14 papers · ranked by Valyu relevance
Nikita Bhandari, Rahee Walambe, Ketan Kotecha, Satyajeet P. Khare
Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative…
Mohsen Hajiloo, Hamid R Rabiee, Mahdi Anooshahpour
Background The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and…
Jingeun Kim, Yourim Yoon, Hye-Jin Park, Yong-Hyuk Kim + 1 more
Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification…
Thanh Nguyen, Abbas Khosravi, Douglas Creighton, Saeid Nahavandi + 1 more
'Enrique Hernandez-Lemus'] This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five…
R Simon
DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and therapeutics development. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This paper provides a review of the key features to…
Jaison Bennet, Chilambuchelvan Arul Ganaprakasam, Kannan Arputharaj
Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in…
Ajin R. Nair, Harikumar Rajaguru, M. S. Karthika, C. Keerthivasan
The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with…
Jard H. de Vries, Daniel Kling, Athina Vidaki, Pascal Arp + 6 more
Single nucleotide polymorphism (SNP) data generated with microarray technologies have been used to solve murder cases via investigative leads obtained from identifying relatives of the unknown perpetrator included in accessible genomic databases, referred to as investigative genetic genealogy (IGG). However, SNP…
Alina Frolova, Vladyslav Bondarenko, Maria Obolenska
According to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological…
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…
Ernur Saka, Benjamin J. Harrison, Kirk West, Jeffrey C. Petruska + 1 more
Since the introduction of microarrays in 1995, researchers world-wide have used both commercial and custom-designed microarrays for understanding differential expression of transcribed genes. Public databases such as ArrayExpress and the Gene Expression Omnibus (GEO) have made millions of samples readily available. One…
Majid Mohammadi, Hossein Sharifi Noghabi, Ghosheh Abed Hodtani, Habib Rajabi Mashhadi
One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this…
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…
Kazi Ferdous Mahin, Md. Robiuddin, Mujahidul Islam, Shayed Ashraf + 2 more
Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related…