Toxicity testing in the 21st century: progress in the past decade and future perspectives
D Krewski, ME Andersen, MG Tyshenko… - Archives of …, 2020 - Springer
Advances in the biological sciences have led to an ongoing paradigm shift in toxicity testing
based on expanded application of high-throughput in vitro screening and in silico methods …
based on expanded application of high-throughput in vitro screening and in silico methods …
Computational deconvolution of transcriptomics data from mixed cell populations
Gene expression analyses of bulk tissues often ignore cell type composition as an important
confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review …
confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review …
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …
statistics are used, and the area under the receiver operating characteristic curve (ROC …
Development of a clinical polygenic risk score assay and reporting workflow
Implementation of polygenic risk scores (PRS) may improve disease prevention and
management but poses several challenges: the construction of clinically valid assays …
management but poses several challenges: the construction of clinically valid assays …
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Data imbalance is frequently encountered in biomedical applications. Resampling
techniques can be used in binary classification to tackle this issue. However such solutions …
techniques can be used in binary classification to tackle this issue. However such solutions …
Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods
The expression microarray is a frequently used approach to study gene expression on a
genome-wide scale. However, the data produced by the thousands of microarray studies …
genome-wide scale. However, the data produced by the thousands of microarray studies …
Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images
In the application of deep learning on optical coherence tomography (OCT) data, it is
common to train classification networks using 2D images originating from volumetric data …
common to train classification networks using 2D images originating from volumetric data …
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice
between several computational methods for performing data analyses. Benchmarking …
between several computational methods for performing data analyses. Benchmarking …
Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer
In many microarray studies, classifiers have been constructed based on gene signatures to
predict clinical outcomes for various cancer sufferers. However, signatures originating from …
predict clinical outcomes for various cancer sufferers. However, signatures originating from …
Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster
Background A generally accepted approach to the analysis of RNA-Seq read count data
does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic …
does not yet exist. We sequenced the mRNA of 726 individuals from the Drosophila Genetic …