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OBJECTIVE: The global rise in type 2 diabetes is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy is the most frequent type 2 diabetes complication and is associated with poor outcomes. The metabolic syndrome has emerged as a major risk factor for diabetic polyneuropathy; however, the metabolites associated with the metabolic syndrome that correlate with diabetic polyneuropathy are unknown.METHODS: We conducted a global metabolomics analysis on plasma samples from a subcohort of participants from the Danish arm of Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION-Denmark) with and without diabetic polyneuropathy versus lean control participants.RESULTS: Compared to lean controls, type 2 diabetes participants had significantly higher HbA1c (p = 0.0028), BMI (p = 0.0004), and waist circumference (p = 0.0001), but lower total cholesterol (p = 0.0001). Out of 991 total metabolites, we identified 15 plasma metabolites that differed in type 2 diabetes participants by diabetic polyneuropathy status, including metabolites belonging to energy, lipid, and xenobiotic pathways, among others. Additionally, these metabolites correlated with alterations in plasma lipid metabolites in type 2 diabetes participants based on neuropathy status. Further evaluating all plasma lipid metabolites identified a shift in abundance, chain length, and saturation of free fatty acids in type 2 diabetes participants. Importantly, the presence of diabetic polyneuropathy impacted the abundance of plasma complex lipids, including acylcarnitines and sphingolipids.INTERPRETATION: Our explorative study suggests that diabetic polyneuropathy in type 2 diabetes is associated with novel alterations in plasma metabolites related to lipid metabolism.

Kidneys are critical target organs of COVID-19, but susceptibility and responses to infection remain poorly understood. Here, we combine SARS-CoV-2 variants with genome edited kidney organoids and clinical data to investigate tropism, mechanism, and therapeutics. SARS-CoV-2 specifically infects organoid proximal tubules amongst diverse cell types. Infections produce replicating virus, apoptosis, and disrupted cell morphology, features of which are revealed in the context of polycystic kidney disease. Cross-validation of gene expression patterns in organoids reflect proteomic signatures of COVID-19 in the urine of critically ill patients indicating interferon pathway upregulation. SARS-CoV-2 viral variants Alpha, Beta, Gamma, Kappa, and Delta exhibit comparable levels of replication in organoids. Infection is ameliorated in ACE2-/- organoids and blocked via treatment with de novo designed spike binder peptides. Collectively, these studies clarify the impact of kidney infection in COVID-19 as reflected in organoids and clinical populations, enabling assessment of viral fitness and emerging therapies.

BackgroundClassification of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies. This approach does not reflect underlying disease biology, limiting the ability to predict progression or treatment response.MethodsSystems biology approaches were used to categorize patients with minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) based on kidney biopsy tissue transcriptomics across three cohorts and assessed association with clinical outcomes. Patient-level tissue pathway activation scores were generated using differential gene expression. Then, functional enrichment and non-invasive urine biomarker candidates were identified. Biomarkers were validated in kidney organoid models and single nucleus RNA-seq (snRNAseq) from kidney biopsies.ResultsTranscriptome-based categorization identified three subgroups of patients with shared molecular signatures across independent North American, European and African cohorts. One subgroup demonstrated worse longterm outcomes (HR 5.2, p = 0.001) which persisted after adjusting for diagnosis and clinical measures (HR 3.8, p = 0.035) at time of biopsy. This subgroup’s molecular profile was largely (48%) driven by tissue necrosis factor (TNF) activation and could be predicted based on levels of TNF pathway urinary biomarkers TIMP-1 and MCP-1 and clinical features (correlation 0.63, p ConclusionsMolecular profiling identified a patient subgroup within nephrotic syndrome with poor outcome and kidney TNF pathway activation. Clinical trials using non-invasive biomarkers of pathway activation to target therapies are currently being evaluated.Significance StatementMechanistic, targeted therapies are urgently needed for patients with nephrotic syndrome. The inability to target an individual’s specific disease mechanism using currently used diagnostic parameters leads to potential treatment failure and toxicity risk. Patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) were grouped by kidney tissue transcriptional profiles and a subgroup associated with poor outcomes defined. The segregation of the poor outcome group was driven by tumor necrosis factor (TNF) pathway activation and could be identified by urine biomarkers, MCP1 and TIMP1. Based on these findings, clinical trials utilizing non-invasive biomarkers of pathway activation to target therapies, improve response rates and facilitate personalized treatment in nephrotic syndrome have been initiated.

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised

Maternal pre‐pregnancy obesity may have an impact on both maternal and fetal health. We examined the microbiome recovered from placentas in a multi‐ethnic maternal pre‐pregnant obesity cohort, through an optimized microbiome protocol to enrich low bacterial biomass samples. We found that the microbiomes recovered from the placentas of obese pre‐pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre‐pregnancy weight. Microbiome richness also decreases from the maternal side to the fetal side, demonstrating heterogeneity by geolocation within the placenta. In summary, our study shows that the microbiomes recovered from the placentas are associated with pre‐pregnancy obesity. Importance Maternal pre‐pregnancy obesity may have an impact on both maternal and fetal health. The placenta is an important organ at the interface of the mother and fetus, and supplies nutrients to the fetus. We report that the microbiomes enriched from the placentas of obese pre‐pregnant mothers are less abundant and less diverse when compared to those from mothers of normal pre‐pregnancy weight. More over, the microbiomes also vary by geolocation within the placenta.

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

Peripheral neuropathy (PN) is a common complication of diabetes that develops in up to 50% of people with diabetes. Obesity and the metabolic syndrome are risk factors for PN in patients with type 2 diabetes (T2D). However, the lipid metabolites associated with PN development are unknown. We compared the metabolomic profile of plasma from T2D patients with and without PN compared to lean control patients to identify metabolic changes that underlie PN associated with T2D. PN was evaluated in a cohort of T2D patients from the Danish portion of the Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION). The Toronto criteria was used to assess PN based on symptoms of neuropathy and abnormal nerve conduction studies. To identify metabolomic signatures that associate with PN, plasma samples from 9 lean control, 49 T2D, and 48 T2D patients with PN were submitted to Metabolon® for a global metabolomics analysis. We used least absolute shrinkage and selection operator (LASSO) and Welch’s two-sample t-test to identify metabolites that differed between PN and non-PN subjects. Metabolomics identified plasma lipid metabolites that were dysregulated in T2D including cholesterol, sphingolipids, and acylcarnitines. Cholesterol and three classes of sphingolipid metabolites (dihydrosphingomyelin, glucosylceramide, and sphingomyelin) were significantly lower in plasma from T2D patients irrespective of PN compared to lean controls. Interestingly, long-chain and very long-chain acylcarnitines were also reduced in T2D patients while short-chain acylcarnitines were unchanged. Only a small number of metabolites were uniquely regulated by PN; of these, two very long-chain acylcarnitine species (C26:1 and C24) were significantly diminished in T2D patients with PN compared to those without. These results suggest an association with lipid markers of impaired mitochondrial β-oxidation and PN in T2D. Disclosure A.E. Rumora: None. F. Alakwaa: None. S.T. Andersen: None. M.E. Jørgensen: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. M. Charles: Other Relationship; Self; Novo Nordisk A/S. B.C. Callaghan: None. T.S. Jensen: None. E.L. Feldman: Consultant; Self; Novartis Pharmaceuticals Corporation. Funding American Diabetes Association (7-12-BS-045 to E.L.F.); Novo Nordisk Foundation (NNF14OC0011633); National Institutes of Health (1R24082841, 1DP3DK094292); National Institute of Diabetes and Digestive and Kidney Diseases (T32DK101357, F32DK112642, K99DK119366); NeuroNetwork for Emerging Therapies; A. Alfred Taubman Medical Research Institute

As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS)., As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database “Library of Integrated Network-Based Cellular Signatures” (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzyl-quinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.

ObjectiveTo identify dysregulated metabolic pathways in amyotrophic lateral sclerosis (ALS) versus control participants through untargeted metabolomics.MethodsUntargeted metabolomics was performed on plasma from ALS participants (n=125) around 6.8 months after diagnosis and healthy controls (n=71). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and partial least squares-discriminant analysis (PLS-DA), while group lasso explored sub-pathway-level differences. Adjustment parameters included sex, age and body mass index (BMI). Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Finally, machine learning classification algorithms applied to group lasso-selected metabolites were evaluated for classifying case status.ResultsThere were no group differences in sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. ‘Benzoate metabolism’, ‘ceramides’, ‘creatine metabolism’, ‘fatty acid metabolism (acyl carnitine, polyunsaturated)’ and ‘hexosylceramides’ sub-pathways were enriched by all methods, and ‘sphingomyelins’ by all but Wilcoxon, indicating these pathways significantly associate with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%.ConclusionIn our analysis, ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER-patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value

Background: Recent studies have reveled the presence of a complex fungal community (mycobiome) in the oral cavity. However, the role of oral mycobiome in dental caries and its interaction with caries-associated bacteria is not yet clear. Methods: Whole-mouth supragingival plaque samples from 30 children (6–10 years old) with no caries, early caries, or advanced caries were sequenced for internal transcribed spacer 2 (ITS-2). The mycobiome profiles were correlated with previously published bacteriome counterparts. Interaction among selected fungal and bacterial species was assessed by co-culture or spent media experiments. Results: Fungal load was extremely low. Candida, Malassezia, Cryptococcus, and Trichoderma spp. were the most prevalent/abundant taxa. Advanced caries was associated with significantly higher fungal load and prevalence/abundance of Candida albicans. Cryptococcus neoformans and Candida sake were significantly over-abundant in early caries, while Malassezia globosa was significantly enriched in caries-free subjects. C. albicans correlated with Streptococcus mutans and Scardovia wiggsiae among other caries-associated bacteria, while M. globosa inversely correlated with caries-associated bacteria. In-vitro, M. globosa demonstrated inhibitory properties against S. mutans. Conclusions: the results substantiate the potential role of the oral mycobiome, primarily Candida species, in dental caries. Inter-kingdom correlations and inhibition of S. mutans by M. globosa are worth further investigation.

Preeclampsia is a medical condition affecting 5–10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level and studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using the network analysis on the biomarkers of all literatures reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.

A mathematical model of artificial ventilation takes more positive evolution weather to represent the lung status during normal breathing or the artificial ventilation. This paper presents a Mathematical Model (MM) of Volume Controlled Ventilator (VCV) output signals, including Positive End Expiration Pressure (PEEP) and Dynamic Compliance (C). The proposed MM is expressed by linear, quadratic and exponential equations to represent the obtained combination of inspiration and expiration activities by VCV for ideal and practical lung cases. The MM of ventilator output signals is combined with existed MM of lung to represent the artificial ventilation process using VCV. The combined MMs are modelled and simulated using Simulink tool in MATLAB program. The input (pressure) signal from VCV and derived output (volume and flow) signals are monitored graphically using constructed simulator. The results clarified the efficiency of proposed MM and simulator. Moreover, the simulator has abilities to display instantaneous and continues waveforms of pressure, flow, volume and P-V loop similar to the real artificial ventilation. The simulator has ability to use as a good training tool for student.

Recently there has been considerable debate in the scientific community regarding the placenta as the host of a unique microbiome. No studies have addressed the associations of clinical conditions such as maternal obesity, or localizations on the placental microbiome. We examined the placental microbiome in a multi-ethnic maternal pre-pregnant obesity cohort using controls for environmental contaminants and an optimized microbiome protocol to enrich low bacterial biomass samples. We confirmed that a distinct placenta microbiome does exist, as compared to the environmental background. The placenta microbiome consists predominantly of Lactobacillus, Enterococcus and Chryseobacterium. Moreover, the microbiome in the placentas of obese pre-pregnant mothers are less diverse when compared to those of mothers of normal pre-pregnancy weight. Lastly, microbiome richness also decreases from the maternal side to fetal side. In summary, our study reveals associations of placental microbiome with placenta biogeography and with maternal pre-pregnant obesity.

Lilikoi (the Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway-based classification modeling using metabolomics data. Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi and CRAN: https://cran.r-project.org/web/packages/lilikoi/index.html.

Lilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal pre-pregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using coupled untargeted and targeted metabolomics approach, from the newborn cord blood metabolomes associated with a matched maternal pre-pregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multi-ethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using elastic net regularization based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age and ethnicity, parity and gravidity). We validated the model results in a subsequent set of samples (N=30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese vs. norm-weight groups. Interestingly, Hydroxy-3-methylbutyric acid showed significnatly higher levels in cord blood from the NHPI group, compared to asian and caucasian groups. In summary, significant associations were observed between maternal pre-pregnant obesity and offspring metabolomics alternation at birth, revealing the inter-generational impact of maternal obesity.

Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to fully understand disease ontology and to reduce the cost of drug development, gene regulatory networks (GRN) have to be constructed. During the last decade, many GRN inference algorithms like ‘Bayesian network’ that are based on genome-wide data have been developed to unravel the complexity of gene regulation. Recently, many of structure learning algorithms were used to learn Bayesian network that have shown promise in gene regulatory network reconstruction. In this paper we apply different structure learning algorithms on actual microarray data to obtain a better understanding of their relative strengths and weaknesses on the system biology community and we evaluate their outputs from different perspectives.

In the last years numerous methods have been developed and applied to reconstruct the structure and dynamic rules of gene-regulatory networks from different high-throughput data sources such as gene expression data. In this paper we summarized four promising modeling approaches to obtain a better understanding of their relative strengths and weaknesses which, in turn, can have a profound effect on developing techniques for drug testing and therapeutic intervention for effective treatment of human diseases and help the systems biology community.

As basic building blocks of life, genes, as well as their products (proteins), do not work independently. Instead, in order for a cell to function properly, they interact with each other and form a complicated network. Recently, many researchers agree that most of biological ambiguous questions might be easily being answered if a sophisticated modeling of the gene regulatory network (GRN) was constructed. Also, GRN was used to help understanding disease ontology and reducing the cost of drug development. During the last decade, many GRN inference algorithms that are based on microarrays data have been developed to unravel the complexity of gene regulation. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes. The dimensionality and high degree of noise are interesting problems in GRN modeling. In this paper we proposed a new integrated algorithm to overcome these problems. Our software show good performance comparable to previous methods and many of produced edges have evidence in the literature data. The proposed method was applied to time series gene expression data of Saccharomyces Cerevisiae and could potentially be applied to other networks in yeast as well as higher organisms.

One of the main research areas of bioinformatics is functional genomics; which focuses on the interactions and functions of each gene and its products (mRNA, protein) through the whole genome (the entire genetics sequences encoded in the DNA and responsible for the hereditary information). In order to identify the functions of certain gene, we should able to capture the gene expressions which describe how the genetic information converted to a functional gene product through the transcription and translation processes. Functional genomics uses microarray technology to measure the genes expressions levels under certain conditions and environmental limitations. In the last few years, microarray has become a central tool in biological research. Consequently, the corresponding data analysis becomes one of the important work disciplines in bioinformatics. The analysis of microarray data poses a large number of exploratory statistical aspects including clustering and biclustering algorithms, which help to identify similar patterns in gene expression data and group genes and conditions in to subsets that share biological significance.

The Heterogeneous Wireless Network (HWN) integrates different wireless networks into one common network. The integrated networks often overlap coverage in the same wireless service areas, leading to the availability of a great variety of innovative services based on user demands in a cost-efficient manner. Joint Admission Control (JAC) handles all new or handoff service requests in the HWN. It checks whether the incoming service request to the selected Radio Access Network (RAN) by the initial access network selection or the vertical handover module can be admitted and allocated the suitable resources. In this paper, a decision support system is developed to address the JAC problem in the modern HWN networks. This system combines fuzzy logic and the PROMETHEE II multiple criteria decision making system algorithm, to the problem of JAC. This combination decreases the influence of the dissimilar, imprecise, and contradictory measurements for the JAC criteria coming from different sources. A performance analysis is done and the results are compared with traditional algorithms for JAC. These results demonstrate a significant improvement with our developed algorithm.

A new adaptive signal-preserving technique for noise suppression in gene expression data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. The new technique is tested using computer simulations from DREAM3 competition dataset. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Also, this new method outperforms other denoising methods like multi-wavelet algorithm. Moreover, when the new technique is used given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for gene expression data analysis.

Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. Results In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. Conclusions Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.