The most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development are discussed and major hurdles in the field are highlighted, such as the required data characteristics for applying ML.
Molecular de-novo design through deep reinforcement learning
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The rise of deep learning in drug discovery.
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Planning chemical syntheses with deep neural networks and symbolic AI
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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.
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Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships
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enchmark for molecular machine learning †
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Dropout: a simple way to prevent neural networks from overfitting
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VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder
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Deep Learning-A Technology With the Potential to Transform Health Care.
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
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Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer
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Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission
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Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification
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Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers
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Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
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Biomarkers as drug development tools: discovery, validation, qualification and use
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Significance and implications of FDA approval of pembrolizumab for biomarker-defined disease
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Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer
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Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.
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A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue
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Transcriptional regulatory networks underlying gene expression changes in Huntington's disease
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Deep learning based tissue analysis predicts outcome in colorectal cancer
Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets
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Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology
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Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
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MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
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Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data
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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
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Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders
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An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival
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Is Multitask Deep Learning Practical for Pharma?
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Defining a Cancer Dependency Map
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In silico prediction of novel therapeutic targets using gene–disease association data
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A multi‐scale convolutional neural network for phenotyping high‐content cellular images
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Abstract 662: The differential association of PD-1, PD-L1, and CD8+ cells with response to pembrolizumab and presence of Merkel cell polyomavirus (MCPyV) in patients with Merkel cell carcinoma (MCC)
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IntegratedMRF: random forest‐based framework for integrating prediction from different data types
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Bridging the translational innovation gap through good biomarker practice
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A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine
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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
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A machine-learning heuristic to improve gene score prediction of polygenic traits
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Maximum entropy methods for extracting the learned features of deep neural networks
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Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network
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Integrative deep models for alternative splicing
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An analysis of disease-gene relationship from Medline abstracts by DigSee
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Deep Learning for Classification of Colorectal Polyps on Whole-slide Images
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Open Targets: a platform for therapeutic target identification and validation
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Systematic Analysis of Drug Targets Confirms Expression in Disease-Relevant Tissues
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Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer
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A Landscape of Pharmacogenomic Interactions in Cancer
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Deep learning for computational biology
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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
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Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images
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A new view of transcriptome complexity and regulation through the lens of local splicing variations
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ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions
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Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
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Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
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Sparse group factor analysis for biclustering of multiple data sources
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Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review
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Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib
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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
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Improving Drug Sensitivity Prediction Using Different Types of Data
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A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening
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Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research
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Deep learning of the tissue-regulated splicing code
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A community effort to assess and improve drug sensitivity prediction algorithms
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Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
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The BATTLE trial: personalizing therapy for lung cancer.
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A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data
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Properties and identification of human protein drug targets
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Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myélome.
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Prediction of potential drug targets based on simple sequence properties
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Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib.
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A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1.
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On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites
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High-risk myeloma: a gene expression–based risk-stratification model for newly diagnosed multiple myeloma treated with high-dose therapy is predictive of outcome in relapsed disease treated with single-agent bortezomib or high-dose dexamethasone
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From bloodjournal.hematologylibrary.org at PENN STATE UNIVERSITY on February 21, 2013. For personal use only.
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Quantitative nuclear grade (QNG): A new image analysis‐based biomarker of clinically relevant nuclear structure alterations
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Melting of Peridotite to 140 Gigapascals
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A guide to deep learning in healthcare
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The human splicing code reveals new insights into the genetic determinants of disease
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Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks
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The ChEMBL database in 2017
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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
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Massively Multitask Networks for Drug Discovery
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A call for deep-learning healthcare
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Transforming Computational Drug Discovery with Machine Learning and AI.
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Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases
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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
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Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
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Binding Pathway of Opiates to μ-Opioid Receptors Revealed by Machine Learning
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Estimation of clinical trial success rates and related parameters
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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
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Impact of a five-dimensional framework on R&D productivity at AstraZeneca
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Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology
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ESRP1 Mutations Cause Hearing Loss due to Defects in Alternative Splicing that Disrupt Cochlear Development.
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The art of curation at a biological database: Principles and application
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Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
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A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers
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Morphoproteomic Characterization of Lung Squamous Cell Carcinoma Fragmentation, a Histological Marker of Increased Tumor Invasiveness.
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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.
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Characterisation of mental health conditions in social media using Informed Deep Learning
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Performance measures in evaluating machine learning based bioinformatics predictors for classifications
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Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability
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Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.
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Convergence of Acquired Mutations and Alternative Splicing of CD19 Enables Resistance to CART-19 Immunotherapy.
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Toxicity Prediction using Deep Learning
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Count on kappa
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Using Information from Historical High-Throughput Screens to Predict Active Compounds
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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
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Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic
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Relating protein pharmacology by ligand chemistry
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This work identifies molecular signatures that are resistant to drug treatments and illustrates a multiomics approach to understanding drug response
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This article is the first effort to highlight the recent applications of DL in drug discovery research and is an introduction to some popular DL architectures
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The support of human genetic evidence for approved drug
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Bioinformatics — From Genomes to Therapies Ch
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The molecular classification of multiple myeloma
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Nature Genetics Advance Online Publication a N a Ly S I S the Support of Human Genetic Evidence for Approved Drug Indications
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quantification of immune cells from H&E slides and the identification of sub-categories of immune infiltrate as related to therapeutic outcome