Biology subjects

Islam, M. M.

Publications and source records attributed to Islam, M. M..

42 records · Page 3Linked to original sources

bioRxiv2019

Retinal Vessels Detection Using Convolutional Neural Networks in Fundus Images

Computer-aided detection (CAD) system is a realistic option for physicians to screen fundus images. Automated segmentation of retinal vessel is in fundus important step to identify the retinal disease region. However, identification of the retinal disease region accurately is still challenging due to the varied distribution of blood vessel on noisy and low contrast fundus images. Healthcare system has been changing significantly with the emergence of machine learning (ML), deep learning (DL) and artificial intelligence (AI) in recent year. Retinal vessel detection is one such area of application of deep learning, for improving the accuracy of detection and segmentation and the quality of patient care. Recently, the convolutional neural networks (CNN) have been applied to the detection of the retinal vessel from fundus images and have demonstrated promising results. The range of accuracy of the CNN model was 0.91-0.95 and the area under the receiver operating curve was 0.09-0.98. Therefore, CNN may play a crucial role in determining the therapeutic methods and detecting the retinal vessel accurately in an individual manner. In this survey, we described the use of CNN in fundus imaging, especially focused on CNN technique, clinical application for retinal vessel detection and future prospective.

bioinformatics↗

bioRxiv2019

Machine Learning Models of Breast Cancer Risk Prediction

Breast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.

bioinformatics↗

bioRxiv2019

Metabolic Modeling Elucidates the Transactions in the Rumen Microbiome and the Shifts upon Virome Interactions

The complex microbial ecosystem within the bovine rumen plays a crucial role in host nutrition, health, and environmental impact. However, little is known about the interactions between the functional entities within the system, which dictates the community structure and functional dynamics and host physiology. With the advancements in high-throughput sequencing and mathematical modeling, in silico genome-scale metabolic analysis promises to expand our understanding of the metabolic interplay in the community. In an attempt to understand the interactions between microbial species and the phages inside rumen, a genome-scale metabolic modeling approach was utilized by using key members in the rumen microbiome (a bacteroidete, a firmicute, and an archaeon) and the viral phages associated with them. Individual microbial host models were integrated into a community model using multi-level mathematical frameworks. An elaborate and heuristics-based computational procedure was employed to predict previously-unknown interactions involving the transfer of fatty acids, vitamins, coenzymes, amino acids, and sugars among the community members. While some of these interactions could be inferred by the available multi-omic datasets, our proposed method provides a systemic understanding of why these occur and how these affect the dynamics in a complex microbial ecosystem. To elucidate the functional role of the virome on the microbiome, local alignment search was used to identify the metabolic functions of the viruses associated with the hosts. The incorporation these functions demonstrated the role of viral auxiliary metabolic genes in relaxing the metabolic bottlenecks in the microbial hosts and complementing the inter-species interactions. Finally, a comparative statistical analysis of different biologically significant community fitness criteria identified the variation in flux space and robustness of metabolic capacities of the community members. Our elucidation of metabolite exchange among three members of rumen microbiome shows how their genomic differences and interactions with the viral strains shape up a highly sophisticated metabolic interplay and explains how such interactions across kingdoms can cause metabolic and compositional shifts in the community and affect the health, nutrition, and pathophysiology of the ruminant animal.

systems biology↗

bioRxiv2019

An integrated computational and experimental study to elucidate Staphylococcus aureus metabolism

Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understanding its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 non-essential mutants from the Nebraska Transposon Mutant Library was performed and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 840 metabolic genes, 1442 metabolites, and 1566 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

systems biology↗

bioRxiv2019

Rational nanotoolbox with theranostic potential for medicated pro-regenerative corneal implants

Cornea diseases are a leading cause of blindness and the disease burden is exacerbated by the increasing shortage around the world for cadaveric donor corneas. Despite the advances in the field of regenerative medicine, successful transplantation of laboratory made artificial corneas has not been fully realised in clinical practice. The causes of failure of such artificial corneal implants are multifactorial and include latent infections from viruses and other micorbes, enzyme over-expression, implant degradation, extrusion or delayed epithelial regeneration. Therefore, there is an urgent unmet need for developing customized corneal implants to suit the host environment, counter the effects of inflammation or infection and that are able to track early signs of implant failure in situ. In the present work, we describe a nano toolbox comprising tools for drug release and in addition capable of being infection responsive, promoting regeneration including non-invasive monitoring of in situ corneal environment. These nano constructs can be incorporated within pro-regenerative biosynthetic implants, transforming them into theranostic devices able to respond to biological changes following implantation.

biochemistry↗

bioRxiv2018

A multiplexed homology-directed DNA repair assay reveals the impact of ~1,700 BRCA1 variants on protein function

Loss-of-function mutations in BRCA1 confer a predisposition to breast and ovarian cancer. Genetic testing for mutations in the BRCA1 gene frequently reveals a missense variant for which the impact on the molecular function of the BRCA1 protein is unknown. Functional BRCA1 is required for homology directed repair (HDR) of double-strand DNA breaks, a key activity for maintaining genome integrity and tumor suppression. Here we describe a multiplex HDR reporter assay to simultaneously measure the effect of hundreds of variants of BRCA1 on its role in DNA repair. Using this assay, we measured the effects of ~1,700 amino acid substitutions in the first 302 residues of BRCA1. Benchmarking these results against variants with known effects, we demonstrate accurate discrimination of loss-of-function versus benign variants. We anticipate that this assay can be used to functionally characterize BRCA1 missense variants at scale, even before the variants are observed in results from genetic testing.

genomics↗