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Medical diagnosis is a major area of current research in Machine Learning and Data Mining. Single Nucleotide Polymorphisms (SNPs) are an important source of the human genome variability and have thus been implicated in several human diseases, including cancer. Breast Cancer is the most common malignant tumour for women and has known a large spread during the past twenty years. To separate the tumorous...
Disease prediction of infants from DNA sequences remains as an open challenge in the area of bioinformatics, which deals with understanding human diseases and in identification of new molecular target for drug discovery. To provide solution, a novel prototype architecture is designed for the disease prediction of infants, based on the combination of parent DNA using Artificial Neural Networks (ANN)...
Following the availability of whole genome sequence of Mycobacterium tuberculosis (MTB) in public database, the anticipation of its benefits would definitely falls into the contribution towards development of improved vaccine or drug discovery to fight against the fatal disease, tuberculosis (TB). This research aimed to scan the whole genome sequence of MTB by application of bioinformatics approaches...
Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study represents an integrative effort, coupling the results of a bioinformatic analysis based on microarray data of atherosclerotic aortic lesions of apoE knockout mice, a model widely used in atherosclerosis research, together with gene expression measurements of...
Vaticination and pigeonholing of lung carcinoma is a conspicuous nuisance for emancipate genesis. The carnage rate in elderly age is high as compared to younger ones and cure from carcinoma at premier stage is most salubrious. In this paper, we are proposing an interspersed and malleable approach for lung carcinoma pigeonholing and vaticination. The contrivance has been crumbled into laconic precincts...
The decision support systems that have been developed to assist physicians in the diagnostic process often are based on static data which may be out of date. We present a comprehensive analysis of artificial intelligent methods which could be applied to documents encoded by SNOMED CT. By mining information directly from SNOMED CT encoded documents, a decision support system could contain timely updated...
Motivation: Machine learning in bioinformatic sheds light on the traditional biography research. Through the prediction of functional genes from amino sequence information, the experimental cost for new gene finding could be reduced. Results: We propose an effective machine-learning approach based on artificial neural networks (ANN), to assess the chance of a protein in rice to be disease resistant...
Most common diseases are the result of complex nonlinear interactions between multiple genetic and environmental components. There is thus a pressing need for new computational methods capable of detecting nonlinearly interacting single nucleotide polymorphism (SNPs) that are associated with disease, from amidst up to hundreds of thousands of candidate SNPs. Recently, some progress has been made using...
The HIV-1 genome is highly heterogeneous. This variation affords the virus a wide range of molecular properties, including the ability to infect cell types, such as macrophages and lymphocytes, expressing different chemokine receptors on the cell surface. In particular, R5 HIV-1 viruses use CCR5 as a coreceptor for viral entry, X4 viruses use CXCR4, whereas some viral strains, known as R5X4 or D-tropic,...
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