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Slide 1: Title & Introduction

Title:

Network Pharmacology: Harnessing STRING, STITCH, and Clustering for Cellular Biology

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Introduction:

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Investigating Potential Drug Targets for Non-small Cell Lung Cancer through STITCH Database Analysis

Purpose

The aim of this study is to identify potential drug targets and repurposing opportunities by analyzing interactions between key proteins involved in non-small cell lung cancer (NSCLC) pathways, focusing on the inhibition of aberrant signaling in cancer therapy.

Hypothesis Formulation

• Initial Hypothesis: Targeting key nodes in the Ras, PI3K-Akt, Cell Cycle, p53, and Retinoid signaling pathways may provide effective therapeutic strategies for NSCLC by inhibiting cancer cell proliferation and survival.

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Leveraging STRING and STITCH APIs for White Paper Insights: A Structured Approach

This document provides a structured approach to utilizing the STRING and STITCH APIs for retrieving protein-protein and chemical-protein interaction data, enriching the findings of your white paper on drug response assessment in lung cancer, particularly in the context of TP53 and RB1 pathways.

Understanding the APIs

• STRING (Search Tool for the Retrieval of Interacting Genes/Proteins):
  STRING is a database and web resource dedicated to protein-protein interactions, including both known and predicted interactions. It integrates data from multiple sources, such as experimental repositories, computational prediction methods, and public text collections.

• STITCH (Search Tool for Interactions of Chemicals):

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WikiPathway: Retinoblastoma gene in cancer

PMID	Proteins	Year	Title
30773851	24	2019	Retinoblastoma mutation predicts poor outcomes in advanced non small cell lung cancer.
36399634	28	2023	Retinoblastoma Expression and Targeting by CDK46 Inhibitors in Small Cell Lung Cancer.
38034873	38	2023	Ropivacaine inhibits the malignant behavior of lung cancer cells by regulating retinoblastoma-binding protein 4.
17804741	3	2007	Retinoblastoma deficiency increases chemosensitivity in lung cancer.
25162518	31	2014	Retinoblastoma binding protein 2 (RBP2) promotes HIF-1alpha-VEGF-induced angiogenesis of non-small cell lung cancer via the Akt pathway.
[2

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WikiPathway: TP53 network

PMID	Proteins	Year	Title
PMID:21331359	10	2011	TP53 mutations in nonsmall cell lung cancer.
PMID:36290859	12	2022	TP53 Mutation Mapping in Advanced Non-Small Cell Lung Cancer: A Real-World Retrospective Cohort Study.
PMID:32545367	40	2020	TP53 Status, Patient Sex, and the Immune Response as Determinants of Lung Cancer Patient Survival.
PMID:36429016	40	2022	A Novel TP53 Gene Mutation Sustains Non-Small Cell Lung Cancer through Mitophagy.
PMID:35577980	27	2022	TP53, CDKN2AP16, and NFE2L2NRF2 regulate the incidence of pure- and combined-small cell lung cancer in mice.
[PMID:37930190](https://pubmed.ncbi.nlm.nih.gov/37930190/

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Emerging Standards and Trends in Drug Repurposing Bioinformatics: Explainable AI, Data Privacy, and Cloud-Based Advancements in 2024

Abstract

Traditional drug discovery is a costly and time-consuming process. Drug repurposing offers a promising alternative by seeking new therapeutic applications for existing drugs. In 2024, bioinformatics has become essential to drug repurposing, integrating multi-omics data, computational models, and knowledge bases to accelerate this process. This report explores the current bioinformatics standards in drug repurposing, highlighting the growing importance of explainable AI, data privacy, cloud computing, and standardized ontologies. We examine key players in this field, including government agencies, philanthropic organizations, and industry stakeholders, and detail the vital tools propelling data integration and workflow efficiency.

1. Introduction

Drug repurposing presents a paradigm shift in drug discovery, aiming to find new therapeutic uses for existing dru

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Leveraging STRING Database API Queries to Elucidate Sensitivity and Resistance Mechanisms in Cancer Cells

Abstract

Understanding the molecular mechanisms underlying cancer cell sensitivity and resistance to targeted therapies is crucial for the development of effective treatments. Protein-protein interaction (PPI) networks provide valuable insights into these mechanisms. This study demonstrates the use of optimized STRING database API queries to investigate key proteins and pathways associated with sensitivity and resistance in cancer cells harboring specific genetic mutations. Five focused queries were designed to explore interactions involving Aurora kinases, CDK4/6, KIF11, the MDM2-TP53 axis, and the RAS-MEK-ERK signaling pathway. The results highlight critical interactions and pathways that may contribute to drug response, emphasizing the significance of integrating PPI data to uncover potential therapeutic targets and enhance our understanding of cancer biology.

Introduction

Cancer remains a

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Revolutionizing Drug Repurposing and Discovery: Harnessing Pharmacological Datasets with Cloud Graph Databases

In the ever-evolving landscape of drug discovery and development, the ability to efficiently analyze vast amounts of pharmacological and bioinformatics data has become paramount. This article explores how the integration of comprehensive datasets like PubChem and the Guide to Pharmacology with cloud-based graph databases such as Neo4j's AuraDB is transforming drug repurposing and discovery efforts.

We'll delve into practical examples, showcasing how graph databases are uniquely suited to visualizing and interpreting the complex, multidimensional data typical in pharmacology. Our recent analysis of the STE7 kinase family serves as a compelling case study, demonstrating the power of this approach in uncovering new insights and potential therapeutic strategies.

Join us as we explore how these cutting-edge tools and methodologies are paving the way for more efficient, data-driven drug discovery and

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Leveraging STRING Database API Queries to Elucidate Sensitivity and Resistance Mechanisms in Cancer Cells

Abstract

Understanding the molecular mechanisms underlying cancer cell sensitivity and resistance to targeted therapies is crucial for the development of effective treatments. Protein-protein interaction (PPI) networks provide valuable insights into these mechanisms. This study demonstrates the use of optimized STRING database API queries to investigate key proteins and pathways associated with sensitivity and resistance in cancer cells harboring specific genetic mutations. Five focused queries were designed to explore interactions involving Aurora kinases, CDK4/6, KIF11, the MDM2-TP53 axis, and the RAS-MEK-ERK signaling pathway. The results highlight critical interactions and pathways that may contribute to drug response, emphasizing the significance of integrating PPI data to uncover potential therapeutic targets and enhance our understanding of cancer biology.

Introduction

Cancer remains a