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Comparative study of whole exome sequencing-based copy number variation detection tools

BMC bioinformatics, 2020-03, Vol.21 (1), p.97-97, Article 97

2020

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DeepTIS: Improved translation initiation site prediction in genomic sequence via a two-stage deep learning model

Digital signal processing, 2021-10, Vol.117, p.103202, Article 103202

2021

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Identification of Molecular Pathway Aberrations in Uterine Serous Carcinoma by Genome-wide Analyses

JNCI : Journal of the National Cancer Institute, 2012-10, Vol.104 (19), p.1503-1513

2012

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FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm

PloS one, 2016-03, Vol.11 (3), p.e0150669-e0150669

2016

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A shortest path-based approach for copy number variation detection from next-generation sequencing data

Frontiers in genetics, 2023-01, Vol.13, p.1084974-1084974

2023

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Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods

Frontiers in genetics, 2021-01, Vol.11, p.632901-632901

2021

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A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model

Complex & intelligent systems, 2024-10, Vol.10 (5), p.7199-7211

2024

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HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

Frontiers in genetics, 2021-06, Vol.12, p.642473-642473

2021

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RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Frontiers in genetics, 2020-11, Vol.11, p.569227-569227

2020

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A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Frontiers in genetics, 2021-01, Vol.11, p.632311-632311

2021

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DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads

Frontiers in genetics, 2020-08, Vol.11, p.924-924

2020

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MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Frontiers in genetics, 2020-05, Vol.11, p.434-434

2020

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Network based stratification of major cancers by integrating somatic mutation and gene expression data

PloS one, 2017-05, Vol.12 (5), p.e0177662-e0177662

2017

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Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

Frontiers in genetics, 2020-11, Vol.11, p.603093-603093

2020

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Analysis of breast cancer subtypes by AP-ISA biclustering

BMC bioinformatics, 2017-11, Vol.18 (1), p.481-481, Article 481

2017

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Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Frontiers in genetics, 2020-04, Vol.11, p.458-458

2020

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Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables

Molecules (Basel, Switzerland), 2019-02, Vol.24 (3), p.631

2019

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dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

IEEE access, 2020, Vol.8, p.27973-27985

2020

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Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

Journal of biomolecular structure & dynamics, 2012-01, Vol.29 (6), p.1138-1146

2012

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WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

Mathematics (Basel), 2022-06, Vol.10 (12), p.2151

2022

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KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data

Frontiers in cell and developmental biology, 2021-12, Vol.9, p.796249-796249

2021

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Comparative analysis of methods for identifying recurrent copy number alterations in cancer

PloS one, 2012-12, Vol.7 (12), p.e52516

2012

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CRSCNV: A Cross-Model-Based Statistical Approach to Detect Copy Number Variations in Sequence Data

IEEE access, 2020, Vol.8, p.2302-2312

2020

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SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

Genes & Genomics, 2019, 41(5), , pp.529-536

2019

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Genome-wide identification of significant aberrations in cancer genome

BMC genomics, 2012-07, Vol.13 (1), p.342-342, Article 342

2012

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Comparative study of whole exome sequencing-based copy number variation detection tools

DeepTIS: Improved translation initiation site prediction in genomic sequence via a two-stage deep learning model

Identification of Molecular Pathway Aberrations in Uterine Serous Carcinoma by Genome-wide Analyses

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm

A shortest path-based approach for copy number variation detection from next-generation sequencing data

Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods

A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data

DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Network based stratification of major cancers by integrating somatic mutation and gene expression data

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data

Analysis of breast cancer subtypes by AP-ISA biclustering

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables

dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data

Comparative analysis of methods for identifying recurrent copy number alterations in cancer

CRSCNV: A Cross-Model-Based Statistical Approach to Detect Copy Number Variations in Sequence Data

SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

Genome-wide identification of significant aberrations in cancer genome

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