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

Identifying Lung Cancer Using CT Scan Images Based On Artificial Intelligence

Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection

Comparing Prediction Accuracy for Machine Learning and Other Classical Approaches in Gene Expression Data

Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. Using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the unwanted noisy and redundant genes. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. In this paper, we have compared the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods are applied to datasets from four recently published cancer gene expression studies. The performance of the classification technique has been evaluated for varying number of selected features in terms of misclassification rate  using hold-out cross validation. Our study shows that KNN, RDA and SVM with linear kernel methods have lower misclassification rate than the other algorithms. Keywords: microarray, gene expression, KNN, DLDA, RDA, SV

Comparing Prediction Accuracy for Supervised Techniques in Gene Expression Data

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data classification etc. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. One challenging area in the studies of gene expression data is the classification of different types of tumors into correct classes. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. The methods are applied to datasets from four recently published cancer gene expression studies. Four publicly available microarray data sets are Leukemia, Lymphoma, SRBCT & Prostate. The performance of the classification technique has been evaluated according to the percentage of misclassification through hold-out cross validation

Study of different classification models based-on microarray

基因芯片技术的发展为生物信息学带来了机遇,使在基因表达水平上进行癌症诊断成为可能。但基因芯片数据高维小样本的特征也使传统机器学习方法面临挑战。本文利用真实的基因表达数据,测试了目前主要的分类方法和降维方法在癌症诊断方面的效果,通过实验对比发现:基于线性核函数的支持向量机可以有效地分类肿瘤与非肿瘤的基因表达,从而为癌症诊断提供借鉴。The development of microarray technology will bring opportunities to bioinformatics and makes it possible to diagnose cancer on the level of gene expression.But the high-dimensional characteristics and small number of samples in microarray data sets also challenges the traditional machine learning methods.In this paper,we compare the effect among the popular classification and dimensionality reduction methods in the diagnosis of cancer using the real gene expression data,the result demonstrates that SVM based on the linear kernel can better classify tumor and non-tumor gene expression,and thereby provide a reference for cancer diagonsis.国家自然科学基金(61001013); 黑龙江省教育厅科学研究项目(12521392); 黑龙江省自然科学基金(F201119

A harmonized atlas of mouse spinal cord cell types and their spatial organization

Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

A study of modulation of P2X3 and TRPV1 receptors by the B-type natriuretic peptide and novel synthetic compounds in trigeminal sensory neurons of wild type and migraine-model mice

Background Trigeminal ganglion (TG) is a key player in processing noxious stimuli. Among many ligand-gated ion channels, trigeminal sensory neurons express on their membranes purinergic P2X3 receptors and capsaicin-sensitive transient receptor potential vanilloid 1 channels (TRPV1). These receptors are thought to be involved in pain transduction and pathophysiology of different pain syndromes, including migraine disorders. P2X3 and TRPV1 channels are continuously regulated by a variety of endogenous modulators, which, upregulating these receptors, can cause sensitization and promote development of pathological pain conditions. Although positive P2X3 and TRPV1 regulators are well studied, not much is known about those which might restrain the activity of these receptors. One candidate for the role of endogenous negative regulator of sensory ganglion activity is the brain natriuretic peptide (BNP). In fact, BNP was recently reported to downregulate inflammatory pain and firing frequency of small neurons in dorsal root ganglia via its receptor NPR-A. Aims In order to investigate the role of BNP/NPR-A system in trigeminal ganglion in control conditions and in migraine pathology we used wild-type (WT) mice and transgenic R192Q KI mice of the familial hemiplegic migraine type 1 (FHM1) model. First we characterized BNP and NPR-A expression and functional properties of the BNP/NPR-A pathway in trigeminal ganglions of WT and KI mice. To understand if this pathway can affect the properties of sensory neurons in TG we studied the effects of endogenous and exogenous BNP on P2X3 and TRPV1 receptors responses in vitro. Investigating molecular mechanisms underneath P2X3 receptor modulation we carefully examined changes in P2X3 phosphorylation and membrane distribution and considered involvement of particular kinases and phosphatases in this process. Firing activity of the WT and KI trigeminal neurons were also evaluated to find out if the modulatory effects of BNP/NPR-A system on the P2X3 channels are reflected in neuronal excitability. Additionally, in search for new potent P2X3 antagonists a variety of diaminopurine derivatives as well as several adenosine nucleotide analogues were evaluated on recombinant P2X3 receptors in HEK cells and on native P2X3 receptors of TG sensory neurons. Results We found abundant expression of NPR-A in trigeminal ganglion along with low levels of BNP itself; the BNP/NPR-A pathway in both WT and KI neurons proved to be functional. Exogenously applied BNP inhibited TRPV1-mediated responses in WT and KI trigeminal neurons without any changes in the receptor\u2019s expression level. On the other hand, P2X3 receptors were not sensitive to additional exogenous BNP, but appeared to be downregulated by the low amount of endogenous BNP already present in WT TG cultures. This negative modulation included P2X3 serine phosphorylation and receptor redistribution to the non-lipid raft membrane compartments. Both mechanisms were dependent on the activity of protein kinase G. Interestingly, in KI mice NPR-A-mediated P2X3 inhibition could not be seen and receptors remained upregulated, most probably due to the increased activity of P/Q calcium channels and high concentration of calcitonin gene related peptide (CGRP). Considering firing properties of trigeminal neurons, inactivation of BNP/NPR-A system with NPR-A antagonist anantin caused a hyperexcitability phenotype of WT cultures, which was very similar to what is typical for KI neurons. KI cultures remained unaltered, consistent with lack of BNP/NPR-A regulation over P2X3 activity. Experiments with new diaminopurine compounds and adenosine nucleotide derivatives resulted in molecules which showed antagonistic behavior towards P2X3 receptors with IC50 values in low micromolar and nanomolar range, respectively. Conclusion The main result of the present study is the identification of BNP/NPR-A pathway as an intrinsic negative modulatory system for P2X3 and TRPV1 receptors activity in sensory neurons of mouse trigeminal ganglion and related neuronal excitability. However, in a mouse FHM1 migraine model BNP/NPR-A lacked the inhibitory effect on P2X3 receptors due to the overall amount of activation these receptors undergo in KI neurons. Modifications of diaminopurine and adenosine scaffold could serve as a promising strategy in search for new potent antagonists of P2X3 receptors