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1.
Artigo em Inglês | MEDLINE | ID: mdl-31751222

RESUMO

OBJECTIVE: The study of pathogenic mechanism at the genetic level by imaging genetics methods enables to effectively reveal the association of histopathology and genetics. However, there is a lack of effective and accurate tools to establish association models from macroscopic to microscopic. METHODS: The multi-constrained joint non-negative matrix factorization (MCJNMF) was developed for simultaneous integration of genomic data and image data to identify common modules related to disease. Two types of data matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. Meanwhile, the correlation between original data features was integrated by using regularization constraints to improve the biological relevance. Sparsity constraints and orthogonal constraints were performed on decomposition factors to minimize the redundancy between different bases and to reduce algorithm complexity. RESULTS: This algorithm was successfully performed on the module identification of lung metastasis in soft tissue sarcomas (STSs) by integrating FDG-PET image and DNA methylation data features. Multilevel analysis on the top extracted modules revealed that these modules were closely related to the lung metastasis. Particularly, several genes with diagnostic potential for lung metastasis can be discovered from high score modules. CONCLUSION: This method not only can be applied for the accurate identification of patterns related to pathogenic mechanism of diseases, but also has a significant implication for discovering protein biomarkers. SIGNIFICANCE: This method provides avenues for further studies of identifying complex association patterns of diseases according to different types of biological data.

2.
Interdiscip Sci ; 11(2): 226-236, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29675796

RESUMO

The focus of modern biomedical research concentrates on molecular level regulatory mechanisms and how the normal and abnormal phenotypes of tissue functional are affected by regulatory mechanisms. Most of the research on regulatory mechanism starts from the reconstruction of gene regulation network. At present, a large number of reconstruction methods construct the network using a single data set. These methods of inferring and predicting the relationship between the target gene and the transcription factor (TF) can be used to identify individual interactions between genes, while there is not much research on the interaction of many functional-related genes. In this paper, an integrated approach based on multi-data fusion is used to reconstruct the network on Alzheimer's disease (AD) which is the most common form of dementia. It not only considers the interaction between many functional-related genes and the TFs that have important implications for regulatory mechanisms, but also detects new genes associated with specific gene function expression. Protein interaction data, motif data and gene expression data of AD were integrated to gain insight into the underlying biological processes of AD. This method takes into account the TF on the target gene regulation, at the same time also considers co-expression mechanism of the TF and co-regulatory mechanism of the target gene. Eventually, not only a number of genes such as E2F4 and ATF1 related to the pathogenesis of AD have been identified, but also several significant biological processes, such as immunoregulation and neurogenesis, have been found to be associated with AD.


Assuntos
Algoritmos , Doença de Alzheimer/genética , Redes Reguladoras de Genes/genética , Bases de Dados Genéticas , Humanos , Mapas de Interação de Proteínas/genética , Fatores de Transcrição/metabolismo
3.
Pathol Res Pract ; 215(1): 159-170, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30466766

RESUMO

Cancer immunotherapy has achieved unprecedented success in the treatment of cancer. However, different patients have different responses to immunotherapy. More and more studies have shown that tumor immune heterogeneity has an important influence on the prognosis of cancer. Therefore, understanding the clinical impact of tumor immune infiltration and the regulatory mechanism of RNA molecules is crucial for exploring the pathogenesis of lung adenocarcinoma (LUAD) and the development of immunotherapy protocols.The endogenous competitive RNA hypothesis provides new ideas for studying immune heterogeneity. Therefore, by using the method of immune genomics, this article explores the relationship between immune infiltration and prognosis of patients with lung adenocarcinoma, and found that B-cell immune infiltration highly affects the survival of patients. Through differential analysis, differential mRNAs, lncRNAs and miRNAs were extracted, and 318 differentially expressed mRNAs related to B cell immunity were screened by correlation analysis, and prognosis of patients with COX risk regression model was predicted and analyzed. Through multiple database searches, an immune-related ceRNA regulatory network was constructed, containing 3 key mRNAs, 4 miRNAs, and 50 lncRNAs. Three mRNAs and most miRNAs, lncRNAs, are significantly associated with LUAD prognosis. Bioinformatics analysis of the network showed that LINC00337 may up-regulate the expression of PBK and KIF23 through competitive binding of has-mir-373 and has-mir-519d. The competitive binding of has-mir-373 and has-mir-372 can up-regulate the expression of SLC7A11. The interaction between these RNAs may have an important regulatory role in the immune infiltration in lung adenocarcinoma, thereby affecting the patient's prognosis and immunotherapy efficacy.


Assuntos
Adenocarcinoma de Pulmão/genética , Adenocarcinoma/genética , Redes Reguladoras de Genes/genética , RNA Longo não Codificante/genética , Adenocarcinoma/patologia , Regulação Neoplásica da Expressão Gênica/genética , Humanos , MicroRNAs/genética
4.
J Cell Biochem ; 120(6): 9034-9046, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30582215

RESUMO

Recent theoretical and experimental studies indicate that long-chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease-related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non-small-cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer.

5.
Int J Biol Sci ; 14(13): 1822-1833, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30443186

RESUMO

MRNA and lncRNA serve as a type of endogenous RNA in cell, which can competitively bind to the same miRNA through miRNA response elements (MREs), thereby regulating their respective expression levels, playing an important role in post-transcriptional regulation, and regulating the progress of tumors. The proposed competing endogenous RNA (ceRNA) hypothesis provides novel clues for the occurrence and development of tumors, but the integrative analysis methods of diverse RNA data are significantly limited. In order to find out the relationship among miRNA, mRNA and lncRNA, the previous studies only used individual dataset as seeds to search two other related data in the database to construct ceRNA network, but it was difficult to identify the synchronized effects from multiple regulatory levels. Here, we developed the joint matrix factorization method integrating prior knowledge to map the three types of RNA data of lung cancer to the common coordinate system and construct the ceRNA network corresponding to the common module. The results show that more than 90% of the modules are closely related to cancer, including lung cancer. Furthermore, the resulting ceRNA network not only accurately excavates the known correlation of the three types of RNA molecular, but also further discovers the potential biological associations of them. Our work provides support and foundation for future biological validation how competitive relationships of multiple RNAs affects the development of tumors.


Assuntos
Algoritmos , Biomarcadores Tumorais/genética , Biologia Computacional , Regulação Neoplásica da Expressão Gênica , Neoplasias Pulmonares/genética , MicroRNAs/genética , RNA Mensageiro/genética
6.
Per Med ; 15(5): 381-394, 2018 09.
Artigo em Inglês | MEDLINE | ID: mdl-30259787

RESUMO

AIM: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. METHODS: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). RESULTS: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. CONCLUSION: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.


Assuntos
Carcinoma de Células Renais/genética , Perfilação da Expressão Gênica/métodos , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Biomarcadores/sangue , Biomarcadores Tumorais/genética , Proteínas de Ciclo Celular , Proteínas de Ligação a DNA/genética , Expressão Gênica/genética , Regulação Neoplásica da Expressão Gênica/genética , Redes Reguladoras de Genes , Humanos , Mapas de Interação de Proteínas/genética , Fatores de Transcrição/genética , Transcriptoma/genética
7.
Artigo em Inglês | MEDLINE | ID: mdl-29165066

RESUMO

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design.

8.
PLoS One ; 12(7): e0180337, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28719625

RESUMO

Although chronic inflammation and immune disorders are of great importance to the pathogenesis of both dementia and cancer, the pathophysiological mechanisms are not clearly understood. In recent years, growing epidemiological evidence and meta-analysis data suggest an inverse association between Alzheimer's disease (AD), which is the most common form of dementia, and cancer. It has been revealed that some common genes and biological processes play opposite roles in AD and cancer; however, the biological immune mechanism for the inverse association is not clearly defined. An unsupervised matrix decomposition two-stage bioinformatics procedure was adopted to investigate the opposite behaviors of the immune response in AD and breast cancer (BC) and to discover the underlying transcriptional regulatory mechanisms. Fast independent component analysis (FastICA) was applied to extract significant genes from AD and BC microarray gene expression data. Based on the extracted data, the shared transcription factors (TFs) from AD and BC were captured. Second, the network component analysis (NCA) algorithm in this study was presented to quantitatively deduce the TF activities and regulatory influences because quantitative dynamic regulatory information for TFs is not available via microarray techniques. Based on the NCA results and reconstructed transcriptional regulatory networks, inverse regulatory processes and some known innate immune responses were described in detail. Many of the shared TFs and their regulatory processes were found to be closely related to the adaptive immune response from dramatically different directions and to play crucial roles in both AD and BC pathogenesis. From the above findings, the opposing cellular behaviors demonstrate an invaluable opportunity to gain insights into the pathogenesis of these two types of diseases and to aid in developing new treatments.


Assuntos
Doença de Alzheimer/genética , Doença de Alzheimer/imunologia , Neoplasias da Mama/genética , Neoplasias da Mama/imunologia , Regulação da Expressão Gênica/imunologia , Transcrição Genética/imunologia , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Análise de Sequência com Séries de Oligonucleotídeos
9.
J Funct Biomater ; 7(3)2016 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-27527229

RESUMO

Silk proteins are natural biopolymers that have extensive structural possibilities for chemical and mechanical modifications to facilitate novel properties, functions, and applications in the biomedical field. The versatile processability of silk fibroins (SF) into different forms such as gels, films, foams, membranes, scaffolds, and nanofibers makes it appealing in a variety of applications that require mechanically superior, biocompatible, biodegradable, and functionalizable biomaterials. There is no doubt that nature is the world's best biological engineer, with simple, exquisite but powerful designs that have inspired novel technologies. By understanding the surface interaction of silk materials with living cells, unique characteristics can be implemented through structural modifications, such as controllable wettability, high-strength adhesiveness, and reflectivity properties, suggesting its potential suitability for surgical, optical, and other biomedical applications. All of the interesting features of SF, such as tunable biodegradation, anti-bacterial properties, and mechanical properties combined with potential self-healing modifications, make it ideal for future tissue engineering applications. In this review, we first demonstrate the current understanding of the structures and mechanical properties of SF and the various functionalizations of SF matrices through chemical and physical manipulations. Then the diverse applications of SF architectures and scaffolds for different regenerative medicine will be discussed in detail, including their current applications in bone, eye, nerve, skin, tendon, ligament, and cartilage regeneration.

10.
Biomed Res Int ; 2015: 394260, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25866779

RESUMO

Alzheimer's disease (AD) is a progressively and fatally neurodegenerative disorder and leads to irreversibly cognitive and memorial damage in different brain regions. The identification and analysis of the dysregulated pathways and subnetworks among affected brain regions will provide deep insights for the pathogenetic mechanism of AD. In this paper, commonly and specifically significant subnetworks were identified from six AD brain regions. Protein-protein interaction (PPI) data were integrated to add molecular biological information to construct the functional modules of six AD brain regions by Heinz algorithm. Then, the simulated annealing algorithm based on edge weight is applied to predicting and optimizing the maximal scoring networks for common and specific genes, respectively, which can remove the weak interactions and add the prediction of strong interactions to increase the accuracy of the networks. The identified common subnetworks showed that inflammation of the brain nerves is one of the critical factors of AD and calcium imbalance may be a link among several causative factors in AD pathogenesis. In addition, the extracted specific subnetworks for each brain region revealed many biologically functional mechanisms to understand AD pathogenesis.


Assuntos
Algoritmos , Doença de Alzheimer , Encéfalo , Modelos Biológicos , Rede Nervosa , Doença de Alzheimer/metabolismo , Doença de Alzheimer/patologia , Encéfalo/metabolismo , Encéfalo/patologia , Cálcio/metabolismo , Feminino , Humanos , Masculino , Rede Nervosa/metabolismo , Rede Nervosa/patologia , Proteínas do Tecido Nervoso/metabolismo
11.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 31(3): 662-70, 2014 Jun.
Artigo em Chinês | MEDLINE | ID: mdl-25219254

RESUMO

It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.


Assuntos
Perfilação da Expressão Gênica/métodos , Algoritmos , Doença de Alzheimer/genética , Humanos
12.
Comput Math Methods Med ; 2014: 891761, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25024739

RESUMO

Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.


Assuntos
Doença de Alzheimer/diagnóstico , Encéfalo/patologia , Biologia Computacional/métodos , Algoritmos , Doença de Alzheimer/patologia , Animais , Análise por Conglomerados , Escherichia coli/metabolismo , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Inflamação , Camundongos , Modelos Estatísticos , Distribuição Normal , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Software , Fatores de Transcrição/metabolismo
13.
Comput Math Methods Med ; 2014: 340758, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24812571

RESUMO

Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer's disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF- κ B (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF- κ B and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.


Assuntos
Doença de Alzheimer/metabolismo , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Inflamação/metabolismo , Transdução de Sinais , Algoritmos , Precursor de Proteína beta-Amiloide/metabolismo , Redes Reguladoras de Genes , Humanos , Modelos Lineares , NF-kappa B/metabolismo , Software
14.
BMC Bioinformatics ; 12 Suppl 5: S7, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21989140

RESUMO

BACKGROUND: The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets. METHODS: Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF) are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer's disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. RESULTS: In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer's disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and the biological analysis of the identified significant genes and their related pathways demonstrated that these genes play a prominent role in AD and relate the activation patterns to AD phenotypes. It is validated that the combination of these two methods is efficient. CONCLUSIONS: Unsupervised matrix factorization methods provide efficient tools to analyze high-throughput microarray dataset. According to the facts that different unsupervised approaches explore correlations in the high-dimensional data space and identify relevant subspace base on different hypotheses, integrating these methods to explore the underlying biological information from microarray dataset is an efficient approach. By combining the significant genes identified by both ICA and NMF, the biological analysis shows great efficient for elucidating the molecular taxonomy of Alzheimer's disease and enable better experimental design to further identify potential pathways and therapeutic targets of AD.


Assuntos
Doença de Alzheimer/genética , Perfilação da Expressão Gênica/métodos , Algoritmos , Inteligência Artificial , Análise por Conglomerados , Bases de Dados Genéticas , Regulação da Expressão Gênica , Hipocampo/metabolismo , Humanos , Análise de Sequência com Séries de Oligonucleotídeos/métodos
15.
Int J Clin Exp Med ; 4(1): 17-25, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21394282

RESUMO

Although tremendous progress has been made in basic cancer biology and in the development of novel cancer treatments, cancer remains a leading cause of death in the world. The etiopathogenesis of cancer is complex. Besides genetic predisposition, known environmental factors associated with cancer are: diet, lifestyle, and environmental toxins. Toxicity of drugs and eventual relapse of cancers contribute to high cancer death rates. Current therapeutic interventions for cancer- surgery, chemotherapy, radiotherapy, thermotherapy, etc. are far from being curative for many forms of cancer. Chemotherapy, in particular, though the most commonly used cancer treatment, is usually associated with side effects with varying degrees of severity. The purpose of this brief review is to assemble current literature on some crude drugs and to focus on their beneficial roles and drug targets in cancer therapy and chemo-prevention. Although their pharmacological mechanisms and biochemical roles in cancer biology and tumor chemo-prevention are not fully understood, crude drugs are believed to have nutriceutical effects upon cancer patients.

16.
Mol Neurodegener ; 4: 5, 2009 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-19173745

RESUMO

BACKGROUND: Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics. RESULTS: ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis. CONCLUSION: We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.

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