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Data generated from microarray experiments often suffer from missing values. As most downstream analyses need full matrices as input, these missing values have to be estimated. Bayesian principal component analysis (BPCA) is a well-known microarray missing value estimation method, but its performance is not satisfactory on datasets with strong local(More)
The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA)(More)
In this study, we used a wide spectrum of bioinformatics techniques to evaluate the extent of intrinsic disorder in the complete proteomes of genotypes of four human dengue virus (DENV), to analyze the peculiarities of disorder distribution within individual DENV proteins, and to establish potential roles for the structural disorder with respect to their(More)
MOTIVATION Disordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build(More)
Recent analyses indicated that autophagy can be regulated via some nuclear transcriptional networks and many important players in the autophagy and other forms of programmed cell death are known to be intrinsically disordered. To this end, we analyzed similarities and differences in the intrinsic disorder distribution of nuclear and non-nuclear proteins(More)
l1-norm minimization was utilized in the imputation of microarray missing values, which is an important procedure in bioinformatics experiments. Two l1 approaches, based on the framework of local least squares (LLS) and iterative biclusterbased least squares (bicluster-iLLS) respectively, were employed. Imputed datasets of the l1 approaches were compared(More)
Secondary structure of proteins refers to local and repetitive conformations, such as α-helices and β-strands, which occur in protein structures. Computational prediction of secondary structure from protein sequences has a long history with three generations of predictive methods. This unit summarizes several recent third-generation predictors. We discuss(More)
Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered(More)
l<sub>1</sub>-norm minimization was utilized in the imputation of microarray missing values, which is an important procedure in bioinformatics experiments. Two l<sub>1</sub> approaches, based on the framework of local least squares (LLS) and iterative bicluster-based least squares (bicluster-iLLS) respectively, were employed. Imputed datasets of the(More)
Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides(More)
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