Lana X. Garmire

The share of this author’s assessed papers carrying a ⚑ flag. A concentration is a prompt for expert review — never, on its own, a determination about the person.

Collaborators, sized by shared output and coloured by their own reproducibility (green = high, red = low). Click a node to open their card. A pattern is a prompt for review, never a determination.

How this author’s assessed papers reproduced — the outcome of reproduction attempts, not a judgement of the person. Coverage is partial and grows over time.

Assessed papers (1)

• DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.
  2021 L1 84/100

1 assessed by us (1 reproduced) · 24 not yet assessed — every PubMed paper on record, linked below.

• Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies ↗

  UNC Libraries · 2026
  not yet assessed
• Supplementary File S1: Description of normalization methods used from Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer ↗

  2023
  not yet assessed
• Supplementary Table S4: Performance of models constructed with one omic only from Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer ↗

  2023
  not yet assessed
• Supplementary File S1: Description of normalization methods used from Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer ↗

  2023
  not yet assessed
• Supplementary Table S4: Performance of models constructed with one omic only from Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer ↗

  2023
  not yet assessed
• Supplementary Table S2: ANOVA-based top features from three omics: mRNA, miR, and methylation from Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer ↗

  2023
  not yet assessed
• Computational Methods for Single-Cell Multi-Omics Integration and Alignment ↗

  arXiv (Cornell University) · 2022
  not yet assessed
• DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

  Genome Medicine · 2021 · PMID 34261540
  L1 84/100
• Additional file 6 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Additional file 2 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Additional file 1 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Multi-omic and survival datasets used for "DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data" ↗

  Figshare · 2021
  not yet assessed
• Additional file 5 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Additional file 8 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Additional file 3 of DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  Figshare · 2021
  not yet assessed
• Strategies to integrate multi-omics data for patient survival prediction ↗

  arXiv (Cornell University) · 2020
  not yet assessed
• Network-Based Matching of Patients and Targeted Therapies for Precision Oncology ↗

  2019 · PMID 31797633
  not yet assessed
• DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data ↗

  medRxiv · 2019
  not yet assessed
• Network-Based Matching of Patients and Targeted Therapies for Precision Oncology ^* ↗

  bioRxiv (Cold Spring Harbor Laboratory) · 2019
  not yet assessed
• Enter the Matrix: Factorization Uncovers Knowledge from Omics ↗

  Trends in Genetics · 2018 · PMID 30143323
  not yet assessed
• More Is Better: Recent Progress in Multi-Omics Data Integration Methods ↗

  Frontiers in Genetics · 2017 · PMID 28670325
  not yet assessed
• Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization ↗

  2016
  not yet assessed
• Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization ↗

  2016
  not yet assessed
• Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization ↗

  2016
  not yet assessed
• A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer ↗

  PLoS Computational Biology · 2014 · PMID 25233347
  not yet assessed