• Machine Learning Approach in Mutation Testing🔍
• Machine Learning Classification and Structure–Functional Analysis ...🔍
• A new machine learning method for cancer mutation analysis🔍
• Machine Learning Approaches on High Throughput NGS Data to ...🔍
• Identifying novel oncogenes🔍
• Novel Machine Learning Blood Test Detects Cancers with Genome ...🔍
• Novel machine learning methods for cancer sequencing analysis🔍
• A novel machine learning approach 🔍

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.

Machine Learning Approach in Mutation Testing - SpringerLink

It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This ...

Machine Learning Classification and Structure–Functional Analysis ...

In this study, we developed two cancer-specific machine learning classifiers for prediction of driver mutations in cancer-associated genes ...

A new machine learning method for cancer mutation analysis - OUCI

By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules.

Machine Learning Approaches on High Throughput NGS Data to ...

Regression techniques have a broad range of applications in bioinformatics. These methods have been widely adopted in normalization procedures of RNA-seq data, ...

Identifying novel oncogenes: A machine learning approach

Implementing computational platforms to determine the pathogenecity associated with the SNPs can provide a probable solution to this problem. To ...

Novel Machine Learning Blood Test Detects Cancers with Genome ...

Finally, a machine learning model trained to identify changes in cancer and non-cancer mutation frequencies in different regions of the genome ...

Novel machine learning methods for cancer sequencing analysis

In this thesis, I developed three novel approaches based on machine learning for the analysis of \correction{cancer evolution} using genomics data.

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 ML Approach for Computing Missing Sift, Provean, and ...

... function. This study proposes a novel machine learning (ML) approach to calculate missing values in the tp53 database for three computational methods: SIFT ...

Development of Integrated Machine Learning and Data Science ...

... Analysis Tool). (Parthiban et al., 2006, 2007). This approach was successfully adopted for the energetic analysis of cancer mutation hotspots (Dixit et al ...

Novel machine learning blood test detects cancers with genome ...

Finally, a machine learning model trained to identify changes in cancer and non-cancer mutation frequencies in different regions of the genome ...

Predicting Loss-of-Function Impact of Genetic Mutations: A Machine ...

Thus, this paper's aims were to train machine learning models on the attributes of a genetic mutation to predict LoFtool scores (which measure a ...

Gevaert Lab | Stanford Medicine

We develop novel machine learning approaches that digest multi-omics, multi-modal or multi-scale data. Previously we pioneered data fusion work using ...

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using ...

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method ... The mutation features analyzed in our study were generated by a ...

Classification of Cancer Primary Sites Using Machine Learning and ...

The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine learning. Here, ...

A novel machine learning based method to detect homozygous ...

WES results were concor- dant with the cfDNA assay for a subset of mutations from the targeted gene panel for. SNVs (sensitivity = 0.23) and indels (sensitivity ...

Deep-GenMut: Automated genetic mutation classification in oncology

are inherently unstructured, requiring techniques that can bring meaning and structure to be utilized by machine learning algorithms.

A new machine learning method for cancer mutation analysis - DiVA

A new machine learning method for cancer mutation analysis ... Here we propose a machine learning method to study ... We also proposed a novel clustering method to ...

Novel machine learning tool predicts gain- and loss-of-function ...

Unlike current methods that predominantly focus on loss of function, LoGoFunc distinguishes among different types of harmful mutations, offering ...