A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

3D clustering of gene expression data from systemic autoinflammatory diseases using self-organizing maps (Clust3D)

Background and objective

Systemic autoinflammatory diseases (SAIDs) are characterized by widespread inflammation, but for most of them there is a lack of specific biomarkers for accurate diagnosis. Although a number of machine learning algorithms have been used to analyze SAID datasets, aiding in the discovery of novel biomarkers, there is a growing recognition of the importance of SAID timeseries clustering, as it can capture the temporal dynamics of gene expression patterns.

Methodology

This paper proposes a novel clustering methodology to efficiently associate three-dimensional data. The algorithm utilizes competitive learning to create a self-organizing neural network and adjust neuron positions in time-dependent and high dimensional feature space in order to assign them as clustering centers. The quantitative evaluation of the clustering was based on well-known clustering indices. Furthermore, a differential expression analysis and classification pipeline was employed to assess the capability of the proposed methodology to extract more accurate pathway-specific genes from its clusters. For that, a comparative analysis was also conducted against a heuristic timeseries clustering method.

Results

The proposed methodology achieved better overall clustering indices scores and classification metrics using genes derived from its clusters. Notable cases include a threefold increase in the Calinski-Harabasz clustering index, a twofold improvement in the Davies-Bouldin clustering index and a ∼ 60 % increase in the classification specificity score.

Conclusion

A novel clustering methodology was developed and applied on several gene expression timeseries datasets from systemic autoinflammatory diseases, and its ability to efficiently produce well separated clusters compared to existing heuristic methods was demonstrated.

Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield

The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.

Neurological involvement, immune response, and biomarkers in Kawasaki disease along with its pathogenesis, therapeutic and diagnostic updates

Kawasaki disease is an acute, febrile disease that is not typically fatal if treated and affects infants and children more commonly. More than 80% of the afflicted patients are under the age of four. This disease most commonly affects coronary arteries. In a minority of cases, Aneurysms can burst or produce thrombosis, and they can cause infarction. The distinctive redness in the palms and soles of the feet might result from a delayed-type hypersensitivity reaction to a cross-reactive or recently discovered antigen (s). Autoantibodies against epithelial cells and smooth muscle cells are produced as a result of subsequent macromolecule synthesis and polyclonal white blood cell activation, which intensifies the redness. Kawasaki disease's clinical manifestations range from oral skin disease to the blistering of the mucosa, symptoms involving the hands and the feet, skin disease of the palms and soles, a desquamative rash, and cervical lymphatic tissue enlargement (so it is also referred to as tissue layer lymphatic tissue syndrome). Most untreated patients develop some vessel sequelae, from well-organized coronary inflammation to severe arterial blood vessel dilatation to giant artery aneurysms with rupture or occlusion, infarction, and thrombosis. With human gamma globulin administration, reasonable standards of medical care, and the use of analgesics, the speed of symptomatic progression and inflammatory artery changes are reduced. In this review, we have covered the immunology of Kawasaki disease, its biomarkers, and the neurological manifestations of this multisystem illness. We have also included a discussion on its pathogenesis, diagnosis, and treatment.

Neurological involvement, immune response, and biomarkers in Kawasaki disease along with its pathogenesis, therapeutic and diagnostic updates

Kawasaki disease is an acute, febrile disease that is not typically fatal if treated and affects infants and children more commonly. More than 80% of the afflicted patients are under the age of four. This disease most commonly affects coronary arteries. In a minority of cases, Aneurysms can burst or produce thrombosis, and they can cause infarction. The distinctive redness in the palms and soles of the feet might result from a delayed-type hypersensitivity reaction to a cross-reactive or recently discovered antigen (s). Autoantibodies against epithelial cells and smooth muscle cells are produced as a result of subsequent macromolecule synthesis and polyclonal white blood cell activation, which intensifies the redness. Kawasaki disease's clinical manifestations range from oral skin disease to the blistering of the mucosa, symptoms involving the hands and the feet, skin disease of the palms and soles, a desquamative rash, and cervical lymphatic tissue enlargement (so it is also referred to as tissue layer lymphatic tissue syndrome). Most untreated patients develop some vessel sequelae, from well-organized coronary inflammation to severe arterial blood vessel dilatation to giant artery aneurysms with rupture or occlusion, infarction, and thrombosis. With human gamma globulin administration, reasonable standards of medical care, and the use of analgesics, the speed of symptomatic progression and inflammatory artery changes are reduced. In this review, we have covered the immunology of Kawasaki disease, its biomarkers, and the neurological manifestations of this multisystem illness. We have also included a discussion on its pathogenesis, diagnosis, and treatment.

An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers

Pulmonary fibrosing diseases are in the very epicenter of biomedical research both due to their increasing prevalence and their association with SARS-CoV-2 infections. Research of idiopathic pulmonary fibrosis, the most lethal among the interstitial lung diseases, is in need for new biomarkers and potential disease targets, a goal that could be accelerated using machine learning techniques. In this study, we have used Shapley values to explain the decisions made by an ensemble learning model trained to classify samples to an either pulmonary fibrosis or steady state based on the expression values of deregulated genes. This process resulted in a full and a laconic set of features capable of separating phenotypes to an at least equal degree as previously published marker sets. Indicatively, a maximum increase of 6% in specificity and 5% in Mathew's correlation coefficient was achieved. Evaluation with an additional independent dataset showed our feature set having a greater generalization potential than the rest. Ultimately, the proposed gene lists are expected not only to serve as new sets of diagnostic marker elements, but also as a target pool for future research initiatives.

ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.