ArticleJournal of theoretical biology2009
Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.
Article in Journal of theoretical biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 68 citations in OpenAlex.
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
- PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors.Biomedicines · 2022Article
- Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.Scientific reports · 2020Article
- Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection.Scientific reports · 2018Article
- Expression levels of miR-205 and miR-506 in colon cancer tissues and their relationships with clinicopathological features.Oncology letters · 2018Article
- Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction.Current topics in medicinal chemistry · 2012Article
- The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism.Journal of theoretical biology · 2011Article
- Three 3D graphical representations of DNA primary sequences based on the classifications of DNA bases and their applications.Journal of theoretical biology · 2011Article
- Graphical representation and mathematical characterization of protein sequences and applications to viral proteins.Advances in protein chemistry and structural biology · 2011Review
- Analysis of protein pathway networks using hybrid properties.Molecules (Basel, Switzerland) · 2010Article
- Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia.Molecules (Basel, Switzerland) · 2010Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randic's star networks topological indices using the new sequence to star networks (S2SNet) application. A set of 1054 proteins were selected from previous works and contains proteins related or not with two types of cancer, human breast cancer (HBC) and human colon cancer (HCC). The general discriminant analysis method generates an input-coded multi-target classification model with the training/predicting set accuracies of 90.0% for the forward stepwise model type. In addition, a protein subset was modified by single amino acid mutations with higher log-odds PAM250 values and tested with the new classification if can be related with HBC or HCC. In conclusion, we shown that, using simple input data such is the primary protein sequence and the simples linear analysis, it is possible to obtain accurate classification models that can predict if a new protein related with two types of cancer. These results promote the use of the S2SNet in clinical proteomics.
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