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The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets.

Small RNAs play an important role in plant development, stress responses, and epigenetic regulation, primarily through their role in transcriptional and post-transcriptional silencing of specific target genes and loci. Most if not all plants utilize these small RNA signaling networks. We have developed a deep-sequencing based dataset of plant small RNAs, based on the hypothesis that comparisons among the complex pool of small RNAs from diverse plants will identify novel types of conserved, regulated, or species-specific molecules.

MicroRNAs have been found in various organisms and play essential roles in gene expression regulation of many critical cellular processes. Large-scale computational prediction of miRNAs has been conducted for many organisms using known genomic sequences; however, there has been no such effort for the thousands of known viral genomes. Some viruses utilize existing host cellular pathways for their own benefit. Furthermore, viruses are capable of encoding miRNAs and using them to repress host genes.

Tissue-specific miRNAs (TS miRNA) specifically expressed in particular tissues play an important role in tissue identity, differentiation and function. However, transcription factor (TF) and TS miRNA regulatory networks across multiple tissues have not been systematically studied. Here, we manually extracted 116 TS miRNAs and systematically investigated the regulatory network of TF-TS miRNA in 12 human tissues. We identified 2,347 TF-TS miRNA regulatory relations and revealed that most TF binding sites tend to enrich close to the transcription start site of TS miRNAs.

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms.

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets.