Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices

Abstract

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, HBC (human breast cancer) and HCC (human colon cancer). The General Discriminant Analysis method generates an input-coded multitarget 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.

S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal Sciences

“…S2SNet has been successfully used by our group in four previous papers in order to create classification models for evaluating a protein as natural or random [25], as breast/colon [26] or prostate [27] cancer-related, a DNA sequence as a Mycobacterial promoter [28], and for early detection of diseases and drug-induced toxicities using the blood serum proteome mass spectrum [29]. This work presents four cases of S2SNet calculations for proteins, DNA, mass spectra and laws.…”

Star network analysis of sequence based identified Yarrowia lipolytica strains

“…Also in the manner of some epidemic spreading networks a more physical insight comes from the analysis of a star graph with one center connected to leaves [9]. Proteins can be transformed into a star graph, where the amino acids are the vertices (nodes), connected in a specific sequence by the peptide bonds (central node) [10,11]. In our work, each of the branches of the star contains the sequences related to different species and the star center which is the central node is a non-species vertex.…”

Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties

“…Computational algorithms may play an important role in the process of elucidation of structure-activity relationships for many molecular systems and biological problems (Aguilera and Rodriguez-Gonzalez, 2014; Barresi et al, 2013;Gonzalez-Diaz et al, 2011;Munteanu et al, 2009). In particular, the theoretical biology has been useful in the study of anti-HIV drugs and/or their molecular targets (Jain Pancholi et al, 2014;Ogul, 2009;SpeckPlanche et al, 2012;Weekes and Fogel, 2003;Xu et al, 2013).…”