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BRAF mutation predicts sensitivity to MEK inhibition

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What Does This Mutation Mean? The Tools and Pitfalls of Variant Interpretation in Lymphoid Malignancies

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Whole-genome sequencing reveals an association between small genomic deletions and an increased risk of developing Parkinson’s disease

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High-throughput screening and genome-wide analyses of 44 anticancer drugs in the 1000 Genomes cell lines reveals an association of the NQO1 gene with the response of multiple anticancer drugs

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Decoding Drug Response With Structurized Gridding Map-Based Cell Representation

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RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction

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MEK is a promising target in the basal subtype of bladder cancer

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Robustin-silicoidentification of cancer cell lines based on next generation sequencing

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Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells

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Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens

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Unearthing new genomic markers of drug response by improved measurement of discriminative power

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OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicinePreprint

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Reliable anti-cancer drug sensitivity prediction and prioritization

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EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants

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Macromolecular crowding in the development of a three-dimensional organotypic human breast cancer model

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Prediction of Cancer Treatment Using Advancements in Machine Learning

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The necroptosis signature and molecular mechanism of lung squamous cell carcinoma

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A Core NRF2 Gene Set Defined Through Comprehensive Transcriptomic Analysis Predicts Selective Drug Resistance and Poor Multicancer Prognosis

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Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses

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A computational model for anti-cancer drug sensitivity prediction

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ATM inhibition synergizes with fenofibrate in high grade serous ovarian cancer cells

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A pre-trained large generative model for translating single-cell transcriptome to proteomePreprint

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Patient-Derived In Vitro and In Vivo Models of Cancer

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A Novel Hydrogel-Based 3D In Vitro Tumor Panel of 30 PDX Models Incorporates Tumor, Stromal and Immune Cell Compartments of the TME for the Screening of Oncology and Immuno-Therapies

Xue, B., Schüler, J. B., Harrod, C. M., Lashuk, K., Bomya, Z., & Hribar, K. C. (2023) · Cells · doi:10.3390/cells12081145 · Cited by 12

Chemogenetic interactions in human cancer cells

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Feasibility Technique of Low-passage In Vitro Drug Sensitivity Testing of Malignant Pleural Effusion from Advanced-stage Non-small Cell Lung Cancer for Prediction of Clinical Outcome

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Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines

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The impact of violating the independence assumption in meta-analysis on biomarker discovery

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Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)

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Parkin activates innate immunity and promotes antitumor immune responses

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Restoration of TP53 strategy via specific nanoparticles for ovarian cancer therapy

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PDGFRα/β heterodimer activation negatively affects downstream ERK1/2 signaling and cellular proliferation

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Upregulation of HOX genes promotes cell migration and proliferation in head and neck squamous cell carcinoma

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Methods and resources to access mutation-dependent effects on cancer drug treatment

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Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms

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Microbial Metabolites of Flavanols in Urine are Associated with Enhanced Anti-Proliferative Activity in Bladder Cancer Cells In Vitro

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Analysis of the PD-1 Ligands Among Gastrointestinal Cancer Patients: Focus on Cancer Immunity

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How to build a tumor: An industry perspective

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miR663a‑TTC22V1 axis inhibits colon cancer metastasis

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Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

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Is There a Reproducibility Crisis? On the Need for Evidence-based Approaches

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shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning

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Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

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Functionalized Nanomaterials In Pancreatic Cancer Theranostics And Molecular Imaging

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Nanopore sequencing reveals TACC2 locus complexity and diversity of isoforms transcribed from an intronic promoter

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Landscape of Epidermal Growth Factor Receptor Heterodimers in Brain Metastases

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Revisiting inconsistency in large pharmacogenomic studies

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Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach

Kim, Y. R., Kim, Y., Lee, S., Yang, H. J., & Kim, S. Y. (2019) · Cancers · doi:10.3390/cancers11010045 · Cited by 8

Integrating functional genomics to accelerate mechanistic personalized medicinePreprint

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BDKANN - Biological Domain Knowledge-based Artificial Neural Network for drug response predictionPreprint

Snow, O., Sharifi-Noghabi, H., Lu, J., Zolotareva, O. I., Lee, M., & Ester, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/840553 · Cited by 8

A pre-trained large generative model for translating single-cell transcriptomes to proteomes

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Drug response in the era of precision medicine: A methodological review

Okyere, D., Bravo-Merodio, L., Xu, Y., Guan, X., Parkhi, D., Gkoutos, G. V., & Acharjee, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.067 · Cited by 9

RePhine: An Integrative Method for Identification of Drug Response-Related Transcriptional Regulators

Wang, X., Zhang, Z., Qin, W., Liu, S., Liu, C., Genchev, G. Z., Hui, L., Zhao, H., & Lu, H. J. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.09.008 · Cited by 7

Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies

Rao, J. S., & Liu, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-15590-4 · Cited by 7

Co-regularized kernel ensemble regression

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Copy number and expression of CEP89, a protein required for ciliogenesis, are increased and predict poor prognosis in patients with ovarian cancer

Berkel, Ç., & Çaçan, E. (2022) · Cell Biochemistry and Function · doi:10.1002/cbf.3694 · Cited by 7

Exploratory Analysis of Single-Gene Predictive Biomarkers in HERA DASL Cohort Reveals That C8A mRNA Expression Is Prognostic of Outcome and Predictive of Benefit of Trastuzumab

Willis, S., Polydoropoulou, V., Sun, Y., Young, B. M., Tsourti, Z., Karlis, D., Long, B. C., Lin, X., Theel, S., Carlson, J. H., et al. (2018) · JCO Precision Oncology · doi:10.1200/po.18.00016 · Cited by 7

Identifying Drug Sensitivity Subnetworks with NETPHIX

Kim, Y.-A., Basso, R. S., Wójtowicz, D., Liu, A. S., Hochbaum, D. S., Vandin, F., & Przytycka, T. M. (2020) · iScience · doi:10.1016/j.isci.2020.101619 · Cited by 8

Assessing consistency across functional screening datasets in cancer cells

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Prediction of regulatory targets of alternative isoforms of the epidermal growth factor receptor in a glioblastoma cell line

Weinholdt, C., Wichmann, H., Kotrba, J., Ardell, D. H., Kappler, M., Eckert, A. W., Vordermark, D., & Große, I. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2944-9 · Cited by 7

Organoid‐based two‐step drug screening for rapid identification of chemotherapy‐resistant oesophageal squamous cell carcinoma and alternative therapies

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Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Kaushik, A. C., & Zhao, Z. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1215204 · Cited by 6

Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning

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PLK1 Inhibition Induces Synthetic Lethality in Fanconi Anemia Pathway–Deficient Acute Myeloid Leukemia

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A side-by-side comparison of variant function measurements using deep mutational scanning and base editing

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Progress towards precision functional genomics in cancer

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RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction

Lee, K., Cho, D., Jang, J., Choi, K. Y., Jeong, H., Seo, J., Jeong, W., & Lee, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac504 · Cited by 6

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Zang, L., Zhang, B., Zhou, Y., Zhang, F., Tian, X., Tian, Z., Chen, D., & Miao, Q. (2023) · Aging · doi:10.18632/aging.205293 · Cited by 6

A new approach to assess drug sensitivity in cells for novel drug discovery

Mazzarella, L., & Curigliano, G. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1437136 · Cited by 6

Characterization of inv(3) cell line OCI-AML-20 with stroma-dependent CD34 expression

Luciani, G. M., Xie, L., Dilworth, D., Tierens, A. M., Moskovitz, Y., Murison, A., Szewczyk, M. M., Mitchell, A., Lupien, M., Shlush, L. I., et al. (2018) · Experimental Hematology · doi:10.1016/j.exphem.2018.10.006 · Cited by 6

O-GlcNAcylation: Implications in normal and malignant hematopoiesis

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Effectively utilizing publicly available databases for cancer target evaluation

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