Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

30 Apr 2020

Abstract excerpt

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Caveolin-1 and cellular processes
Share to Ferroptosis and cancer prognosis
Share to Pancreatic and Hepatic Oncology Research

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32425783
DOI: 10.3389/fphar.2020.00534

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural ModelingPMID 32425783Read ↗

Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Integrated Bioinformatics Analysis and Molecular Docking ExperimentsSemantic proximity Read ↗

Identification of potential target genes in pancreatic ductal adenocarcinoma by bioinformatics analysisSemantic proximity Read ↗

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasetsSemantic proximity Read ↗

Identification of Blood-based Non-invasive Biomarkers and Therapeutic Agents against Pancreatic Ductal Adenocarcinoma (PDAC): A Network-based StudySemantic proximity Read ↗

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expressionSemantic proximity Read ↗

PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expressionSemantic proximity Read ↗

Identification of key hub genes in pancreatic ductal adenocarcinoma: an integrative bioinformatics studySemantic proximity Read ↗

Screening and validating the core biomarkers in patients with pancreatic ductal adenocarcinomaSemantic proximity Read ↗

Integrated bioinformatics analysis of potential biomarkers for pancreatic cancerSemantic proximity Read ↗

ITGA2, LAMB3, and LAMC2 May Be the Potential Therapeutic Targets in Pancreatic Ductal Adenocarcinoma: an Integrated Bioinformatics AnalysisSemantic proximity Read ↗

Molecular classification and prognosis study of pancreatic ductal adenocarcinoma through multi-omics integrated clustering analysis.Semantic proximity Read ↗

ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.