8,584 research outputs found
CAMUR: Knowledge extraction from RNA-seq cancer data through equivalent classification rules
Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class
Pairwise gene GO-based measures for biclustering of high-dimensional expression data
Background: Biclustering algorithms search for groups of genes that share the same
behavior under a subset of samples in gene expression data. Nowadays, the biological
knowledge available in public repositories can be used to drive these algorithms to
find biclusters composed of groups of genes functionally coherent. On the other hand,
a distance among genes can be defined according to their information stored in Gene
Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each
pair of genes which establishes their functional similarity. A scatter search-based
algorithm that optimizes a merit function that integrates GO information is studied in
this paper. This merit function uses a term that addresses the information through a GO
measure.
Results: The effect of two possible different gene pairwise GO measures on the
performance of the algorithm is analyzed. Firstly, three well known yeast datasets with
approximately one thousand of genes are studied. Secondly, a group of human
datasets related to clinical data of cancer is also explored by the algorithm. Most of
these data are high-dimensional datasets composed of a huge number of genes. The
resultant biclusters reveal groups of genes linked by a same functionality when the
search procedure is driven by one of the proposed GO measures. Furthermore, a
qualitative biological study of a group of biclusters show their relevance from a cancer
disease perspective.
Conclusions: It can be concluded that the integration of biological information
improves the performance of the biclustering process. The two different GO measures
studied show an improvement in the results obtained for the yeast dataset. However, if
datasets are composed of a huge number of genes, only one of them really improves
the algorithm performance. This second case constitutes a clear option to explore
interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Elephant Search with Deep Learning for Microarray Data Analysis
Even though there is a plethora of research in Microarray gene expression
data analysis, still, it poses challenges for researchers to effectively and
efficiently analyze the large yet complex expression of genes. The feature
(gene) selection method is of paramount importance for understanding the
differences in biological and non-biological variation between samples. In
order to address this problem, a novel elephant search (ES) based optimization
is proposed to select best gene expressions from the large volume of microarray
data. Further, a promising machine learning method is envisioned to leverage
such high dimensional and complex microarray dataset for extracting hidden
patterns inside to make a meaningful prediction and most accurate
classification. In particular, stochastic gradient descent based Deep learning
(DL) with softmax activation function is then used on the reduced features
(genes) for better classification of different samples according to their gene
expression levels. The experiments are carried out on nine most popular Cancer
microarray gene selection datasets, obtained from UCI machine learning
repository. The empirical results obtained by the proposed elephant search
based deep learning (ESDL) approach are compared with most recent published
article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl
The importance of data classification using machine learning methods in microarray data
The detection of genetic mutations has attracted global attention. several methods have proposed to detect diseases such as cancers and tumours. One of them is microarrays, which is a type of representation for gene expression that is helpful in diagnosis. To unleash the full potential of microarrays, machine-learning algorithms and gene selection methods can be implemented to facilitate processing on microarrays and to overcome other potential challenges. One of these challenges involves high dimensional data that are redundant, irrelevant, and noisy. To alleviate this problem, this representation should be simplified. For example, the feature selection process can be implemented by reducing the number of features adopted in clustering and classification. A subset of genes can be selected from a pool of gene expression data recorded on DNA micro-arrays. This paper reviews existing classification techniques and gene selection methods. The effectiveness of emerging techniques, such as the swarm intelligence technique in feature selection and classification in microarrays, are reported as well. These emerging techniques can be used in detecting cancer. The swarm intelligence technique can be combined with other statistical methods for attaining better results
Classification Based Analysis on Cancer Datasets Using Predictor Measures
Cancer is a life-threatening disease. Probably the most effective way to reduce cancer deaths is to detect it earlier. Diagnosing the disease earlier needs an accurate and reliable procedure which could be used by physicians to distinguish between cancer from malignant ones without leaving for surgical biopsy. Data mining offers solution for such types of the problems where a large quantity of information about patients and their conditions are stored in clinical database. This paper focuses on prediction of some such diseases like Leukemia and Breast cancers. Naïve Bayes and SVM prediction models are built for the prediction and classification. The performance of the proposed models produced significant results of above 96% while compared with other models in terms of accuracy, computational time and convergence. Keywords: Prediction, Data Mining, Diagnosis, Cancer, Naïve Bayes, Supper Vector machine (SVM). DOI: 10.7176/CEIS/10-6-05 Publication date:July 31st 201
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