scholarly journals A Classification-Based Machine Learning Approach for the Analysis of Genome-Wide Expression Data

2003 ◽  
Vol 13 (3) ◽  
pp. 503-512 ◽  
Author(s):  
J. Lyons-Weiler
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Expression Data ◽  
Genome Wide ◽  
Machine Learning Approach ◽  
Genome Wide Expression

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2019 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

scholarly journals Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach

2014 ◽  
Vol 8 (S5) ◽  
Author(s):  
Giulietta Minozzi ◽  
Andrea Pedretti ◽  
Stefano Biffani ◽  
Ezequiel Luis Nicolazzi ◽  
Alessandra Stella
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Association Analysis ◽  
Genome Wide Association ◽  
Learning Approach ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
Machine Learning Approach

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2020 ◽  
Vol 66 (6) ◽  
pp. 1117-1134 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

Abstract In vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

scholarly journals A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1562 ◽  
Author(s):  
Maurizio Polano ◽  
Marco Chierici ◽  
Michele Dal Bo ◽  
Davide Gentilini ◽  
Federica Di Cintio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Support Vector ◽  
Learning Approach ◽  
Expression Data ◽  
Machine Learning Approach ◽  
Cancer Types ◽  
Pan Cancer

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

Genome-Wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-Learning Approach

2019 ◽  
Vol 16 (4) ◽  
pp. 1398-1408 ◽  
Author(s):  
Roman Sergeevich Sergeev ◽  
Ivan S. Kavaliou ◽  
Uladzislau V. Sataneuski ◽  
Andrei Gabrielian ◽  
Alex Rosenthal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document