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Microarray Analysis

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Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.

Scientific reports
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system...

Using Gene Ontology to Annotate and Prioritize Microarray Data.

Methods in molecular biology (Clifton, N.J.)
The results of high-throughput experiments consist of numerous candidate genes, proteins, or other molecules potentially associated with diseases. A challenge for omics science is the knowledge extraction from the results and the filtering of promisi...

Low-precision feature selection on microarray data: an information theoretic approach.

Medical & biological engineering & computing
The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the capacity of using machine learning models fo...

An ensemble framework for microarray data classification based on feature subspace partitioning.

Computers in biology and medicine
Feature selection is exposed to the curse of dimensionality risk, and it is even more exacerbated with high-dimensional data such as microarrays. Moreover, the low-instance/high-feature (LIHF) property of microarray data needs considerable processing...

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.

International journal of molecular sciences
Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel the...

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Communications biology
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. Th...

Machine Learning Informs RNA-Binding Chemical Space.

Angewandte Chemie (International ed. in English)
Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders...

MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data.

Computers in biology and medicine
Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although machine learning models have been widely applied in medical data analysis, some barriers are yet to ...

A Unified Multi-Class Feature Selection Framework for Microarray Data.

IEEE/ACM transactions on computational biology and bioinformatics
In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary featur...