ArticleBioMed research international2015
Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 18 citations in OpenAlex.
- Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.Sensors (Basel, Switzerland) · 2023Article
- Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural MotifsAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach.Oncotarget · 2016Article
- Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.BMC bioinformatics · 2016Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.
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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.