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BioAssay involves the use of live animal or plant (in vivo) or tissue or cell (in vitro) to determine the biological activity of a substance, such as hormone or drug, which plays a key role especially in evaluation of clinical efficacy for drug development. How to better utilize the biological information contained in the BioAssay for systematical identification of novel biological associations is...
Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC...
Aptamers and biclustering are enabling technologies for personalized medicine. Aptamers are highly valuable for medicine and biosensors, but the search is lengthy. We seek to accelerate aptamer discovery by reporting on the first set of DNA aptamer biclusters, which are based on a database of known DNA aptamers. These biclusters identify similar aptamer sequences, and show the important 4-mers shared...
Current genomic tests reporting single nucleotide polymorphisms (SNPs) have limited predictability on individual patient responses to drugs. A novel method of using SNPs for homology proteomic modeling and drug docking of target proteins was tested in combination with transcriptomic determined kinetic biosimulations generating metabolomics and fluxomics of individual patients. Multiple informatics...
RNA sequencing, or RNA-seq for short, is widely applied for extracting gene and transcript expression from biological samples. Given numerous quantification pipelines for RNA-seq data, one fundamental challenge is to identify a pipeline that can produce the most accurate gene and/or transcript expression. Exploring all pipelines requires extensive computational resources, so we propose to use a subsampling...
With the rapid growth of huge amounts of DNA sequence, gene prediction has become a challenging problem in bioinformatics. Splice sites prediction plays a key role in identification of genes. Hence, development of new methods to improve the accuracy of the splice sites prediction has great significance. This paper introduces a new method for splice sites prediction by combining AdaBoost classifier...
The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation...
Heterogeneity of genomic instabilities among individual patients is believed to be a major cause of drug response heterogeneity. Cancer patients who are sensitive to anti-cancer drugs are often re-examined to understand the unknown mechanism of action (MoA) of given drugs. For example, a non-small cell lung cancer (NSCLC) patient was reported to be responsive to Dasatinib treatment and remained cancer-free...
In this paper, a novel approach based on Binary Black Hole Algorithm (BBHA) and Random Forest Ranking (RFR) is proposed for gene selection and classification of microarray data. In this approach, RFR and BBHA are used to perform gene selection to remove irrelevant and redundant genes. Because of its ability in reducing noise, bias and variance errors Bagging with 10-fold cross validation is selected...
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