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Sparse genomic structural variant detection: Exploiting parent-child relatedness for signal recovery.

, , , , and . SSP, page 1-5. IEEE, (2016)

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A Neural Network Approach for Anomaly Detection in Genomic Signals., , , and . APSIPA, page 968-971. IEEE, (2020)Related Inference: A Supervised Learning Approach to Detect Signal Variation in Genome Data., , , and . EUSIPCO, page 1215-1219. IEEE, (2020)Predicting Novel and Inherited Variants in Parent-Child Trios., , , and . MeMeA, page 1-6. IEEE, (2019)A Non-iterative Parallelizable Eigenbasis Algorithm for Johnson Graphs., , , , and . CoRR, (2018)Detecting novel genomic structural variants through negative binomial optimization., , , and . ACSSC, page 511-515. IEEE, (2020)Detecting Novel Structural Variants In Genomes By Leveraging Parent-Child Relatedness., , , and . BIBM, page 943-950. IEEE Computer Society, (2018)Genomic Signal Processing for Variant Detection in Diploid Parent-Child Trios., , , and . EUSIPCO, page 1318-1322. 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)Sparse diploid spatial biosignal recovery for genomic variation detection., , , , , , , , and . MeMeA, page 275-280. IEEE, (2017)