Abstract. Given the number of available genomic DNA, one now faces the task of identifying the functional parts of such raw sequence data, like the protein-coding regions. The gene prediction problem can be addressed in several ways. The most recently methods make use of the similarities between regions of two unannotated genomic sequences in order to find their genes. In this paper we present ...

Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodol...

Motivation: The annotation of the Arabidopsis thaliana genome remains a problem in terms of time and quality. To improve the annotation process, we want to choose the most appropriate tools to use inside a computer-assisted annotation platform. We therefore need evaluation of prediction programs with Arabidopsis sequences containing multiple genes. Results: We have developed AraSet, a data set ...

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Genome sequencing efforts will soon generate hundreds of millions of bases of human genomic DNA containing thousands of novel genes. In the past year, the accuracy of computational gene-finding methods has improved significantly, to the point where a reasonable approximation of the gene structures within an extended genomic region can often be predicted in advance of more detailed experimental ...

Using primary tumor gene expression has been shown to have the ability of finding metastasis-driving gene markers for prediction of breast cancer recurrence (BCR). However, there are some difficulties associated with analysis of microarray data, which led to poor predictive power and inconsistency of previously introduced gene signatures. In this study, a hybrid method was proposed for identify...

All three approaches share a number of common properties, which we list before going on to explore their differences: • Most algorithms locate protein-coding regions as opposed to whole genes. Nevertheless “gene prediction” remains standard nomenclature. • There is a significant amount of application-specific knowledge going into these designs. Of the systems referenced here only VEIL comes clo...

Considerable research eeort has been directed in recent years toward the problem of com-putationally identifying genes in DNA sequences. A fundamental component of a gene-nding system is a predictor which, when given a window of DNA sequence data, predicts whether or not it codes for protein product. In this paper we propose that mistake-driven, multiplicative-weight-update learning algorithms ...