257 research outputs found

    An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification

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    Malaria parasites introduce outstanding life-phase variations as they grow across multiple atmospheres of the mosquito vector. There are transcriptomes of several thousand different parasites. (RNA-seq) Ribonucleic acid sequencing is a prevalent gene expression tool leading to better understanding of genetic interrogations. RNA-seq measures transcriptions of expressions of genes. Data from RNA-seq necessitate procedural enhancements in machine learning techniques. Researchers have suggested various approached learning for the study of biological data. This study works on ICA feature extraction algorithm to realize dormant components from a huge dimensional RNA-seq vector dataset, and estimates its classification performance, Ensemble classification algorithm is used in carrying out the experiment. This study is tested on RNA-Seq mosquito anopheles gambiae dataset. The results of the experiment obtained an output metrics with a 93.3% classification accuracy

    A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

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    Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively

    An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification

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    Malaria parasites adopt outstanding variation of life phases as they evolve through manifold mosquito vector atmospheres. Transcriptomes of thousands of individual parasites exists. Ribonucleic acid sequencing (RNA-seq) is a widespread method for gene expression which has resulted into improved understandings of genetical queries. RNA-seq compute transcripts of gene expressions. RNA-seq data necessitates analytical improvements of machine learning techniques. Several learning approached have been proposed by researchers for analysing biological data. In this study, PCA feature extraction algorithm is used to fetch latent components out of a high dimensional malaria vector RNA-seq dataset, and evaluates it classification performance using an Ensemble classification algorithm. The effectiveness of this experiment is validated on aa mosquito anopheles gambiae RNA-Seq dataset. The experiment result achieved a relevant performance metrics with a classification accuracy of 93.3%

    Blood biomarker-based classification study for neurodegenerative diseases

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    \ua9 2023, Springer Nature Limited. As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer\u27s (AD) and Parkinson\u27s disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer\u27s (ROC AUC = 0.810) and Parkinson\u27s (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture

    A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

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    Malaria larvae embrace unpredictable variable life periods as they spread across many stratospheres of the mosquito vectors. There are transcriptomes of a thousand distinct species. Ribonucleic acid sequencing (RNA-seq) is a ubiquitous gene expression strategy that contributes to the improvement of genetic survey recognition. RNA-seq measures gene expression transcripts data, including methodological enhancements to machine learning procedures. Scientists have suggested many addressed learning for the study of biological evidence. An enhanced optimized Genetic Algorithm feature selection technique is used in this analysis to obtain relevant information from a high-dimensional Anopheles gambiae dataset and test its classification using SVM-Kernel algorithms. The efficacy of this assay is tested, and the outcome of the experiment obtained an accuracy metric of 93% and 96% respectively

    Unsupervised machine learning of high dimensional data for patient stratification

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    The development mechanisms of numerous complex, rare diseases are largely unknown to scientists partly due to their multifaceted heterogeneity. Stratifying patients is becoming a very important objective as we further research that inherent heterogeneity which can be utilised towards personalised medicine. However, considerable difficulties slow down accurate patient stratification mainly represented by outdated clinical criteria, weak associations or simple symptom categories. Fortunately, immense steps have been taken towards multiple omic data generation and utilisation aiming to produce new insights as in exploratory machine learning which showed the potential to identify the source of disease mechanisms from patient subgroups. This work describes the development of a modular clustering toolkit, named Omada, designed to assist researchers in exploring disease heterogeneity without extensive expertise in the machine learning field. Subsequently, it assesses Omada’s capabilities and validity by testing the toolkit on multiple data modalities from pulmonary hypertension (PH) patients. I first demonstrate the toolkit’s ability to create biologically meaningful subgroups based on whole blood RNA-seq data from H/IPAH patients in the manuscript “Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood”. Our work on the manuscript titled “Diagnostic miRNA signatures for treatable forms of pulmonary hypertension highlight challenges with clinical classification” aimed to apply the same clustering approach on a PH microRNA dataset as a first step in forming microRNA diagnostic signatures by recognising the potential of microRNA expression in identifying diverse disease sub-populations irrespectively of pre-existing PH classes. The toolkit’s effectiveness on metabolite data was also tested. Lastly, a longitudinal clustering approach was explored on activity readouts from wearables on COVID-19 patients as part of our manuscript “Unsupervised machine learning identifies and associates trajectory patterns of COVID-19 symptoms and physical activity measured via a smart watch”. Two clusters of high and low activity trajectories were generated and associated with symptom classes showing a weak but interesting relationship between the two. In summary, this thesis is examining the potential of patient stratification based on several data types from patients that represent a new, unseen picture of disease mechanisms. The tools presented provide important indications of distinct patient groups and could generate the insights needed for further targeted research and clinical associations that can help towards understanding rare, complex diseases

    Genes and gene expression modules associated with caloric restriction and aging in the laboratory mouse

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    <p>Abstract</p> <p>Background</p> <p>Caloric restriction (CR) counters deleterious effects of aging and, for most mouse genotypes, increases mean and maximum lifespan. Previous analyses of microarray data have identified gene expression responses to CR that are shared among multiple mouse tissues, including the activation of anti-oxidant, tumor suppressor and anti-inflammatory pathways. These analyses have provided useful research directions, but have been restricted to a limited number of tissues, and have focused on individual genes, rather than whole-genome transcriptional networks. Furthermore, CR is thought to oppose age-associated gene expression patterns, but detailed statistical investigations of this hypothesis have not been carried out.</p> <p>Results</p> <p>Systemic effects of CR and aging were identified by examining transcriptional responses to CR in 17 mouse tissue types, as well as responses to aging in 22 tissues. CR broadly induced the expression of genes known to inhibit oxidative stress (e.g., <it>Mt1</it>, <it>Mt2</it>), inflammation (e.g., <it>Nfkbia</it>, <it>Timp3</it>) and tumorigenesis (e.g., <it>Txnip</it>, <it>Zbtb16</it>). Additionally, a network-based investigation revealed that CR regulates a large co-expression module containing genes associated with the metabolism and splicing of mRNA (e.g., <it>Cpsf6</it>, <it>Sfpq</it>, <it>Sfrs18</it>). The effects of aging were, to a considerable degree, similar among groups of co-expressed genes. Age-related gene expression patterns characteristic of most mouse tissues were identified, including up regulation of granulin (<it>Grn</it>) and secreted phosphoprotein 1 (<it>Spp1</it>). The transcriptional association between CR and aging varied at different levels of analysis. With respect to gene subsets associated with certain biological processes (e.g., immunity and inflammation), CR opposed age-associated expression patterns. However, among all genes, global transcriptional effects of CR were only weakly related to those of aging.</p> <p>Conclusion</p> <p>The study of aging, and of interventions thought to combat aging, has much to gain from data-driven and unbiased genomic investigations. Expression patterns identified in this analysis characterize a generalized response of mammalian cells to CR and/or aging. These patterns may be of importance in determining effects of CR on overall lifespan, or as factors that underlie age-related disease. The association between CR and aging warrants further study, but most evidence indicates that CR does not induce a genome-wide "reversal" of age-associated gene expression patterns.</p

    Blood

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    This book examines both the fluid and cellular components of blood. After the introductory section, the second section presents updates on various topics in hemodynamics. Chapters in this section discuss anemia, 4D flow MRI in cardiology, cardiovascular complications of robot-assisted laparoscopic pelvic surgery, altered perfusion in multiple sclerosis, and hemodynamic laminar shear stress in oxidative homeostasis. The third section focuses on thalassemia with chapters on diagnosis and screening for thalassemia, high blood pressure in beta-thalassemia, and hepatitis C infection in thalassemia patients

    Multiplexed affinity peptidomic assays: multiplexing and applications for testing protein biomarkers

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    Biomarkers are increasingly used in a wide range of areas such as sports and clinical diagnostics, biometric applications, forensic analysis and population screening. Testing for such biomarkers requires substantial resources and has traditionally involved centralised laboratory testing. From cancer diagnosis to COVID testing, there is an increasing demand for protein based assays that are portable, easy to use and ideally multiplexed, so that more than one biomarker can be tested at the same time, thus increasing the throughput and reducing time of the analysis and potentially the costs. Events in recent years, not least the ongoing investigations into claims of widespread state-sponsored doping schemes in sport and the COVID-19 pandemic of 2020 highlight the ever-growing requirement and importance of such tests across multiple frontiers. The project evaluated the feasibility of new antipeptide affinity reagents and suitable technologies for application to multiplexed affinity assays geared towards quantitatively analysing a range of analytes. In the first part of this project, key protein biomarkers available from blood serum and covering a range of conditions including cancer, inflammation, and various behavioural traits were chosen from the literature. Peptide antigens for the development of antipeptide polyclonal antibodies for each protein were selected following in silico proteolysis and ranking of the peptides using an algorithm devised as part of this research. A microarray format was used to achieve spatial multiplexing and increase throughput of the assays. The arrays were evaluated experimentally and were tested for their usability for studying up/down regulation of the target biomarkers in human sera samples. Another protein assay format tested for compatibility with affinity peptidomics approach was a gold nanoparticle based lateral flow test. An affinity-based lateral flow test device was built and used for the detection of the benzodiazepine Valium. Here spectral multiplexing of detection was considered. The principle was tested using quantum dot nanoparticles instead of traditionally used gold nanoparticles. The spectral deconvolution was achieved for mixtures containing up to six differently sized quantum dots. In the final part of this project, a search for novel peptide affinity reagents against insulin growth-like factor 1 (IGF-1) was conducted using phage display. Four peptides were identified after screening a phage display library, and the binding of these peptides to IGF-1 was compared to that of traditional antibody
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