China
Africa
Explore chapters and articles related to this topic
The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Knowledge-based approaches have also been developed to consolidate known information about a particular drug in an attempt to anticipate previously unexplored unidentified targets for old drugs, unknown drug–drug similarities, and new biomarkers. By integrating a large amount of information, the certainty of prediction can be improved. Knowledge-based approaches can be broken down into four categories: bioinformatics (biological data mining), cheminformatics, pathway- or network-based approaches, and signature-based approaches. For bioinformatics, data can be accessed internally (in pharmaceutical companies) or from publicly sourced data bases such as the Biomolecular Interaction Network Database (BIND), the Human Protein Reference Database (HPRD), and the Human Proteome Organization (HUPO). Bioinformatics-based approaches have been used to discover new relationships between biomedical entities such as genes, biological pathways, and diseases. For example, this strategy has been used to study the potential Repurposing of FDA-approved tricyclic antidepressants to treat patients with small-cell lung cancer (SCLC) and other neuroendocrine tumors. In another example, ormeloxifene, a selective estrogen receptor modulator used as a nonsteroidal oral contraceptive and experimentally for dysfunctional uterine bleeding and advanced breast cancer, was recently shown to suppress prostate cancer through a knowledge-based study.
Scalable module detection for attributed networks with applications to breast cancer
Published in Journal of Applied Statistics, 2022
WFG was applied to a breast cancer omics problem that integrates three different types of data (1) gene expression data, (2) Protein–Protein Interaction (PPI) network, and (3) survival data. Our objective was to identify functional modules that relate to survival. In this application, the attributes are the gene expression profiles, the network is the PPI network, and the survival data as an outcome to prioritize functional modules. The gene expression data were taken from a breast cancer dataset (N = 295) by Van De Vijver et al. [38], and the survival data were also made available for all each patient. Briefly, transcript abundance was represented by the log10 of the ratio between each sample and the reference RNA [38]. The duration for survival analysis in this data is the Time To Metastasis (TTM) and 101 metastasis events occurred and 194 censored data points. The human PPI network was extracted from the Human Protein Reference Database (HPRD) [34]. The 24, 496 transcripts in the gene expression dataset were mapped to PPI network using Entrez gene identifiers. After mapping, the resulting functional network contained 7692 nodes and 27, 873 edges.
The development and clinical applications of proteomics: an Indian perspective
Published in Expert Review of Proteomics, 2020
Khushman Taunk, Bhargab Kalita, Vaikhari Kale, Venkatesh Chanukuppa, Tufan Naiya, Surekha M. Zingde, Srikanth Rapole
The research group at G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, has published many articles about choosing the databases and strategies for proteomics data analysis [73–76]. Researchers from IOB, Bangalore have developed several bioinformatics tools and portals including Human Proteinpedia (www.humanproteinpedia.org/), Human Protein Reference Database (www.hprd.org) and Human Proteome Map for sharing and integration of human protein data [26,77]. The same group also developed the NetPath database (http://www.netpath.org) that harbors data on 36 human signaling pathways curated manually [78]. Other notable examples include Human Protein Atlas (http://www.proteinatlas.org/) for which the team at Lab SurgPath, Mumbai has annotated the images of the stained tissue sections [27]; CCDB, a cervical cancer database [79], CancerPPD, a database of anticancer peptides and proteins [80] and Hemolytik, a database of hemolytic and non-hemolytic peptides created by researchers at IMTECH, Chandigarh [81]; RespCanDB, a genomic and proteomic database of respiratory organ cancer [82]; DOQCS, a collection of diverse signaling pathways developed at NCBS, Bangalore [83]; and a few other protein databases including Immune Epitope Prediction Database & Tools, PepBind, VPDB, Clostridium-DT (DB), SEDB, PRODOC, NrichD, ‘KinG’, MulPSSM, PALI developed at Pondicherry University and IISc, Bangalore [84–92], and oral cancer databases developed at ACTREC, Navi Mumbai [93,94] and ICPO, New Delhi [95].
Ma Huang Tang ameliorates bronchial asthma symptoms through the TLR9 pathway
Published in Pharmaceutical Biology, 2018
Jiayuan Jiao, Jiming Wu, Jiali Wang, Yaping Guo, Le Gao, Honggang Liang, Jian Huang, Jinhui Wang
A primary global human PPI network of the determined proteins was constructed with diverse PPIs from online databases. These protein interaction data were collected from Human Protein Reference Database (HPRD) and PrePPI database (Keshava Prasad et al. 2009; Zhang et al. 2013). As different data sources can predict protein interactions from different aspects, we applied a naive Bayesian model to integrate diverse data and make the final interaction predictions (Li et al. 2010). Bronchial asthma-related proteins were filtered by the DAVID database, in which the functional determined protein in each pair is included. The unified conceptual framework of the PPI network was finally integrated by Cytoscape (Politano et al. 2014).