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DeepProfile: Deep learning of cancer molecular profiles for precision medicine

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1038/nature11003openalex-snapshot

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

inferredcites →https://doi.org/10.1038/nature11005openalex-snapshot

Systematic identification of genomic markers of drug sensitivity in cancer cells

inferredcites →https://doi.org/10.1038/nature11412openalex-snapshot

Comprehensive molecular portraits of human breast tumours

inferredcites →https://doi.org/10.1038/nbt.2877openalex-snapshot

A community effort to assess and improve drug sensitivity prediction algorithms

inferredcites →https://doi.org/10.1038/s41467-017-02465-5openalex-snapshot

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

inferredcites →https://doi.org/10.1038/srep31619openalex-snapshot

Multitask learning improves prediction of cancer drug sensitivity

inferredcites →https://doi.org/10.1073/pnas.0506580102openalex-snapshot

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

inferredcites →https://doi.org/10.1093/biostatistics/kxj037openalex-snapshot

Adjusting batch effects in microarray expression data using empirical Bayes methods

inferredcites →https://doi.org/10.1093/nar/30.1.207openalex-snapshot

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

inferredcites →https://doi.org/10.1142/9789813235533_0008openalex-snapshot

Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

inferredcites →https://doi.org/10.1142/9789814583220_0007openalex-snapshot

SYSTEMATIC ASSESSMENT OF ANALYTICAL METHODS FOR DRUG SENSITIVITY PREDICTION FROM CANCER CELL LINE DATA

inferredcites →https://doi.org/10.1142/9789814644730_0014openalex-snapshot

UNSUPERVISED FEATURE CONSTRUCTION AND KNOWLEDGE EXTRACTION FROM GENOME-WIDE ASSAYS OF BREAST CANCER WITH DENOISING AUTOENCODERS

inferredcites →https://doi.org/10.1186/1471-2164-15-s9-s2openalex-snapshot

TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

inferredcites →https://doi.org/10.1186/s13073-016-0319-7openalex-snapshot

Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

inferredcites →https://doi.org/10.17615/hvp3-wg08openalex-snapshot

Integrated genomic analyses of ovarian carcinoma

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare Applications and Use-Cases

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The Role of Machine Learning in Pharmaceutical Biotechnology

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Deep learning in drug discovery: an integrative review and future challenges

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Deep learning explains the biology of branched glycans from single-cell sequencing data

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Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

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Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models

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Machine learning in rare disease

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Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data

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Machine learning approaches to drug response prediction: challenges and recent progress

inferred← citeshttps://doi.org/10.1038/s41698-021-00148-5openalex-snapshot

The Aurora kinase/β-catenin axis contributes to dexamethasone resistance in leukemia

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Variational autoencoders learn transferrable representations of metabolomics data

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An interpretable deep learning framework for genome-informed precision oncology

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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

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Adversarial deconfounding autoencoder for learning robust gene expression embeddings

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Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

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Variational autoencoders learn universal latent representations of metabolomics data

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pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules

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Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

inferred← citeshttps://doi.org/10.1101/2022.05.03.490535openalex-snapshot

Principled feature attribution for unsupervised gene expression analysis

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Infer related genes from large scale gene expression dataset with embedding

inferred← citeshttps://doi.org/10.1101/573782openalex-snapshot

Sequential compression of gene expression across dimensionalities and methods reveals no single best method or dimensionality

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Deep learning of representations for transcriptomics-based phenotype prediction

inferred← citeshttps://doi.org/10.1109/tase.2024.3515839openalex-snapshot

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review

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Integrating Molecular Graph Data of Drugs and Multiple -Omic Data of Cell Lines for Drug Response Prediction

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Biological interpretation of deep neural network for phenotype prediction based on gene expression

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Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

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Predicting chemotherapy response using a variational autoencoder approach

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Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

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PAUSE: principled feature attribution for unsupervised gene expression analysis

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Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data

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Enhancing scientific discoveries in molecular biology with deep generative models

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The impact of deep learning and omics data in transforming precision therapy for brain cancer

inferred← citeshttps://doi.org/10.3389/fgene.2018.00682openalex-snapshot

Embedding of Genes Using Cancer Gene Expression Data: Biological Relevance and Potential Application on Biomarker Discovery

inferred← citeshttps://doi.org/10.3390/ijms221910891openalex-snapshot

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

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Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

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Artificial Intelligence (AI)-Powered RNA Sequence Analysis: Algorithms and Applications

inferred← citeshttps://doi.org/10.5772/intechopen.84148openalex-snapshot

Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research

inferred← citeshttps://doi.org/10.6084/m9.figshare.16664380openalex-snapshot

Additional file 1 of Predicting chemotherapy response using a variational autoencoder approach

inferred← citeshttps://doi.org/10.7554/elife.95273openalex-snapshot