BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

201 related articles for article (PubMed ID: 31729414)

  • 1. CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.
    Lee K; Jeong HO; Lee S; Jeong WK
    Sci Rep; 2019 Nov; 9(1):16927. PubMed ID: 31729414
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. A deep learning-based multi-model ensemble method for cancer prediction.
    Xiao Y; Wu J; Lin Z; Zhao X
    Comput Methods Programs Biomed; 2018 Jan; 153():1-9. PubMed ID: 29157442
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluating the Predictability of Cancer Types from 536 Somatic Mutations: A New Dataset.
    Dehkharghanian T; Rahnamayan S; Tizhoosh HR
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():5308-5311. PubMed ID: 33019182
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Reviewing ensemble classification methods in breast cancer.
    Hosni M; Abnane I; Idri A; Carrillo de Gea JM; Fernández Alemán JL
    Comput Methods Programs Biomed; 2019 Aug; 177():89-112. PubMed ID: 31319964
    [TBL] [Abstract][Full Text] [Related]  

  • 7. NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.
    Anzar I; Sverchkova A; Stratford R; Clancy T
    BMC Med Genomics; 2019 May; 12(1):63. PubMed ID: 31096972
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A pan-cancer somatic mutation embedding using autoencoders.
    Palazzo M; Beauseroy P; Yankilevich P
    BMC Bioinformatics; 2019 Dec; 20(1):655. PubMed ID: 31829157
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations.
    Agajanian S; Oluyemi O; Verkhivker GM
    Front Mol Biosci; 2019; 6():44. PubMed ID: 31245384
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A feasibility study of returning clinically actionable somatic genomic alterations identified in a research laboratory.
    Arango NP; Brusco L; Mills Shaw KR; Chen K; Eterovic AK; Holla V; Johnson A; Litzenburger B; Khotskaya YB; Sanchez N; Bailey A; Zheng X; Horombe C; Kopetz S; Farhangfar CJ; Routbort M; Broaddus R; Bernstam EV; Mendelsohn J; Mills GB; Meric-Bernstam F
    Oncotarget; 2017 Jun; 8(26):41806-41814. PubMed ID: 28415679
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Deep convolutional neural networks for accurate somatic mutation detection.
    Sahraeian SME; Liu R; Lau B; Podesta K; Mohiyuddin M; Lam HYK
    Nat Commun; 2019 Mar; 10(1):1041. PubMed ID: 30833567
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.
    Vural S; Wang X; Guda C
    BMC Syst Biol; 2016 Aug; 10 Suppl 3(Suppl 3):62. PubMed ID: 27587275
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Exploiting ensemble learning to improve prediction of phospholipidosis inducing potential.
    Nath A; Sahu GK
    J Theor Biol; 2019 Oct; 479():37-47. PubMed ID: 31310757
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.
    Fang LT
    Methods Mol Biol; 2020; 2120():47-70. PubMed ID: 32124311
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.
    Sun JX; He Y; Sanford E; Montesion M; Frampton GM; Vignot S; Soria JC; Ross JS; Miller VA; Stephens PJ; Lipson D; Yelensky R
    PLoS Comput Biol; 2018 Feb; 14(2):e1005965. PubMed ID: 29415044
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Optimisation of cancer classification by machine learning generates an enriched list of candidate drug targets and biomarkers.
    Ramroach S; Joshi A; John M
    Mol Omics; 2020 Apr; 16(2):113-125. PubMed ID: 32095794
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep learning of mutation-gene-drug relations from the literature.
    Lee K; Kim B; Choi Y; Kim S; Shin W; Lee S; Park S; Kim S; Tan AC; Kang J
    BMC Bioinformatics; 2018 Jan; 19(1):21. PubMed ID: 29368597
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network.
    Elgin Christo VR; Khanna Nehemiah H; Minu B; Kannan A
    Comput Math Methods Med; 2019; 2019():7398307. PubMed ID: 31662787
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.