15 papers · ranked by Valyu relevance
Maliheh Miri, Mohammad Taghi Sadeghi, Vahid Abootalebi
Sparse representation of signals has achieved satisfactory results in classification applications compared to the conventional methods. Microarray data, which are obtained from monitoring the expression levels of thousands of genes simultaneously, have very high dimensions in relation to the small number of samples.…
Atikul Islam, Tapas Bhadra, Kalyani Mali, Jayant Giri + 3 more
This article basically explores the impact of univariate & multivariate filter-based feature selection methodologies on enhancing the classification performance for the real-life classification problems. Our study considers two univariate filter-based feature selection techniques, namely, Chi-square and Fisher score…
Elena Aramendía Cotillas, Carina Bernardo, Srinivas Veerla, Fredrik Liedberg + 2 more
Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine and transcriptomic profiling has revealed that urothelial carcinoma (UC) is a heterogeneous disease with several distinct molecular subtypes. The Lund Taxonomy classification system for urothelial…
Tay Xin Hui, Tole Sutikno, Shahreen Kasim, Mohd Farhan Md Fudzee + 3 more
The integration of microarray technologies and machine learning methods has become popular in predicting pathological condition of diseases and discovering risk genes. The traditional microarray analysis considers pathways as simple gene sets, treating all genes in the pathway identically while ignoring the pathway…
Samuel Bharti, Nikita Krishnan, Arian Veyssi, Maryam Momeni + 1 more
Microarray data enables biologists to extract differentially expressed genes (DEGs) across multiple phenotypes. While several pipelines and tools exist to perform microarray data analysis, they are targeted to users with moderate to advanced computational understanding and lack an easy-to-use, interactive and dynamic…
Luca Visentin, Luca Munaron, Federico Alessandro Ruffinatti
Tens of thousands of gene expression data sets describing a variety of model organisms in a many different pathophysiological conditions are currently stored in publicly available databases such as Gene Expression Omnibus (GEO) and ArrayExpress. As microarray technology is giving way to RNA-Seq, it becomes strategic to…
Christina Bligaard Pedersen, Benito Campos, Neeraja M. Krishnan, Binay Panda + 4 more
Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray vs. RNA sequencing data) and…
Keegan Flanagan, Steven Pelech, Yossef Av-Gay, Khanh Dao Duc
Analysis of signal transduction pathways relies on monitoring global changes in cellular response to perturbation or genetic manipulation. Antibody microarray data provides an increasingly viable tool for the monitoring and analyzing high throughput expression and protein phosphorylation data. However, computational…
Michèle Simon, Luis P. Kuschel, Katja von Hoff, Dongsheng Yuan + 7 more
Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded…
Preston Raab, W. Evan Johnson, Stephen R. Piccolo
Precision medicine relies on accurate and generalizable predictions for patients across the spectrum of human diversity. Because capturing biological heterogeneity requires large sample sizes, researchers must often aggregate data from several experimental batches or independent studies. This integration allows for…
Yael Amitay, Yuval Bussi, Ben Feinstein, Shai Bagon + 2 more
Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we…
Jianmei Zhong, Junyao Yang, Yinghui Song, Zhihua Zhang + 8 more
In this study, we have devised a computational framework SuperFeat that allows for the training of a machine learning model and evaluate the canonical cellular states/features in pathological tissues that underlie the progression of disease. This framework also enables the identification of potential drugs that target…
Ali Karimnezhad
The rapid single-cell transcriptomic technology developments has led to an increasing interest in cellular heterogeneity within cell populations. Although cell-type proportions can be obtained directly from single-cell RNA sequencing (scRNA-seq), it is costly and not feasible in every study. Alternatively, with fewer…
Bassel Ghaddar, Subhajyoti De
We developed Census, an automated, hierarchical cell-type identification method for scRNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly…
Kaspar Märtens, Michele Bortolomeazzi, Lucia Montorsi, Jo Spencer + 2 more
Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori. While unsupervised…