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Antibiotics have been given to food animals for several decades as a performance enhancer. For nearly as long there has been a concern that using these antimicrobials in production animals could lead to bacteria developing resistance to antibiotics and eventually escaping into the human population. While this risk is still undefined, it would be of benefit to minimize the ratio of resistant bacteria...
The use of covariance models for non-coding RNA gene finding is extremely powerful and also extremely computationally demanding. A major reason for the high computational burden of this algorithm is that the search proceeds through every possible start position in the database and every possible sequence length between zero and a user-defined maximum length at every one of these start positions. Furthermore,...
The geographic distribution of species is of interest in making conservation plans, designating biosphere reserves, and in understanding the different range sizes of species. In this paper, an evolutionary algorithm is used to classify species of freshwater crustacean zooplankton as geographically informative or geographically non-informative. Geographically non-informative species tend to have a...
The vast number of expressed sequence tags (ESTs) in public databases provides an important resource for comparative and functional genomics. A variety of methods based on homology search or domain profile search have been developed to functionally annotate protein domains in ESTs. However, these methods either ignore potentially valuable information from the homologues beyond the top N hits, or they...
Protein structure prediction is one of the core research areas in bioinformatics. This paper addresses the protein secondary structure prediction problem for the twilight zone proteins, which are characterized by low, about 25% homology to the sets of known sequences. The commonly used sequence alignment based algorithms fail to provide accurate prediction for sequences of such low homology, and thus...
Many different clustering algorithms have been applied to biological networks, with varying degrees of success. The output of a clustering algorithm may be hard to interpret in biological terms because such networks are often large and highly interconnected, with structural and functional modules overlapping to varying degrees. In this paper we describe an evolutionary network clustering algorithm...
For one to infer the overall structures of gene regulatory networks (GRNs), it is important to identify, for each gene in a GRN, which other genes can affect its expression and how they can affect it. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory tests can be carried out afterwards...
Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of...
Sliding window based methods are relatively often applied in prediction of various aspects related to protein structure. Despite their wide spread use, researchers did not establish a standard related to the size of the window, i.e., window sizes ranging between 7 and 17 residues were used in the past. To this end, this paper performs a computational study based on a probabilistic approach that aims...
Recently, large-scale protein-protein interactions were recovered using the similar two-hybrid system for the model systems. This information allows us to investigate the protein interaction network from a systematic point of view. However, experimentally determined interactions are susceptible to errors. A previous assessment estimated that only ~10% of the interactions can be supported by more than...
The following topics are dealt with: protein-protein interaction; cancer informatics; disease informatics; protein folding; gene networks; biosequence analysis; biochemical modeling; microarray analysis; biological theory; medical informatics; RNA structure; and RNA function
This paper presents a hidden Markov model method (referred as HMM_AA_SA) for the identification of Helix-Turn-Helix (HTH) DNA-binding motifs. The method takes amino acid sequence and predicted solvent accessibility as input. Solvent accessibility of amino acids is predicted from amino acid sequence and discretized into three categories: buried (B), medium (M) and exposed (E). At each state, HMM_AA_SA...
While prisoner's dilemma has frequently been used in studies of animal behavior, past work in the area fails to address the question of differential utility. As animals needs change, their behavior also changes. A hungry animal may be less likely to cooperate than a full one. In this study an abstract species is modelled using an energy balance computation that assigns a state of hungry, full, or...
We propose a new method for collecting information on regulatory elements found by any motif discovery program. We suggest that combining the results of n leave-one-out motif discovery runs provides additional information. By examining motifs found in n - 1 of the sequences and scoring them on the remaining sequence, we overcome some of the issues arising from noisy data to identify more high-quality...
DNA sequence basecalling is commonly regarded as a solved problem, despite significant error rates being reflected in inaccuracies in databases and genome annotations. These errors commonly arise from an inability to sequence through peak height variations in DNA sequencing traces from the Sanger sequencing method. Recent efforts toward improving basecalling accuracy have taken the form of more sophisticated...
Ribonucleic acid (RNA) plays fundamental roles in cellular processes and its structure is directly related to its functions. This paper describes and presents a novel algorithm for RNA secondary structure prediction based on simulated annealing (SA). SA is known to be effective in solving many different types of minimization problems and for finding the global minima in the solution space. Based on...
Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most significantly differentially expressed genes under different categories of conditions, is both computationally and biologically interesting, and has become a central research focus in all studies that use gene expression microarray...
We present a novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of an RNN, while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct...
A systematic statistical process is proposed for cDNA microarray data analysis to counter the potential problems of limited replication and unknown distribution found in some public data sets. The proposed process integrates several existing methods to infer expression patterns of unknown genes. It consists of data normalization, identification of significant genes using a novel within-slide replication...
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