Progressive data mining: An Exploration of using whole-dataset feature selection in building classifiers on three biological problems

Ph.DDOCTOR OF PHILOSOPH

DDESC: Dragon database for exploration of sodium channels in human

<p>Abstract</p> <p>Background</p> <p>Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information.</p> <p>Results</p> <p>We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses.</p> <p>Conclusion</p> <p>DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels. This database is freely accessible for academic and non-profit users via the worldwide web <url>http://apps.sanbi.ac.za/ddesc</url>.</p

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.publishedVersionPeer reviewe

DAMPD: a manually curated antimicrobial peptide database

The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs

Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification and scoring based prioritization methods for determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors’ could be used in accurately categorizing the genetic factors in disease causatio