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Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Synthetic Lethal Interaction between Oncogenic KRAS Dependency and STK33 Suppression in Human Cancer Cells
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A Systematic Mammalian Genetic Interaction Map Reveals Pathways Underlying Ricin Susceptibility
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Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality
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In Vivo HIF-Mediated Reductive Carboxylation Is Regulated by Citrate Levels and Sensitizes VHL-Deficient Cells to Glutamine Deprivation
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Decreasing the Rate of Metabolic Ketone Reduction in the Discovery of a Clinical Acetyl-CoA Carboxylase Inhibitor for the Treatment of Diabetes
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Gene Ontology: tool for the unification of biology
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Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy
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Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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A community effort to assess and improve drug sensitivity prediction algorithms
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The Cancer Genome Atlas Pan-Cancer analysis project
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Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression
inferredcites →https://doi.org/10.1038/nm.3480openalex-snapshot
ARID1B is a specific vulnerability in ARID1A-mutant cancers
inferredcites →https://doi.org/10.1038/nm.3788openalex-snapshot
Isocitrate dehydrogenase 1 and 2 mutations induce BCL-2 dependence in acute myeloid leukemia
inferredcites →https://doi.org/10.1038/nmeth.2689openalex-snapshot
DGIdb: mining the druggable genome
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Minimizing the risk of reporting false positives in large-scale RNAi screens
inferredcites →https://doi.org/10.1038/nrclinonc.2011.121openalex-snapshot
Predictive biomarkers: a paradigm shift towards personalized cancer medicine
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Results of an abbreviated phase-II study with the Akt Inhibitor MK-2206 in Patients with Advanced Biliary Cancer
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Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia
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Functional genomics identifies therapeutic targets for MYC-driven cancer
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Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
inferredcites →https://doi.org/10.1093/genetics/31.3.269openalex-snapshot
GENETICS OF NATURAL POPULATIONS. XIII. RECOMBINATION AND VARIABILITY IN POPULATIONS OF DROSOPHILA PSEUDOOBSCURA
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KEGG: Kyoto Encyclopedia of Genes and Genomes
inferredcites →https://doi.org/10.1093/nar/28.1.27openalex-snapshot
KEGG: Kyoto Encyclopedia of Genes and Genomes
inferredcites →https://doi.org/10.1093/nar/gng015openalex-snapshot
Summaries of Affymetrix GeneChip probe level data
inferredcites →https://doi.org/10.1111/bjh.13603openalex-snapshot
Phase II study of an AKT inhibitor MK2206 in patients with relapsed or refractory lymphoma
inferredcites →https://doi.org/10.1126/science.1160809openalex-snapshot
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
inferredcites →https://doi.org/10.1126/science.1180823openalex-snapshot
The Genetic Landscape of a Cell
inferredcites →https://doi.org/10.1126/science.1225787openalex-snapshot
Use and Abuse of RNAi to Study Mammalian Gene Function
inferredcites →https://doi.org/10.1126/science.aac7041openalex-snapshot
Identification and characterization of essential genes in the human genome
inferredcites →https://doi.org/10.1126/scisignal.2001083openalex-snapshot
Synthetic Lethal Screen of an EGFR-Centered Network to Improve Targeted Therapies
inferredcites →https://doi.org/10.1126/scitranslmed.3002394openalex-snapshot
Targeting GLUT1 and the Warburg Effect in Renal Cell Carcinoma by Chemical Synthetic Lethality
inferredcites →https://doi.org/10.1158/0008-5472.can-06-0140openalex-snapshot
Deficiency in the Repair of DNA Damage by Homologous Recombination and Sensitivity to Poly(ADP-Ribose) Polymerase Inhibition
inferredcites →https://doi.org/10.1158/0008-5472.can-07-0389openalex-snapshot
Chemical Inhibition of Acetyl-CoA Carboxylase Induces Growth Arrest and Cytotoxicity Selectively in Cancer Cells
inferredcites →https://doi.org/10.1158/0008-5472.can-10-2836openalex-snapshot
Parallel High-Throughput RNA Interference Screens Identify PINK1 as a Potential Therapeutic Target for the Treatment of DNA Mismatch Repair–Deficient Cancers
inferredcites →https://doi.org/10.1158/0008-5472.can-12-3956openalex-snapshot
A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer
inferredcites →https://doi.org/10.1158/2159-8290.cd-16-0154openalex-snapshot
Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting
inferredcites →https://doi.org/10.1158/2159-8290.cd-16-0178openalex-snapshot
CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions
inferredcites →https://doi.org/10.1186/gb-2008-9-10-r157openalex-snapshot
Boolean implication networks derived from large scale, whole genome microarray datasets
inferredcites →https://doi.org/10.1200/jco.2015.61.5997openalex-snapshot
Impact of Precision Medicine in Diverse Cancers: A Meta-Analysis of Phase II Clinical Trials
inferredcites →https://doi.org/10.1371/journal.pcbi.1004506openalex-snapshot
Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality
inferredcites →https://doi.org/10.1371/journal.pone.0102119openalex-snapshot
Mining TCGA Data Using Boolean Implications
inferredcites →https://doi.org/10.18632/oncotarget.4507openalex-snapshot
Combinatorial high-throughput experimental and bioinformatic approach identifies molecular pathways linked with the sensitivity to anticancer target drugs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Machine Learning Approaches for the Identification of Genetic Interactions
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R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools
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Genomic Strategies for Identification and Validation of Targets in Personalized Medicine
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SLdGNN: Synthetic Lethality Prediction Based on Multi-Source Data and Dual-Graph Neural Networks
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SLGNNCT: Synthetic Lethality Prediction Based on Knowledge Graph for Different Cancers Types
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MCKG-SL: Knowledge graph-based multi-feature cross-aggregation synthetic lethality prediction for KRAS gene
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Non-oncogene dependencies: Novel opportunities for cancer therapy
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Therapeutic targeting of tricarboxylic acid cycle and oxidative phosphorylation: a critical analysis of metabolic interventions in cancer treatment
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CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference
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G2G: A web-server for the prediction of human synthetic lethal interactions
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Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations
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mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes
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A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma
inferred← citeshttps://doi.org/10.1016/j.csbj.2022.09.025openalex-snapshot
Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer
inferred← citeshttps://doi.org/10.1016/j.csbj.2025.04.012openalex-snapshot
Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron
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Sorting with forbidden intermediates
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Synthetic lethality: a step forward for personalized medicine in cancer
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Neonatal heel prick mass spectrometry identifies metabolic predictors of AML latency
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Fatty acid synthesis and cancer: Aberrant expression of the ACACA and ACACB genes increases the risk for cancer
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Precision Oncology: The Road Ahead
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Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells
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Computational network biology: Data, models, and applications
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Comprehensive molecular and clinical characterization of SLC1A5 in human cancers
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Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets
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A Road Map to Personalizing Targeted Cancer Therapies Using Synthetic Lethality
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PrePAIRing Cas9s for screening success
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Data mining for mutation-specific targets in acute myeloid leukemia
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Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer
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Harnessing synthetic lethality to predict the response to cancer treatment
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Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens
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Benchmarking machine learning methods for synthetic lethality prediction in cancer
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Synthetic lethality in cancer drug discovery: challenges and opportunities
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Von Hippel–Lindau protein signalling in clear cell renal cell carcinoma
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Autopalmitoylation of IDH1-R132H regulates its neomorphic activity in cancer cells
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Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma
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Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients.
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SL-scan identifies synthetic lethal interactions in cancer using metabolic networks
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The m 6 A RNA demethylase FTO is a HIF-independent synthetic lethal partner with the VHL tumor suppressor
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Precision and strategic targeting of novel mutation-specific vulnerabilities in acute myeloid leukemia: the semi-centennial of 7 + 3
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Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches
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Computational methods, databases and tools for synthetic lethality prediction
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Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction
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Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers
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Link synthetic lethality to drug sensitivity of cancer cells
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Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers
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Graph contextualized attention network for predicting synthetic lethality in human cancers
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PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers
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SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network
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DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers
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Predicting synthetic lethal interactions using heterogeneous data sources
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SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery
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SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis
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Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens
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Mining Pathway Associations from Networks of Mutual Exclusivity Interactions
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CASTLE: A database of synthetic lethal sets predicted from genome-scale metabolic networks
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mslp: a comprehensive pipeline in predicting cancer mutations specific synthetic lethal partners
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SLIDE-VIP: a comprehensive, cell line- and patient-based framework for synthetic lethality prediction in DNA damage repair, chromatin remodeling and cell cycle
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Systematic Identification of Novel Cancer Genes through Analysis of Deep shRNA Perturbation Screens
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Multi-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction
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Prediction of Synthetic Lethal Interactions in Human Cancers Using Multi-View Graph Auto-Encoder
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A New Process Using Magnetic Nanoparticles to Neuronal Cells Growth Orientation
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SL 2 MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
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MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality
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KG-SLomics: Synthetic Lethality Prediction Using Knowledge Graph and Cancer Type-Specific Multiomics Integrated Graph Neural Network
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Systematic analysis of cancer‐specific synthetic lethal interactions provides insight into personalized anticancer therapy
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Multi‐omics characterization of synthetic lethality‐related molecular features: implications for SL ‐based therapeutic target screening
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Cancer-associated mutation and beyond: The emerging biology of isocitrate dehydrogenases in human disease
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Searching for Essential Genes in the Cancer Genome
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Importance of genetic screens in precision oncology
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Predicting Synthetic Lethal Genetic Interactions in Breast Cancer using Decision Tree
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Dissecting the Tumor–Immune Landscape in Chimeric Antigen Receptor T-cell Therapy: Key Challenges and Opportunities for a Systems Immunology Approach
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Pertuzumab Enhances the Antitumor Activity of T-DXd in HER2-Positive Gastric Cancer Cells
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Dysregulated Lipid Synthesis by Oncogenic IDH1 Mutation Is a Targetable Synthetic Lethal Vulnerability
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Oncogenic IDH1 Mutation Imparts Therapeutically Targetable Metabolic Dysfunction in Multiple Tumor Types
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RUNX1 loss renders hematopoietic and leukemic cells dependent on IL-3 and sensitive to JAK inhibition
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Synthetic lethality in TP53 -mutant acute myeloid leukemia
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Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
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Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
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Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality
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SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery
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Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes
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MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network
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Searching for essential genes in cancer genomes
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Exploiting Synthetic Lethal Network for Precision Treatment of Clear Cell Renal Cell Carcinoma
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DNA Double Strand Break Repair - Related Synthetic Lethality
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Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens
inferred← citeshttps://doi.org/10.3389/fgene.2022.1103092openalex-snapshot
Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction
inferred← citeshttps://doi.org/10.3389/fgene.2022.961611openalex-snapshot
Synthetic lethal gene pairs: Experimental approaches and predictive models
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EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction
inferred← citeshttps://doi.org/10.3389/fphar.2023.1280099openalex-snapshot
Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population
inferred← citeshttps://doi.org/10.3389/fphar.2025.1636326openalex-snapshot
COPZ1: an example of non-oncogene addiction in human tumors
inferred← citeshttps://doi.org/10.3390/genes16030310openalex-snapshot
Cell Cycle-Based Molecular Features via Synthetic Lethality and Non-Coding RNA Interactions in Cancer
inferred← citeshttps://doi.org/10.3390/ijms22115614openalex-snapshot
Breast Cancer Predisposition Genes and Synthetic Lethality
inferred← citeshttps://doi.org/10.3390/ijms222011114openalex-snapshot
Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes
inferred← citeshttps://doi.org/10.3390/jpm12010098openalex-snapshot
Harnessing Synthetic Lethal Interactions for Personalized Medicine
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Mapping the landscape of synthetic lethal interactions in liver cancer
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