The Advances in Cancer Survival Prediction by Gene Expression Data; Using Machine Learning Approaches

2017 ◽  
Vol 2 (1) ◽  
pp. 136-136
Author(s):  
Marjan Ghazisaeedi ◽  
Azadeh Bashiri
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Cancer Survival ◽  
Survival Prediction ◽  
Learning Approaches ◽  
Expression Data

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Leukemia ◽  
2021 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Marta Sonia González Pérez ◽  
José Ángel Díaz Arias ◽  
Beatriz Antelo Rodríguez ◽  
Natalia Alonso Vence ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Multiple Myeloma ◽  
Gene Expression Data ◽  
Survival Prediction ◽  
Expression Data ◽  
Learning Models ◽  
Treatment Optimization ◽  
Machine Learning Models

scholarly journals Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

2021 ◽  
Vol 11 ◽  
Author(s):  
Adrián Mosquera Orgueira ◽  
Andrés Peleteiro Raíndo ◽  
Miguel Cid López ◽  
José Ángel Díaz Arias ◽  
Marta Sonia González Pérez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
High Risk ◽  
Gene Expression Data ◽  
Predictive Accuracy ◽  
Survival Prediction ◽  
Expression Data ◽  
Learning Tools ◽  
Molecular Alterations ◽  
Risk Of Death

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.

scholarly journals Machine learning approaches to predict lupus disease activity from gene expression data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Brian Kegerreis ◽  
Michelle D. Catalina ◽  
Prathyusha Bachali ◽  
Nicholas S. Geraci ◽  
Adam C. Labonte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Disease Activity ◽  
Gene Expression Data ◽  
Learning Approaches ◽  
Expression Data ◽  
Lupus Disease Activity

scholarly journals Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230536
Author(s):  
Guillermo López-García ◽  
José M. Jerez ◽  
Leonardo Franco ◽  
Francisco J. Veredas
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Gene Expression Data ◽  
Cancer Survival ◽  
Survival Prediction ◽  
Expression Data

scholarly journals A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency

Cell Cycle ◽  
2018 ◽  
Vol 17 (4) ◽  
pp. 486-491 ◽  
Author(s):  
Nicolas Borisov ◽  
Victor Tkachev ◽  
Maria Suntsova ◽  
Olga Kovalchuk ◽  
Alex Zhavoronkov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cancer Patients ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Data Transfer ◽  
Expression Data ◽  
Drug Efficiency

scholarly journals Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Shuhei Kaneko ◽  
Akihiro Hirakawa ◽  
Chikuma Hamada
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Gene Expression Data ◽  
Prediction Models ◽  
Mixture Distribution ◽  
Tuning Parameter ◽  
Survival Prediction ◽  
Expression Data ◽  
True Positive ◽  
False Negatives

In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient’s survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP) genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.

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