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Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome.

The first ncRNA found was an alanine tRNA in baker's yeast, and the first detected microRNAs (miRNAs) promoted ncRNA research to a whole new level. Research on ncRNAs in animals has focused on the medical field, while in plant scientists are more concerned with improving agronomic traits. In 2010, we constructed a plant miRNA database named PMRD to meet the demand for miRNA research in plants.

Although the observations concerning the factors which influence the siRNA efficacy give clues to the mechanism of RNAi, the quantitative prediction of the siRNA efficacy is still a challenge task. In this paper, we introduced a novel non-linear regression method: random forest regression (RFR), to quantitatively estimate siRNAs efficacy values. Compared with an alternative machine learning regression algorithm, support vector machine regression (SVR) and four other score-based algorithms [A. Reynolds, D. Leake, Q. Boese, S. Scaringe, W.S. Marshall, A.

MicroRNAs (miRNAs) are non-coding RNAs (ncRNAs) involved in regulation of gene expression. Intragenic miRNAs, especially those exhibiting a high degree of evolutionary conservation, have been shown to be coordinately regulated and/or expressed with their host genes, either with synergistic or antagonistic correlation patterns. However, the degree of cross-species conservation of miRNA/host gene co-location is not known and co-expression information is incomplete and fragmented among several studies.

microRNAs (miRNAs) regulate expression by promoting degradation or repressing translation of target transcripts. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using various algorithms. Several online resources provide collections of multiple databases but need to be imported into other software, such as R, for processing, tabulation, graphing and computation. Currently available miRNA target site packages in R are limited in the number of databases, types of databases and flexibility.

Renal cell carcinoma or RCC is one of the common and most lethal urological cancers, with 40% of the patients succumbing to death because of metastatic progression of the disease. Treatment of metastatic RCC remains highly challenging because of its resistance to chemotherapy as well as radiotherapy, besides surgical resection. Whereas RCC comprises tumors with differing histological types, clear cell RCC remains the most common.