Global human obesity and political globalization; asymmetric relationship through world human development levels.

Purpose - Political globalization is a crucial and distinct component of strengthening global organizations. Obesity is a global epidemic in a few nations, and it is on the verge of becoming a pandemic that would bring plenty of diseases. This research aims to see how the political globalization index affects worldwide human obesity concerning global human development levels. Methods- To assess any cross-sectional dependence among observed 109 nations, the yearly period from 1990 to 2017 is analyzed using second generation panel data methods. KAO panel cointegration test and Fully Modified Least Square model were used to meet our objectives. Finding- Low level of political globalization tends to increase global human obesity because countries cannot sway international decisions and resources towards them. While the high level of political globalization tends to reduce obesity because it can control and amends international decisions. For the regression model, a fully modified Least Square model was utilized. The study observed that the R squared values for all models are healthy, with a minimum of 87 percent variables explaining differences in global obesity at the country level. Originality: There is very important to tackle the globalization issue to reduce global human obesity. With the simplicity of dietary options and the amount of physical labour they undergo in their agricultural duties, an increase in rural population percentage tends to lower the average national obesity value.

Global impact on human obesity - A robust non-linear panel data analysis.

Purpose: Recent studies in economics showed that humans are bounded rational. This being consumers, they are not perfect judges of what matters for the standard of living. While with a marked increase in economic and social wellbeing, there is a consistent rise in obesity levels, especially in the developed world. Thus, this study intends to explore the empirical and socio-economic antecedents of human obesity across countries using six global indexes. Methods: This study used the data of 40 countries between 1975 to 2018 and used the Panel FGLS Regression with the quadratic specification. Findings: The results showed that health and food indicators increase global human obesity, environment and education indicators decrease global human obesity, and economic and social indicators follow an inverted U-shaped pattern in affecting global human obesity. Originality: Previous studies have used infant mortality and life expectancy as the major health indicator in determining the standard of living while overlooking global human obesity as a major deterrent to welfare. This study has provided a holistic assessment of the causes of obesity in global contexts.

SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks.

BACKGROUND: Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. RESULTS: In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ([Formula: see text] = 0.867, and [Formula: see text]=0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework's performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with [Formula: see text] ranging from 0.879 to 0.931 and [Formula: see text] from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. CONCLUSION: In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php .

COVID-19 pandemic spread against countries' non-pharmaceutical interventions responses: a data-mining driven comparative study.

BACKGROUND: The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020. METHODS: Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans. RESULTS: The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease. CONCLUSIONS: The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.

Realizing drug repositioning by adapting a recommendation system to handle the process.

BACKGROUND: Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. RESULTS: In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. CONCLUSIONS: Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

Correction to: Realizing drug repositioning by adapting a recommendation system to handle the process.

Following publication of the original article [1], the authors reported that there was an error in the spelling of the name of one of the authors.

microRNA 338-3p exhibits tumor suppressor role and its down-regulation is associated with adverse clinical outcome in prostate cancer patients.

MicroRNAs (miRNAs) are small non-coding RNAs that function in transcriptional and post-transcriptional regulation of gene expression. Several miRNAs have been implicated in regulating prostate cancer (PCa) progression. Deregulations of miRNA regulatory networks have been reported in ERG positive PCa, which accounts for ~50 % of PCa and have been suggested to affect tumor aggressiveness. The function of miR338-3p, its prognostic significance, and its association with ERG positive PCa has not been fully investigated. Using microarray expression profiling, we identified miRNA338-3p as among the top deregulated miRNAs associated with ERG status in PCa. We investigated miR338-3p function using in vitro and in vivo experimental models and its expression was assessed and validated in clinical samples and a public cohort of localized and metastatic prostate cancer. miR338-3p was significantly down-regulated with disease progression from benign prostate tissue to primary and metastatic lesions. In localized disease, patients with lower miR338-3p expression levels showed increased association to biochemical recurrence and several adverse pathological parameters compared to patients with higher miRNA338-3p tissue expression levels. Using in vitro PCa cell models, overexpression of miR338-3p resulted in a decrease in cell invasion and expression of chemokine signalling genes CXCL12, CXCR4, and CXCR7. In vivo, orthotropic implantation of PC3 cells stably expressing miR338-3p was associated with a significant decrease in tumor weights compared to control cells. miR338-3p has anti-proliferative and anti-invasive properties. It affects CXCR4 axis, and its down-regulation is associated with adverse clinical outcomes in PCa patients.

Interrogation of ERG gene rearrangements in prostate cancer identifies a prognostic 10-gene signature with relevant implication to patients' clinical outcome.

OBJECTIVES: ERG-gene rearrangement defines a distinct molecular subtype of PCA with potential biological and clinical implications. To identify a molecular signature reflective of the downstream effects of ERG-mediated transcriptional regulation with prognostic implication in patients with prostate cancer (PCA). MATERIAL AND METHODS: We used a singular value decomposition (SVD) bioinformatics approach to re-analyse gene expression data previously generated from 46 prostate tumours, and identified an ERG-like gene signature. The signature was validated on several patient cohorts and individual genes were correlated to ERG expression and PCA progression. RESULTS: An ERG-like 10-gene signature was identified and validated in PCA cohorts of the physician health study (p115) (n = 110) in addition to three independent public datasets, and was significantly associated with disease progression, biochemical recurrence and PCA-specific mortality. Patients with the ERG-like signature were significantly associated with disease recurrence on univariate (hazard ratio [HR] 2.6; 95% confidence interval [CI]:1.3-5.2; P = 0.004) and multivariate analysis (HR 2.3; 95% CI:1.1-4.6, P = 0.016) compared with patients without this signature. Within the group of patients with Gleason score (GS) 6 and 7 PCA, the signature added prognostic value beyond GS and identified patients at higher risk of cancer deaths more accurately than GS alone or in combination with ERG status. Protein expression of the 10 genes were significantly associated with ERG and disease progression regardless of ERG status. CONCLUSION: The characterized ERG-like signature was reflective of aggressive features of ERG-mediated transcription and was prognostically robust. The combination of this signature with clinicopathological variables should be validated prospectively to explore its clinical utility in stratifying patients with PCA and in identifying those at higher risk of metastatic and lethal disease.

LSTM-driven drug design using SELFIES for target-focused de novo generation of HIV-1 protease inhibitor candidates for AIDS treatment.

The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named "LSTM-ProGen" is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.

A survey on brain tumor image analysis.

Medical imaging, also known as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. Medical imaging procedures include non-invasive tests that allow doctors to diagnose injuries and diseases without being intrusive TechTarget (n.d.). A number of tools and techniques are used to automate the analysis of medical images acquired with various image processing methods. The brain is one of the largest and most complex organs of the human body and anomaly detection from brain images (i.e., MRI, CT, PET, etc.) is one of the major research areas of medical image analysis. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning (ML) models, the recent deep learning (DL) models, and various hybrid models are used in brain image analysis. Brain tumors are one of the most common brain diseases with a high mortality rate, and it is difficult to analyze from brain images for the versatility of the shape, location, size, texture, and other characteristics. In this paper, a comprehensive review on brain tumor image analysis is presented with basic ideas of brain tumor, brain imaging, brain image analysis tasks, brain image analysis models, brain tumor image features, performance metrics used for evaluating the models, and some available datasets on brain tumor/medical images. Some challenges of brain tumor analysis are also discussed including suggestions for future research directions. The graphical abstract summarizes the contributions of this paper.

Exploring The Prognostic Significance of SET-Domain Containing 2 (SETD2) Expression in Advanced and Castrate-Resistant Prostate Cancer.

SET-domain containing 2 (SETD2) is a histone methyltransferase and an epigenetic modifier with oncogenic functionality. In the current study, we investigated the potential prognostic role of SETD2 in prostate cancer. A cohort of 202 patients' samples was assembled on tissue microarrays (TMAs) containing incidental, advanced, and castrate-resistant CRPCa cases. Our data showed significant elevated SETD2 expression in advanced and castrate-resistant disease (CRPCa) compared to incidental cases (2.53 ± 0.58 and 2.21 ± 0.63 vs. 1.9 ± 0.68; p < 0.001, respectively). Interestingly, the mean intensity of SETD2 expression in deceased vs. alive patients was also significantly different (2.31 ± 0.66 vs. 2 ± 0.68; p = 0.003, respectively). Overall, high SETD2 expression was found to be considered high risk and was significantly associated with poor prognosis and worse overall survival (OS) (HR 1.80; 95% CI: 1.28-2.53, p = 0.001) and lower cause specific survival (CSS) (HR 3.14; 95% CI: 1.94-5.08, p < 0.0001). Moreover, combining high-intensity SETD2 with PTEN loss resulted in lower OS (HR 2.12; 95% CI: 1.22-3.69, p = 0.008) and unfavorable CSS (HR 3.74; 95% CI: 1.67-8.34, p = 0.001). Additionally, high SETD2 intensity with ERG positive expression showed worse prognosis for both OS (HR 1.99, 95% CI 0.87-4.59; p = 0.015) and CSS (HR 2.14, 95% CI 0.98-4.68, p = 0.058). We also investigated the protein expression database TCPA, and our results showed that high SETD2 expression is associated with a poor prognosis. Finally, we performed TCGA PRAD gene set enrichment analysis (GSEA) data for SETD2 overexpression, and our data revealed a potential association with pathways involved in tumor progression such as the AMPK signaling pathway, the cAMP signaling pathway, and the PI3K-Akt signaling pathway, which are potentially associated with tumor progression, chemoresistance, and a poor prognosis.

Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures.

BACKGROUND: MicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways. A useful step for understanding their functional role is characterizing their influence on the protein context of the targets. Using miRNA context-specific influence as a functional signature is promising to identify functional associations between miRNAs and other gene signatures, and thus advance our understanding of miRNA mode of action. RESULTS: In the current study we utilized the power of regularized regression models to construct functional associations between gene signatures. Genes that are influenced by miRNAs directly(computational miRNA target prediction) or indirectly (protein partners of direct targets) are defined as functional miRNA gene signature. The combined direct and indirect miRNA influence is defined as context-specific effects of miRNAs, and is used to identify regulatory effects of miRNAs on curated gene signatures. Elastic-net regression was used to build functional associations between context-specific effect of miRNAs and other gene signatures (disease, pathway signatures) by identifying miRNAs whose targets are enriched in gene lists. As a proof of concept, elastic-net regression was applied on lists of genes downregulated upon pre-miRNA transfection, and successfully identified the treated miRNA. This model was then extended to construct functional relationships between miRNAs and disease and pathway gene lists. Integrating context-specific effects of miRNAs on a protein network reveals more significant miRNA enrichment in prostate gene signatures compared to miRNA direct targets. The model identified novel list of miRNAs that are associated with prostate clinical variables. CONCLUSIONS: Elastic-net regression is used as a model to construct functional associations between miRNA signatures and other gene signatures. Defining miRNA context-specific functional gene signature by integrating the downstream effect of miRNAs demonstrates better performance compared to the miRNA signature alone (direct targets). miRNA functional signatures can greatly facilitate miRNA research to uncover new functional associations between miRNAs and diseases, drugs or pathways.

A survey of machine learning-based methods for COVID-19 medical image analysis.

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches.

Studying the connection between SF3B1 and four types of cancer by analyzing networks constructed based on published research.

Splicing factor 3B subunit 1 (SF3B1) is the largest component of SF3b protein complex which is involved in the pre-mRNA splicing mechanism. Somatic mutations of SF3B1 were shown to be associated with aberrant splicing, producing abnormal transcripts that drive cancer development and/or prognosis. In this study, we focus on the relationship between SF3B1 and four types of cancer, namely myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and chronic lymphocytic leukemia (CLL) and breast cancer (BC). For this purpose, we identified from the Pubmed library only articles which mentioned SF3B1 in connection with the investigated types of cancer for the period 2007 to 2018 to reveal how the connection has developed over time. We left out all published articles which mentioned SF3B1 in other contexts. We retrieved the target articles and investigated the association between SF3B1 and the mentioned four types of cancer. For this we utilized some of the publicly available databases to retrieve gene/variant/disease information related to SF3B1. We used the outcome to derive and analyze a variety of complex networks that reflect the correlation between the considered diseases and variants associated with SF3B1. The results achieved based on the analyzed articles and reported in this article illustrated that SF3B1 is associated with hematologic malignancies, such as MDS, AML, and CLL more than BC. We found that different gene networks may be required for investigating the impact of mutant splicing factors on cancer development based on the target cancer type. Additionally, based on the literature analyzed in this study, we highlighted and summarized what other researchers have reported as the set of genes and cellular pathways that are affected by aberrant splicing in cancerous cells.

Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust.

Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.

Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning.

Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan-Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.

Serrate RNA Effector Molecule (SRRT) Is Associated with Prostate Cancer Progression and Is a Predictor of Poor Prognosis in Lethal Prostate Cancer.

Arsenite-resistance protein 2, also known as serrate RNA effector molecule (ARS2/SRRT), is known to be involved in cellular proliferation and tumorigenicity. However, its role in prostate cancer (PCa) has not yet been established. We investigated the potential role of SRRT in 496 prostate samples including benign, incidental, advanced, and castrate-resistant patients treated by androgen deprivation therapy (ADT). We also explored the association of SRRT with common genetic aberrations in lethal PCa using immunohistochemistry (IHC) and performed a detailed analysis of SRRT expression using The Cancer Genome Atlas (TCGA PRAD) by utilizing RNA-seq, clinical information (pathological T category and pathological Gleason score). Our findings indicated that high SRRT expression was significantly associated with poor overall survival (OS) and cause-specific survival (CSS). SRRT expression was also significantly associated with common genomic aberrations in lethal PCa such as PTEN loss, ERG gain, mutant TP53, or ATM. Furthermore, TCGA PRAD data revealed that high SRRT mRNA expression was significantly associated with higher Gleason scores, PSA levels, and T pathological categories. Gene set enrichment analysis (GSEA) of RNAseq data from the TCGA PRAD cohort indicated that SRRT may play a potential role in regulating the expression of genes involved in prostate cancer aggressiveness. Conclusion: The current data identify the SRRT's potential role as a prognostic for lethal PCa, and further research is required to investigate its potential as a therapeutic target.

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier.

We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which efficiently works with time-series numerical datasets. In this study, we have extended SNAc to work with any type of tabular data by showing its classification efficiency on a broader collection of datasets that may contain numerical and categorical features. This version of GSNAc simply works by transforming traditional tabular data into a network where samples of the tabular dataset are represented as nodes and similarities between the samples are reflected as edges connecting the corresponding nodes. The raw network graph is further simplified and enriched by its edge space to extract a visualizable 'graph classifier model-GCM'. The concept of the GSNAc classification model relies on the study of node similarities over network graphs. In the prediction step, the GSNAc model maps test nodes into GCM, and evaluates their average similarity to classes by employing vectorial and topological metrics. The novel side of this research lies in transforming multidimensional data into a 2D visualizable domain. This is realized by converting a conventional dataset into a network of 'samples' and predicting classes after a careful and detailed network analysis. We exhibit the classification performance of GSNAc as an effective classifier by comparing it with several well-established machine learning classifiers using some popular benchmark datasets. GSNAc has demonstrated superior or comparable performance compared to other classifiers. Additionally, it introduces a visually comprehensible process for the benefit of end-users. As a result, the spin-off contribution of GSNAc lies in the interpretability of the prediction task since the process is human-comprehensible; and it is highly visual.

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods.

The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS) and Support Vector Machine (SVM). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20.