These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

3384 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. Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization.
    Huang YS; Hsu C; Chune YC; Liao IC; Wang H; Lin YL; Hwu WL; Lee NC; Lai F
    JMIR Bioinform Biotechnol; 2022 Sep; 3(1):e37701. PubMed ID: 38935959
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 170.