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Detecting Novel Structural Variants In Genomes By Leveraging Parent-Child Relatedness.

, , , and . BIBM, page 943-950. IEEE Computer Society, (2018)

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Related Inference: A Supervised Learning Approach to Detect Signal Variation in Genome Data., , , and . EUSIPCO, page 1215-1219. IEEE, (2020)A Neural Network Approach for Anomaly Detection in Genomic Signals., , , and . APSIPA, page 968-971. IEEE, (2020)Structural Variant Prediction in Extended Pedigrees Through Sparse Negative Binomial Genome Signal Recovery., , and . EMBC, page 1311-1314. IEEE, (2018)Sparse genomic structural variant detection: Exploiting parent-child relatedness for signal recovery., , , , and . SSP, page 1-5. IEEE, (2016)A field guide to cultivating computational biology., , , , , , , , , and 4 other author(s). CoRR, (2021)Negative Binomial Optimization for Biomedical Structural Variant Signal Reconstruction., , and . ICASSP, page 906-910. IEEE, (2018)Novel structural variant genome detection in extended pedigrees through negative binomial optimization., , , and . ACSCC, page 563-567. IEEE, (2021)Sparse diploid spatial biosignal recovery for genomic variation detection., , , , , , , , and . MeMeA, page 275-280. IEEE, (2017)Nonconvex regularization for sparse genomic variant signal detection., , , , , , , , and . MeMeA, page 281-286. IEEE, (2017)Sparse signal recovery methods for variant detection in next-generation sequencing data., , , , and . ICASSP, page 864-868. IEEE, (2016)