While node semantics have been extensively explored in social networks, little research attention has been paid to pro le edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to pro le, due to noisy multi-modal signals and limited user-generated ground-truth labels.

In recent years, with the advent of fast sequencing technology, the genomic database is growing rapidly. Researchers in the bioinformatics field are expecting faster and more accurate tools to effectively analyze the gigantic data sets. In the context of aptamer search, the goal is to search for the over-represented DNA sequences from the randomly generated aptamer libraries. Hash functions are widely used in substring comparison, sequence alignment and clustering tools. We have developed a light-weight tool that takes advantage of the hash functions to reduce the size of genomic data and conducts η-neighbor searches on the centroid sequence. This greatly improves the efficiency of the search compared with existing tools. Furthermore, the prior calculation of hash values of η-neighbors decreases the searching overhead. In a dataset of 2.23 million sequences, the proposed algorithm accurately count the frequency of the Human α-Thrombin aptamer sequences in less than 40 seconds, whereas the current script-based method takes 2 hours and 18 minutes.

Gene regulatory networks (GRNs) describe epistatic relationship of genes and how the expression of some genes influence the expression of other genes. This information is critical for understanding molecular mechanisms regulating various biological processes and molecular basis of several diseases. Current research work mostly attempts to infer such regulatory relationships (and GRN architecture topology) from gene expression data. This paper improves on this methodology by utilizing additional information available that describes which cis-elements are present in which genes, and at what locations. Using the underlying principle that target genes of a transcription factor should share the same binding site in their promoter regions, we propose a scoring method that facilitates the refinement of a candidate GRN. Improvements are demonstrated with three data sets, on which GRNs are first obtained from existing dataset (AtRegNet) or using an existing approach (ARACNe), and then modified using cis-element information.