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Computational methods and tools that can efficiently and effectively analyze the temporal changes in dynamic complex relational networks enable us to gain significant insights regarding the entity relations and their evolution. This article introduces a new class of dynamic graph patterns, referred to as coevolving relational motifs (CRMs), which are designed to identify recurring sets of entities whose relations change in a consistent way over time. CRMs can provide evidence to the existence of, possibly unknown, coordination mechanisms by identifying the relational motifs that evolve in a similar and highly conserved fashion. We developed an algorithm to efficiently analyze the frequent relational changes between the entities of the dynamic networks and capture all frequent coevolutions as CRMs. Our algorithm follows a depth-first exploration of the frequent CRM lattice and incorporates canonical labeling for redundancy elimination. Experimental results based on multiple real world dynamic networks show that the method is able to efficiently identify CRMs. In addition, a qualitative analysis of the results shows that the discovered patterns can be used as features to characterize the dynamic network. ACM Reference Format:Rezwan Ahmed and George Karypis. 2015. Algorithms for mining the coevolving relational motifs in dynamic networks.

Motivation:Alpha-helical transmembrane proteins mediate many key biological processes and represent 20-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods that predict their topology (transmembrane helical segments and their orientation) are essential in advancing the understanding of membrane proteins' structures and functions. Methods:We developed a new topology prediction method for transmembrane helices called TOPTMH that combines a helix residue predictor with a helix segment identification method and determines the overall orientation using the positive-inside rule. The residue predictor is built using Support Vector Machines (SVM) that utilize evolutionary information in the form of PSI-BLAST generated sequence profiles to annotate each residue by its likelihood of being part of a helix segment. The helix segment identification method is built by combining the segments predicted by two Hidden Markov Models (HMM)-one based on the SVM predictions and the other based on the hydrophobicity values of the sequence's amino acids. This approach combines the power of SVM-based models to discriminate between the helical and non-helical residues with the power of HMMs to identify contiguous segments of helical residues that take into account the SVM predictions and the hydrophobicity values of neighboring residues. Results:We present empirical results on two standard datasets and show that both the per-residue (Q2) and per-segment (Q ok ) scores obtained by TOPTMH are higher than those achieved by well-known methods such as Phobius and MEMSAT3. In addition, on an independent static benchmark, TOPTMH achieved the highest scores on high-resolution sequences (Q2 score of 84% and Q ok score of 86%) against existing state-of-the-art systems while achieving low signal peptide error. 4 IntroductionAlpha-helical transmembrane proteins perform several cellular functions, such as cell-to-cell communication, transportation of ions and small molecules, and cell signalling [3]. Moreover, these proteins are of key interest for drug discovery, since about 50% of all existing drugs are targeted against membrane proteins [14]. These proteins are encoded by 20% to 30% genes in several organisms [29], but only 1% of known 3D structures represent membrane proteins [2]. Transmembrane proteins are hard to crystallize and not suitable for NMR spectroscopy. Hence, computational methods attempt to characterize the 3D structure of membrane proteins from sequence by identifying the location and orientation of helical segments i.e., topological structure.Over the years a number of methods have been developed for predicting the topology of transmembrane helical (TMH) proteins. The early methods relied on the fact that the helical segments helical segments are usually hydrophobic in nature. Hence, these methods used simple hydrophobicity values [15] for identification of these segments. The TopPred [27] devised a simple method based on the fa...

Motivation:Alpha-helical transmembrane proteins mediate many key biological processes and represent 20-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods that predict their topology (transmembrane helical segments and their orientation) are essential in advancing the understanding of membrane proteins' structures and functions. Methods:We developed a new topology prediction method for transmembrane helices called TOPTMH that combines a helix residue predictor with a helix segment identification method and determines the overall orientation using the positive-inside rule. The residue predictor is built using Support Vector Machines (SVM) that utilize evolutionary information in the form of PSI-BLAST generated sequence profiles to annotate each residue by its likelihood of being part of a helix segment. The helix segment identification method is built by combining the segments predicted by two Hidden Markov Models (HMM)-one based on the SVM predictions and the other based on the hydrophobicity values of the sequence's amino acids. This approach combines the power of SVM-based models to discriminate between the helical and non-helical residues with the power of HMMs to identify contiguous segments of helical residues that take into account the SVM predictions and the hydrophobicity values of neighboring residues. Results:We present empirical results on two standard datasets and show that both the per-residue (Q2) and per-segment (Q ok ) scores obtained by TOPTMH are higher than those achieved by well-known methods such as Phobius and MEMSAT3. In addition, on an independent static benchmark, TOPTMH achieved the highest scores on high-resolution sequences (Q2 score of 84% and Q ok score of 86%) against existing state-of-the-art systems while achieving low signal peptide error.

Temporal data management has an ancient history. From the earliest days people are using timeline (a way of displaying a list of events in chronological order) to record their transaction data in a log file or table and often those files or tables are used by researchers to understand the events or trends of the transaction. The first attempt to illustrate chronological events graphically was made in 1765 and presently timeline is used ubiquitously. This paper attempts to explain the history and present state of timeline visualization and proposes a timeline visualization model that provides a new perspective on the existing models. We showed the development of timeline visualization of temporal data over decades, evaluated different techniques, connected and presented them in sequential order to justify the importance of our proposed technique. Our study is structured in three parts: First we discussed different methods of management and visualization of temporal data, then we showed the history and current state of timeline visualization and finally we proposed a timeline visualization technique.