BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

3275 related articles for article (PubMed ID: 34750410)

  • 1. Machine learning random forest for predicting oncosomatic variant NGS analysis.
    Pellegrino E; Jacques C; Beaufils N; Nanni I; Carlioz A; Metellus P; Ouafik L
    Sci Rep; 2021 Nov; 11(1):21820. PubMed ID: 34750410
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.
    Spinella JF; Mehanna P; Vidal R; Saillour V; Cassart P; Richer C; Ouimet M; Healy J; Sinnett D
    BMC Genomics; 2016 Nov; 17(1):912. PubMed ID: 27842494
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.
    van den Akker J; Mishne G; Zimmer AD; Zhou AY
    BMC Genomics; 2018 Apr; 19(1):263. PubMed ID: 29665779
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Cruxome: a powerful tool for annotating, interpreting and reporting genetic variants.
    Han Q; Yang Y; Wu S; Liao Y; Zhang S; Liang H; Cram DS; Zhang Y
    BMC Genomics; 2021 Jun; 22(1):407. PubMed ID: 34082700
    [TBL] [Abstract][Full Text] [Related]  

  • 5. ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.
    Li J; Jew B; Zhan L; Hwang S; Coppola G; Freimer NB; Sul JH
    PLoS Comput Biol; 2019 Dec; 15(12):e1007556. PubMed ID: 31851693
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.
    Marceddu G; Dallavilla T; Guerri G; Zulian A; Marinelli C; Bertelli M
    Eur Rev Med Pharmacol Sci; 2019 Sep; 23(18):8139-8147. PubMed ID: 31599443
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.
    Wu C; Zhao X; Welsh M; Costello K; Cao K; Abou Tayoun A; Li M; Sarmady M
    Clin Chem; 2020 Jan; 66(1):239-246. PubMed ID: 31672855
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity.
    Pellegrino E; Camilla C; Abbou N; Beaufils N; Pissier C; Gabert J; Nanni-Metellus I; Ouafik L
    Bioengineering (Basel); 2023 Jun; 10(7):. PubMed ID: 37508780
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Accuracy and reproducibility of somatic point mutation calling in clinical-type targeted sequencing data.
    Karimnezhad A; Palidwor GA; Thavorn K; Stewart DJ; Campbell PA; Lo B; Perkins TJ
    BMC Med Genomics; 2020 Oct; 13(1):156. PubMed ID: 33059707
    [TBL] [Abstract][Full Text] [Related]  

  • 10. CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.
    Zhao Y; Pan Z; Namburi S; Pattison A; Posner A; Balachander S; Paisie CA; Reddi HV; Rueter J; Gill AJ; Fox S; Raghav KPS; Flynn WF; Tothill RW; Li S; Karuturi RKM; George J
    EBioMedicine; 2020 Nov; 61():103030. PubMed ID: 33039710
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.
    Chen ML; Doddi A; Royer J; Freschi L; Schito M; Ezewudo M; Kohane IS; Beam A; Farhat M
    EBioMedicine; 2019 May; 43():356-369. PubMed ID: 31047860
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.
    Sami A; El-Metwally S; Rashad MZ
    BMC Bioinformatics; 2024 Feb; 25(1):61. PubMed ID: 38321434
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning models for accurate prioritization of variants of uncertain significance.
    Mahecha D; Nuñez H; Lattig MC; Duitama J
    Hum Mutat; 2022 Apr; 43(4):449-460. PubMed ID: 35143088
    [TBL] [Abstract][Full Text] [Related]  

  • 14. GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS.
    Ravasio V; Ritelli M; Legati A; Giacopuzzi E
    Bioinformatics; 2018 Sep; 34(17):3038-3040. PubMed ID: 29668842
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Validation of a Customized Bioinformatics Pipeline for a Clinical Next-Generation Sequencing Test Targeting Solid Tumor-Associated Variants.
    Schneider T; Smith GH; Rossi MR; Hill CE; Zhang L
    J Mol Diagn; 2018 May; 20(3):355-365. PubMed ID: 29471113
    [TBL] [Abstract][Full Text] [Related]  

  • 16. ERASE-Seq: Leveraging replicate measurements to enhance ultralow frequency variant detection in NGS data.
    Kamps-Hughes N; McUsic A; Kurihara L; Harkins TT; Pal P; Ray C; Ionescu-Zanetti C
    PLoS One; 2018; 13(4):e0195272. PubMed ID: 29630678
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Customisation of the exome data analysis pipeline using a combinatorial approach.
    Pattnaik S; Vaidyanathan S; Pooja DG; Deepak S; Panda B
    PLoS One; 2012; 7(1):e30080. PubMed ID: 22238694
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Detection of Circulating Tumor DNA with a Single-Molecule Sequencing Analysis Validated for Targeted and Immunotherapy Selection.
    Atkins A; Gupta P; Zhang BM; Tsai WS; Lucas J; Javey M; Vora A; Mei R
    Mol Diagn Ther; 2019 Aug; 23(4):521-535. PubMed ID: 31209714
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels.
    Zanti M; Michailidou K; Loizidou MA; Machattou C; Pirpa P; Christodoulou K; Spyrou GM; Kyriacou K; Hadjisavvas A
    BMC Bioinformatics; 2021 Apr; 22(1):218. PubMed ID: 33910496
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants.
    Hao Y; Xuei X; Li L; Nakshatri H; Edenberg HJ; Liu Y
    J Comput Biol; 2017 Jul; 24(7):637-646. PubMed ID: 28541743
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 164.