Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral ...biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins.
In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.
•Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB,UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B).•Common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) were identified.•microRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186- 5p) were identified.•Evaluation of histone modification revealed that hub genes and transcription factors possess several histone modification sites associated with AD.•Protein-drug interactions revealed 10 compounds that affect the identified AD candidate markers.
Alzheimer’s disease (AD) is a dynamic degeneration of the brain with progressive dementia. Considering the uncertainties in its molecular mechanism, in the present study, we employed network-based ...integrative analyses, and aimed to explore the key molecules and their associations with small drugs to identify potential biomarkers and therapeutic agents for the AD. First of all, we studied a transcriptome dataset and identified 1521 differentially expressed genes (DEGs). Integration of transcriptome data with protein-protein and transcriptional regulatory interactions resulted with central (hub) proteins (UBA52, RAC1, CREBBP, AR, RPS11, SMAD3, RPS6, RPL12, RPL15, and UBC), regulatory transcription factors (FOXC1, GATA2, YY1, FOXL1, NFIC, E2F1, USF2, SRF, PPARG, and JUN) and microRNAs (mir-335-5p, mir-26b-5p, mir-93-5p, mir-124-3p, mir-17-5p, mir-16-5p, mir-20a-5p, mir-92a-3p, mir-106b-5p, and mir-192-5p) as key signaling and regulatory molecules associated with transcriptional changes for the AD. Considering these key molecules as potential therapeutic targets and Connectivity Map (CMap) architecture, candidate small molecular agents (such as STOCK1N-35696) were identified as novel potential therapeutics for the AD. This study presents molecular signatures at RNA and protein levels which might be useful in increasing discernment of the molecular mechanisms, and potential drug targets and therapeutics to design effective treatment strategies for the AD.
Many studies have focused on obtaining high accuracy in the design of Intrusion Detection Systems (IDS) for in-vehicle networks, neglecting the significance of different intensive packet injection ...techniques. Because of their reliance on scenario-specific training datasets, these IDSs are vulnerable to failing to detect real-world attacks. This study implemented deep learning (DL)-based classification for intrusion detection using a Gated Recurrent Unit (GRU) while considering various intrusion frequencies. Different intrusion frequencies are comprehensively addressed with frequency-agnostic intrusion and resolved by generalizing features for DL input through time series segmentation and frequency domain conversion using Gabor filtering. For training purposes, five types of vehicle data are used, encompassing DoS, fuzzing, and replay attack scenarios. The accuracy range for mechanical version vehicles is typically between 95% and 100%. For electronic vehicles, it is around 90%. Considering the nature of this IDS system, it has been named a Comprehensive Frequency-Agnostic Intrusion Detection System (CF-AIDS). Although this IDS can perform better in all aspects, achieving more efficient results requires a larger amount of situational data.
Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker ...signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein⁻protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan⁻Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.
Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and ...pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21–5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.
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Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the ...literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use
-values or fold change (FC) values to detect the DE genes. However, sometimes,
-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both
-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of
-values computed by SAM statistic using minimum
-divergence method, which is designed for paired samples.
: The proposed method introduces a weight function known as
-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The
-weight function plays the significant role on the performance of the proposed method. The proposed method uses
-weight function as a measure of outlier detection by setting
= 0.2. We unify both classical and robust estimates using
-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum
-divergence estimators are used in presence of outliers to obtain reasonable
-values and FC values in the proposed method.
We examined the performance of proposed method in a comparison of some popular methods (
-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations.
Using pathway analysis, disease association study, protein-protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer.
: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms ...that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms.
: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD.
: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein-protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found.
: Protein-drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.
Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for ...downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.
•We develop a new robust missing value imputation approach for transcriptomics and metabolomics data analysis.•The proposed algorithm has been implemented in a R package available in https://CRAN.R-project.org/package=rMisbeta.•From breast cancer dataset rMisbeta identified 6 outlying DEGs that were not detected by the other stat-of arts methods.•From GC-MS metabolomics dataset rMisbeta identified 2 additional metabolites that were not detected by the other methods.•rMisbeta is accurate, simple and fast, in the sense that it requires lower computational time.
The Controller Area Network (CAN) is a widely used communication protocol in automobiles, but it is vulnerable to various types of attacks. To address this issue, researchers have been exploring the ...use of intrusion detection systems (IDS) for the CAN bus. Deep learning and machine learning have been proven to be powerful tools for detecting intrusions accurately and quickly. However, deep learning models require large amounts of data to achieve optimal performance, which can be challenging in the case of a CAN bus IDS. To overcome this challenge, we propose a novel machine learning-based IDS called CANPerFL that uses a personalized federated learning scheme to aggregate datasets from different car models. By building a universal model trained on a small amount of data from each manufacturer, we can provide global knowledge that can be transferred to improve the performance of each participant. To demonstrate the efficiency of the proposed model, we collected a real CAN dataset consisting of three different car models: KIA, BMW, and Tesla. The experimental results show that the proposed model increases F1 scores by 4% overall, compared to baselines. Moreover, the proposed system provides significant advantages when the local dataset of each participant is relatively small. According to our experiments, the proposed models can achieve F1 scores of more than 90% with at least 30k training samples on each client. Finally, we show empirically that each participant takes benefits from joining the CANPerFL system.
Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain ...of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.
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