCLE, Cancer Cell Line Encyclopedia; TCGA, The Cancer Genome Atlas; CCLE enc , CCLE encoder.

Article Snippet: TransCell is composed of two networks: (1) a two-step pre-trained CCLE encoder (CCLE enc ) and (2) a prediction feedforward network (P) ( ).

Techniques: RNA Sequencing, Gene Expression, Selection