Ecosyste.ms: Awesome
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Awesome_BigData_AI_DrugDiscovery
A collection of resources useful for leveraging big data and AI for drug discovery. It mainly serves as an orientation for new lab folks. It may be biased towards my lab interest.
https://github.com/Bin-Chen-Lab/Awesome_BigData_AI_DrugDiscovery
Last synced: 1 day ago
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Shape our future
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
- Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia
- Brown Adipogenic Reprogramming Induced by a Small Molecule
- Correlating chemical sensitivity and basal gene expression reveals mechanism of action.
- A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles
- Integrative clinical genomics of metastatic cancer
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Leveraging big data to transform target selection and drug discovery
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
- Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
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Field review
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Hallmarks of Cancer: The Next Generation
- Tumor Metastasis: Molecular Insights and Evolving Paradigms
- Cancer genome landscapes
- Cancer transcriptome profiling at the juncture of clinical translation
- Ewing sarcoma: historical perspectives, current state-of-the-art, and opportunities for targeted therapy in the future.
- Opportunities and challenges in phenotypic drug discovery: an industry perspective
- Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges
- Deep learning
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- Ewing sarcoma: historical perspectives, current state-of-the-art, and opportunities for targeted therapy in the future.
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
- Cancer transcriptome profiling at the juncture of clinical translation
- High-performance medicine: the convergence of human and artificial intelligence
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Statistical method development
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- Significance analysis of microarrays applied to the ionizing radiation response
- limma: Linear Models for Microarray Data
- Differential expression analysis for sequence count data
- Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
- Adjusting batch effects in microarray expression data using Empirical Bayes methods
- Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
- MuSiC: Identifying mutational significance in cancer genomes
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- Adjusting batch effects in microarray expression data using Empirical Bayes methods
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- Adjusting batch effects in microarray expression data using Empirical Bayes methods
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- limma: Linear Models for Microarray Data
- Emergence of Scaling in Random Networks - László Barabási.
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Informatics method development and application
- The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.
- Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data
- Relating protein pharmacology by ligand chemistry
- Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding
- Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy - 1) by Andrea Califano.
- Elucidating compound mechanism of action by network perturbation analysis
- Discovery of drug mode of action and drug repositioning from transcriptional responses
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Computational analysis
- Drug-target network
- Comprehensive molecular portraits of human breast tumours
- Mutational landscape and significance across 12 major cancer types.
- Comprehensive Characterization of Molecular Differences in Cancer between Male and Female Patients - 8), by Han Liang.
- Genetics of rheumatoid arthritis contributes to biology and drug discovery.
- The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
- Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases
- Do structurally similar molecules have similar biological activity
- A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set
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Deep-learning based drug discovery
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells
- A Deep Learning Approach to Antibiotic Discovery
- Deep reinforcement learning for de novo drug design
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
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Diseases/Patients
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Target Discovery
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Drug Discovery
- PubChem
- SEA
- LINCS
- ChemMine
- RNASEQ blog
- RPKM, FPKM and TPM, clearly explained
- RNA-seq workflow: gene-level exploratory analysis and differential expression
- anaconda
- rdkit
- ggplot cheatsheet
- ChemmineR: Cheminformatics Toolkit for R
- biomaRt
- GEOquery
- cgdsr
- pheatmap
- Easy Way to Mix Multiple Graphs on The Same Page
- SEA