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More and more proteins have been observed to display functions through intrinsic disorder. Such structurally flexible regions are shown to play important roles in biological processes and are estimated to be abundant in eukaryotic proteomes. Previous studies largely use evolutionary information and combinations of physicochemical properties of amino acids to detect disordered regions from primary sequences. In our recent work DisPSSMP, it is demonstrated that the accuracy of protein disorder prediction is greatly improved if the disorder propensity of amino acids is considered when generating the condensed PSSM features. This work aims to investigate how the information of secondary structure can be incorporated in DisPSSMP to enhance the predicting power. We propose a new representation of secondary structure information and compare it with three naïve representations that have been discussed or employed in some related works. The experimental results reveal that the refined information from secondary structure prediction is of benefit to this problem. [ABSTRACT FROM AUTHOR]

Discovering protein structural signatures directly from their primary information is a challenging task, because the residues associated with a functional motif are not necessarily clustered in one region of the sequence. This work proposes an algorithm that aims to discover conserved sequential blocks interleaved by large irregular gaps from a set of unaligned biological sequences. Different from the previous works that employ only one type of constraint on gap flexibility, we propose using combination of intra- and inter-block gap constraints to discover longer patterns with larger irregular gaps. The smaller flexible intra-block gap constraint is used to relax the restriction in local motif blocks but still keep them compact, and the larger flexible inter-block gap constraint is proposed to allow longer irregular gaps between compact motif blocks. Using two types of gap constraints for different purposes improves the efficiency of mining process while keeping high accuracy of mining results. The efficiency of the algorithm also helps to identify functional motifs that are conserved in only a small subset of the input sequences. [ABSTRACT FROM AUTHOR]

One of the main challenges in the design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this challenge is to incorporate a clustering algorithm that operates incrementally. Another desirable feature of clustering algorithms is that a clustering dendrogram is generated. This feature is crucial for many applications in biological, social, and behavior studies, due to the need to construct taxonomies. This paper presents the GRIN algorithm, an incremental hierarchical clustering algorithm for numerical data sets based on gravity theory in physics. The GRIN algorithm delivers favorite clustering quality and generally features O(n) time complexity. One main factor that makes the GRIN algorithm be able to deliver favorite clustering quality is that the optimal parameters settings in the GRIN algorithm are not sensitive to the distribution of the data set. On the other hand, many modern clustering algorithms suffer unreliable or poor clustering quality when the data set contains highly skewed local distributions so that no optimal values can be found for some global parameters. This paper also reports the experiments conducted to study the characteristics of the GRIN algorithm. [ABSTRACT FROM AUTHOR]