| scholarly article | Q13442814 |
| P2093 | author name string | Fengju Song | |
| Fanyu Meng | |||
| Ge Yan | |||
| Baoshan Ma | |||
| Yao Geng | |||
| P2860 | cites work | Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma. | Q39319196 |
| Cancer statistics for African Americans, 2016: Progress and opportunities in reducing racial disparities | Q39972544 | ||
| Abnormal gene expression and gene fusion in lung adenocarcinoma with high-throughput RNA sequencing | Q42238571 | ||
| Survival prediction of stage I lung adenocarcinomas by expression of 10 genes | Q42585784 | ||
| deML: robust demultiplexing of Illumina sequences using a likelihood-based approach | Q42943342 | ||
| Gene expression signature of non-involved lung tissue associated with survival in lung adenocarcinoma patients. | Q43639835 | ||
| Novel head and neck cancer survival analysis approach: random survival forests versus Cox proportional hazards regression | Q44129302 | ||
| A five-gene signature and clinical outcome in non-small-cell lung cancer | Q44736779 | ||
| A likelihood-based approach to capture-recapture estimation of demographic parameters under the robust design | Q44863291 | ||
| Random Survival Forests | Q45348477 | ||
| A seven-gene signature predicts overall survival of patients with colorectal cancer | Q47103045 | ||
| LncRNA Expression Signature in Prediction of the Prognosis of Lung Adenocarcinoma | Q47215076 | ||
| Bone, muscle, and metabolic parameters predict survival in patients with synchronous bone metastases from lung cancers | Q47656071 | ||
| The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification | Q47945133 | ||
| Differential microRNA expression profiles between young and old lung adenocarcinoma patients. | Q55243832 | ||
| Three-Gene Prognostic Classifier for Early-Stage Non–Small-Cell Lung Cancer | Q57272624 | ||
| Robust quantitative trait association tests in the parent-offspring triad design: conditional likelihood-based approaches | Q83272945 | ||
| The rise of regulatory RNA | Q22121998 | ||
| Advanced Applications of RNA Sequencing and Challenges | Q27013028 | ||
| DAVID: Database for Annotation, Visualization, and Integrated Discovery | Q27499374 | ||
| Global cancer statistics, 2012 | Q27860501 | ||
| Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources | Q27860739 | ||
| Evolution and functions of long noncoding RNAs | Q29614331 | ||
| Prognostic gene signatures for non-small-cell lung cancer | Q30485836 | ||
| Assessing the clinical utility of cancer genomic and proteomic data across tumor types. | Q30487077 | ||
| Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis | Q31126519 | ||
| Use of the concordance index for predictors of censored survival data. | Q31147068 | ||
| Risk factors and prognosis of pulmonary embolism in patients with lung cancer | Q33606182 | ||
| Random survival forests for competing risks | Q34235347 | ||
| Development and validation of a prognostic gene-expression signature for lung adenocarcinoma | Q34412292 | ||
| Prognostic markers in lung cancer: is it ready for prime time? | Q35200555 | ||
| survcomp: an R/Bioconductor package for performance assessment and comparison of survival models | Q35526651 | ||
| Systematic transcriptome analysis reveals tumor-specific isoforms for ovarian cancer diagnosis and therapy | Q35740435 | ||
| Prognostic and predictive biomarkers in lung cancer. A review | Q38178187 | ||
| A prognostic 4-lncRNA expression signature for lung squamous cell carcinoma | Q38611422 | ||
| Experimental study of peripheral-blood pro-surfactant protein B for screening non-small cell lung cancer | Q38632208 | ||
| Machine Learning in Medicine | Q38634420 | ||
| A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data. | Q38654788 | ||
| microRNA and Lung Cancer. | Q38667020 | ||
| Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non-Small Cell Lung Cancer | Q38688789 | ||
| Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction | Q38719714 | ||
| Machine Learning for Medical Imaging | Q38756151 | ||
| Development of a novel prognostic signature of long non-coding RNAs in lung adenocarcinoma | Q38837312 | ||
| Cancer treatment and survivorship statistics, 2016. | Q38859236 | ||
| The RHOV gene is overexpressed in human non-small cell lung cancer | Q39037564 | ||
| Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit | Q39210607 | ||
| P4510 | describes a project that uses | Cytoscape | Q3699942 |
| P433 | issue | 5 | |
| P921 | main subject | machine learning | Q2539 |
| adenocarcinoma | Q356033 | ||
| biomarker | Q864574 | ||
| P304 | page(s) | 1288-1298 | |
| P577 | publication date | 2020-01-01 | |
| P1433 | published in | Journal of Cancer | Q6294901 |
| P1476 | title | Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method | |
| P478 | volume | 11 |
| Q98475324 | A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma |
| Q106520270 | RHOV GTPase cycle |
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