- A Novel Machine Learning Method for Mutational Analysis to ...🔍
- Uncovering driver genes in breast cancer through an innovative ...🔍
- A new machine learning method for cancer mutation analysis🔍
- Using machine learning to predict the effects and consequences of ...🔍
- A novel machine learning|based approach for the computational ...🔍
- A novel machine|learning|derived genetic score correlates ...🔍
- Predicting mutational function using machine learning🔍
- A novel machine learning approach 🔍
A Novel Machine Learning Method for Mutational Analysis to ...
A Novel Machine Learning Method for Mutational Analysis to ...
A Novel Machine Learning Method for Mutational Analysis to Identifying Driver Genes in Breast Cancer. Golnaz Taheri, Mahnaz Habibi. doi: https://doi.org ...
(PDF) A Novel Machine Learning Method for Mutational Analysis to ...
In this work, we proposed a novel machine-learning method to study the functionality of genes in the networks derived from mutation associations ...
A Novel Machine Learning Method for Mutational Analysis to ... - OUCI
The statistical power of the clinical study is considerably increased when evaluating the network rather than just the effects of a single gene. The proposed ...
Uncovering driver genes in breast cancer through an innovative ...
In this work, as a retrospective study, we used TCGA data, which is gathered from breast cancer patients. We introduced a new machine-learning approach to ...
A new machine learning method for cancer mutation analysis - NCBI
Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine ...
A new machine learning method for cancer mutation analysis - PLOS
Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations.
Using machine learning to predict the effects and consequences of ...
Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to ...
A novel machine learning-based approach for the computational ...
This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would ...
A novel machine-learning-derived genetic score correlates ... - Nature
Although NGS technologies have produced a deluge of cancer genomics data, it is challenging to accurately predict disease outcome from these ...
EnzyACT: A Novel Deep Learning Method to Predict the Impacts of ...
(17) SCANEER is a predicted method that uses sequence coevolution to analyze the effect of single-point mutation on enzyme activity, and limited ...
Predicting mutational function using machine learning - ScienceDirect
To address the challenge above, computational methods become an ideal approach. Machine learning (ML; Box 1) models can be developed and validated on a limited ...
A novel machine learning approach (svmSomatic) to distinguish ...
Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations ...
Multiomics and machine-learning identify novel transcriptional and ...
Catanese et al. use a multiomics approach to study ALS at transcriptomic, epigenetic and genetic levels. They identify a mutation-independent disease signa.
A novel machine learning approach (svmSomatic) to distinguish ...
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants ( ...
A novel machine learning approach (svmSomatic) to distinguish ...
This work detects somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing ...
Mutation-Attention (MuAt): deep representation learning of somatic ...
predicted tumour types with a support vector machine using information on somatically mutated genes resulting in 49% accuracy in 28 tumour types ...
A new machine learning method for cancer mutation - ProQuest
Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to ...
Machine learning optimized DriverDetect software for high precision ...
We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver ...
Explainable Machine Learning Model to Prediction EGFR Mutation ...
The machine learning algorithms (MLAs) included logistic regression (LR), random forest (RF), LightGBM, support vector machine (SVM), multi- ...
RFcaller: a machine learning approach combined with read-level ...
Here we present RFcaller, a pipeline based on machine learning algorithms, for the detection of somatic mutations in tumor–normal paired samples.