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We consider the problem of identifying motifs, recurring or conserved patterns, in the biological sequence data sets. To solve this task, we present a new deterministic algorithm for finding patterns that are embedded as exact or inexact instances in all or most of the input strings. The proposed algorithm (1) improves search efficiency compared to existing(More)
In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming(More)
We present a new family of linear time algorithms for string comparison with mismatches under the string kernels framework. Based on sufficient statistics, our algorithms improve theoretical complexity bounds of existing approaches while scaling well in sequence alphabet size, the number of allowed mismatches and the size of the dataset. In particular, on(More)
UNLABELLED We implemented a high-throughput identification pipeline for promoter interacting enhancer element to streamline the workflow from mapping raw Hi-C reads, identifying DNA-DNA interacting fragments with high confidence and quality control, detecting histone modifications and DNase hypersensitive enrichments in putative enhancer elements, to(More)
Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of labeled words. To tackle this issue, we propose a semi-supervised approach to improve the sequence labeling procedure in IE through a class of algorithms with {\em self-learned(More)
Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein(More)
In this work we present a new string similarity feature , the sparse spatial sample (SSS). An SSS is a set of short substrings at specific spatial displacements contained in the original string. Using this feature we induce the SSS kernel (SSSK) which measures the agreement in the SSS content between pairs of strings. The SSSK yields better prediction(More)