ReviewComputational and structural biotechnology journal2020
Computational advances of tumor marker selection and sample classification in cancer proteomics.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 44 citations in OpenAlex.
- Machine Learning-Assisted Plasma Metabolomics Identifies a Five-Metabolite Panel for Colorectal Cancer Detection.ACS omega · 2026Article
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- NMR-based clinical metabolomics for predictive screening of gestational diabetes mellitus (GDM) during the first trimester: a pilot study on North Indian population.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.Biochemical genetics · 2025Article
- Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model.Frontiers in artificial intelligence · 2025Article
- Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.Scientific reports · 2024Article
- Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (Translational cancer research · 2024Article
- A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.Functional & integrative genomics · 2024Review
- Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning.Heliyon · 2024Article
- Proteomics and metabolomics analyses of Streptococcus agalactiae isolates from human and animal sources.Scientific reports · 2023Article
- Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis.Scientific reports · 2023Article
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- Functional Outcomes of Patients with Primary Brain Tumors Undergoing Inpatient Rehabilitation at a Tertiary Care Rehabilitation Facility in Saudi Arabia.International journal of environmental research and public health · 2023Article
- Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Proteomic and computational analyses followed by functional validation of protective effects of trigonelline against calcium oxalate-induced renal cell deteriorations.Computational and structural biotechnology journal · 2023Article
- Development and evaluation of a radiomics model of restingAnnals of translational medicine · 2022Article
- Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis.International journal of molecular sciences · 2022Article
- A novel hypoxia-driven gene signature that can predict the prognosis of hepatocellular carcinoma.Bioengineered · 2022Article
- Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation.BMC medical genomics · 2022Article
- Identification of the diagnostic genes and immune cell infiltration characteristics of gastric cancer using bioinformatics analysis and machine learning.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.