Biology subjects

Abbas, M.

Publications and source records attributed to Abbas, M..

21 records · Page 2Linked to original sources

bioRxiv2020

Machine Learning Based Refined Differential Gene Expression Analysis of Pediatric Sepsis

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSDifferential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provide a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches. MethodsIn this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure. ResultsUsing gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC (AUC) score of 0.89. ConclusionsMachine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.

bioinformatics↗

bioRxiv2019

Biomarker discovery in inflammatory bowel diseases using network-based feature selection

Reliable identification of inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring networks from metagenomics data, and nine different methods for prioritizing biomarkers as well as a hybrid approach combining best traditional and NBBD based feature selection. We also examine how the performance of the predictive models for IBD diagnosis varies as a function of the size of the data used for biomarker identification. Our results show that (i) NBBD is competitive with some of the state-of-the-art feature selection methods including Random Forest Feature Importance (RFFI) scores; and (ii) NBBD is especially effective in reliably identifying IBD biomarkers when the number of data samples available for biomarker discovery is small.

bioinformatics↗

bioRxiv2019

Development of a deep amplicon sequencing method to determine the proportional species composition of piroplasm haemoprotozoa as an aid in their control

Piroplasmosis is caused by tick-borne haemoprotozoa of the genera Theileria and Babesia. These parasitic infections can cause serious impact on the health of livestock and production. Multiple piroplasm species can infect a single host, but reliable molecular diagnostic tools are needed with which to understand the composition of these complex parasite communities. Theileria and Babesia vary in their epidemiology, drug sensitivity, pathogenicity and interaction of co-infecting species, but are similar in the animals, become persistent carriers after recovery from primary infection, acting as reservoir hosts. Here, we describe for the first time the use of a deep amplicon sequencing platform to identify proportions of piroplasm species in co-infecting communities and develop the concept of a \"haemoprotobiome\". First, four phenotypically-verified species of Theileria and Babesia were used to prepare mock pools with random amounts of the parasites and amplified with four different numbers of PCR cycles to assess sequence representation of each species. Second, we evaluated the detection threshold of the deep amplicon sequencing assay for each of the four species and to assess the accuracy of proportional quantification of all four species. Finally, we applied the assay to the field samples to afford insight of the species composition of piroplasm communities in small and large ruminants in the Punjab province of Pakistan. The \"haemoprotobiome\" concept has several potential applications in veterinary and human research, including understanding of responses to drug treatment; parasite epidemiology and ecology; species interactions during mixed infections; and parasite control strategies.

microbiology↗