RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes

Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients. 2022, The Author(s).The authors would like to thank all the patients, volunteers, and the healthcare co-workers from Allergy and Immunology Section-HMC, and Dr. Mohamed G.H. Mohamedali, Mr. Hassen Maatoug, and Mr. Ahmed Soliman from Hezm Mebairek General Hospital-HMC for developing disposable racks for samples transportation, tubes labeling, blood collection, and handling. We thank the support provided by Qatar University Biomedical Research Centre, Biosafety Level 3, and Associate Professor Hadi M. Yassine (M.Sc., Ph.D.). We also acknowledge the help of the Anti-Doping Lab-Qatar (ADLQ) and Qatar Red Crescent (QRC) for recruiting control samples. This work was supported by a grant fund from Hamad Medical Corporation (fund number MRC-05-003) and core funding from Qatar Biomedical Research Institute (QBRI).Scopu

Age, Disease Severity and Ethnicity Influence Humoral Responses in a Multi-Ethnic COVID-19 Cohort

The COVID-19 pandemic has affected all individuals across the globe in some way. Despite large numbers of reported seroprevalence studies, there remains a limited understanding of how the magnitude and epitope utilization of the humoral immune response to SARS-CoV-2 viral anti-gens varies within populations following natural infection. Here, we designed a quantitative, multi-epitope protein microarray comprising various nucleocapsid protein structural motifs, including two structural domains and three intrinsically disordered regions. Quantitative data from the microarray provided complete differentiation between cases and pre-pandemic controls (100% sensitivity and specificity) in a case-control cohort (n = 100). We then assessed the influence of disease severity, age, and ethnicity on the strength and breadth of the humoral response in a multi-ethnic cohort (n = 138). As expected, patients with severe disease showed significantly higher antibody titers and interestingly also had significantly broader epitope coverage. A significant increase in antibody titer and epitope coverage was observed with increasing age, in both mild and severe disease, which is promising for vaccine efficacy in older individuals. Additionally, we observed significant differences in the breadth and strength of the humoral immune response in relation to ethnicity, which may reflect differences in genetic and lifestyle factors. Furthermore, our data enabled localization of the immuno-dominant epitope to the C-terminal structural domain of the viral nucleocapsid protein in two independent cohorts. Overall, we have designed, validated, and tested an advanced serological assay that enables accurate quantitation of the humoral response post natural infection and that has revealed unexpected differences in the magnitude and epitope utilization within a population

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Primal-dual framework for feature selection using least squares support vector machines

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Denoised Kernel Spectral Data Clustering

© 2016 IEEE. Kernel Spectral Clustering (KSC) solves a weighted kernel principal component analysis problem in a primal-dual optimization framework. It builds an unsupervised model on a small subset of data using the dual solution of the optimization problem. This allows KSC to have a powerful out-of-sample extension property leading to good cluster generalization w.r.t. unseen data points. However, in the presence of noise that causes overlapping data, the technique often fails to provide good generalization capability. In this paper, we propose a two-step process for clustering noisy data. We first denoise the data using kernel principal component analysis (KPCA) with a recently proposed Model selection criterion based on point-wise Distance Distributions (MDD) to obtain the underlying information in the data. We then use the KSC technique on this denoised data to obtain good quality clusters. One advantage of model based techniques is that we can use the same training and validation set for denoising and for clustering. We discovered that using the same kernel bandwidth parameter obtained from MDD for KPCA works efficiently with KSC in combination with the optimal number of clusters k to produce good quality clusters. We compare the proposed approach with normal KSC and KSC with KPCA using a heuristic method based on reconstruction error for several synthetic and real-world datasets to showcase the effectiveness of the proposed approach.status: publishe