Life Sciences Commons^™

Suma: A Lightweight Machine Learning Model Powered Shared Nearest Neighbour Based Clustering Application Interface Of Scrna-Seq, Hamza Umut Karakurt, Pınar Pir

Turkish Journal of Biology

Background/aim: Single-cell transcriptomics (scRNA-Seq) explores cellular diversity at the gene expression level. Due to the inherent sparsity and noise in scRNA-Seq data and the uncertainty on the types of sequenced cells, effective clustering and cell type annotation are essential. The graph-based clustering of scRNA-Seq data is a simple yet powerful approach which presents data as a “shared nearest neighbour” graph and clusters the cells using graph clustering algorithms. These algorithms are dependent on several user-defined parameters. Here we present SUMA, a lightweight tool that uses a random forest model to predict the optimum number of neighbours to have the optimum …

Interpretable Mechanistic And Machine Learning Models For Pre-Dicting Cardiac Remodeling From Biochemical And Biomechanical Features, Anamul Haque

All Dissertations

Biochemical and biomechanical signals drive cardiac remodeling, resulting in altered heart physiology and the precursor for several cardiac diseases, the leading cause of death for most racial groups in the USA. Reversing cardiac remodeling requires medication and device-assisted treatment such as Cardiac Resynchronization Therapy (CRT), but current interventions produce highly variable responses from patient to patient. Mechanistic modeling and Machine learning (ML) approaches have the functionality to aid diagnosis and therapy selection using various input features. Moreover, 'Interpretable' machine learning methods have helped make machine learning models fairer and more suited for clinical application. The overarching objective of this doctoral …

Optimization And Application Of Graph Neural Networks, Shuo Zhang

Dissertations, Theses, and Capstone Projects

Graph Neural Networks (GNNs) are widely recognized for their potential in learning from graph-structured data and solving complex problems. However, optimal performance and applicability of GNNs have been an open-ended challenge. This dissertation presents a series of substantial advances addressing this problem. First, we investigate attention-based GNNs, revealing a critical shortcoming: their ignorance of cardinality information that impacts their discriminative power. To rectify this, we propose Cardinality Preserved Attention (CPA) models that can be applied to any attention-based GNNs, which exhibit a marked improvement in performance. Next, we introduce the Directional Node Pair (DNP) descriptor and the Robust Molecular Graph …

Machine Learning Techniques For Improved Functional Brain Parcellation, Da Zhi

Electronic Thesis and Dissertation Repository

Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques.

To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel …

Sequestered Sequences: A Bioinformatic Approach To The Forgotten Genome, Dylan Barth

UNLV Theses, Dissertations, Professional Papers, and Capstones

As high throughput sequencing generates ever increasing amounts of genetic and epigenetic data new lines of inquiry open up in the field of genomic research. In this thesis, we discuss three ways in which we can utilize public databases of next generation genomic data in order to study areas of the genome previously ignored by traditional approaches. These include the study of linker regions between domains of proteins, indirect enhancers that do not strongly contact promoters of genes they regulate, and transposon-derived enhancer elements. The work uncovers many exceptions to known biological principles, and adds nuance to our understanding of …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Neural Correlates Of Post-Traumatic Brain Injury (Tbi) Attention Deficits In Children, Meng Cao

Dissertations

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can …

Marginal Agricultural Land Identification In The Lower Mississippi Alluvial Valley, Prakash Tiwari

Theses and Dissertations

This study identified marginal agricultural lands in the Lower Mississippi Alluvial Valley using crop yield predicting models. The Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were trained and validated using county-level crop yield data, climate data, soil properties, and Normalized Difference Vegetation Index (NDVI). The RFR model outperformed MLR model in estimating soybean and corn yields, with an index of agreement (d) of 0.98 and 0.96, Nash-Sutcliffe model efficiency (NSE) of 0.88 and 0.93, and root mean square error (RMSE) of 9.34% and 5.84%, respectively. Marginal agricultural lands were estimated to 26,366 hectares using cost and sales …

Computational Analysis Of Microbial Sequence Data Using Statistics And Machine Learning, Zhixiu Lu

Doctoral Dissertations

Since the discovery of the double helix of DNA in 1953, modern molecular biology has opened the door to a better understanding of how genes control chemical processes within cells, including protein synthesis. Although we are still far from claiming a complete understanding, recent advances in sequencing technologies, increased computational capacity, and more sophisticated computational methods have allowed the development of various new applications that provide further insight into DNA sequence data and how the information they encode impacts living organisms and their environment. Sequencing data can now be used to start identifying the relationships between microorganisms, where they live, …

Deep Learning Can Be Used To Classify And Segment Plant Cell Types In Xylem Tissue, Sean Wu, Reem Al Dabagh, Fabien Scalzo, Helen Irene Holmlund

Seaver College Research And Scholarly Achievement Symposium

Studies of plant anatomical traits are essential for understanding plant physiological adaptations to stressful environments. For example, shrubs in the chaparral ecosystem of southern California have adapted various xylem anatomical traits that help them survive drought and freezing. Previous studies have shown that xylem conduits with a narrow diameter allows certain chaparral shrub species to survive temperatures as low as -12 C. Other studies have shown that increased cell wall thickness of fibers surrounding xylem vessels improves resistance to water stress-induced embolism formation. Historically, these studies on xylem anatomical traits have relied on hand measurements of cells in light micrographs, …

Figures In Biological Journal Articles Are Often Unfriendly To People With Color Vision Deficiencies, Harlan Stevens, Arwen Oakley, Stephen Piccolo

Library/Life Sciences Undergraduate Poster Competition 2023

8% of men have red-green color vision deficiencies (CVD).

We focused on deuteranomaly and deuteranopia (deficiency in seeing green) since it is by far the most common CVD.

Colorblind unfriendly figures hinder equity in research and discourage individuals with CVD from pursuing science.

To determine how often researchers use colorblind-unfriendly figures, we classified and labeled 5000 images.

Our annotated dataset will be freely available in the hope that it will prove helpful to other researchers.

We created a computer vision model using a Convolutional Neural Network (CNN) to classify images as colorblind-friendly or not.

Anomaly-Based Network Intrusion Detection System Using Deep Intelligent Technique, Sardar Kh. Hassan, Muhammadamin A. Daneshwar

Polytechnic Journal

Computer systems and network infrastructures are still exposed to many security risks and cyber-attack vulnerabilities despite advancements of information security. Traditional signature-based intrusion detection systems and security solutions by matching rule-based mechanism and prior knowledge are insufficient of fully protecting computer networks against novel attacks. For this purpose, Anomaly-based Network Intrusion Detection System (A-NIDS) as cyber security tool is considered for identifying and detecting anomalous behavior in the flow-based network traffic alongside with firewalls and other security measures.The main objective of the research is to improve the detection rate and reduce false-positive rates of the classifier using anomaly-based technique.

Anomaly-Based Network Intrusion Detection System Using Deep Intelligent Technique, Sardar Kh. Hassan, Muhammadamin A. Daneshwar

Polytechnic Journal

Computer systems and network infrastructures are still exposed to many security risks and cyber-attack vulnerabilities despite advancements of information security. Traditional signature-based intrusion detection systems and security solutions by matching rule-based mechanism and prior knowledge are insufficient of fully protecting computer networks against novel attacks. For this purpose, Anomaly-based Network Intrusion Detection System (A-NIDS) as cyber security tool is considered for identifying and detecting anomalous behavior in the flow-based network traffic alongside with firewalls and other security measures.The main objective of the research is to improve the detection rate and reduce false-positive rates of the classifier using anomaly-based technique.

Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud

Honors Undergraduate Theses

This paper investigates the impact the LASSO, mRMR, SHAP, and Reinforcement Feature Selection techniques on random forest models for the breast cancer subtypes markers ER, HER2, PR, and TN as well as identifying a small subset of biomarkers that could potentially cause the disease and explain them using explainable AI techniques. This is important because in areas such as healthcare understanding why the model makes a specific decision is important it is a diagnostic of an individual which requires reliable AI. Another contribution is using feature selection methods to identify a small subset of biomarkers capable of predicting if a …

Methods For Improving Potassium Fertilizer Recommendations For Corn In South Dakota, Andrew J. Ahlersmeyer

Electronic Theses and Dissertations

Corn (Zea mays L.) is a vital commodity in South Dakota’s agricultural sector. Optimal corn production occurs when there are sufficient mineral nutrients in the soil, especially potassium (K). Applications of K fertilizer are used when soil test K (STK) levels are deficient. Therefore, producers need reliable, thoroughly tested fertilizer recommendations to make profitable decisions and maintain environmental stewardship. South Dakota K fertilizer recommendations have not been updated in nearly 20 years. Simultaneously, changes in corn genetics, management practices, and climate patterns suggest that the critical soil test value (CSTV) for STK may have shifted in that same time frame. …

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the …

The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku

Graduate Research Theses & Dissertations

Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …

Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi

All Works

No abstract provided.

Improving The Utility Of Precision Agriculture Through Machine Learning And Climate-Smart Practices, Skye Brugler

Electronic Theses and Dissertations

Climate Smart Practices are management strategies that focus on increasing soil and crop productivity, utilize site-specific strategies to increase resiliency against the effects of climate change, and mitigate these negative effects by reducing greenhouse gas (GHG) emissions. Decision Support Systems (DSSs) using machine learning (ML) can adjust models based on new information and help farmers make climate smart decisions within their operation. The 4R nutrient management model of right source, rate, location, and time also demonstrates a framework that may be considered climate smart by improving soil and crop productivity. However, when initially conceptualized, the 4R model did not consider …

Three Essays On The U.S. Beef Supply Chain: Production, Marketing, And Price Dynamics, Erdal Erol

Theses and Dissertations--Agricultural Economics

This dissertation contains three essays on select economic components of the U.S. beef industry. The first and second essays concentrate on the different economic problems in beef cattle production. The third essay evaluates the price dynamics and the impact of COVID-19 along the beef supply chain.

The first essay explores the economics of culling decisions in cow-calf operations in the U.S. with a novel application of a dynamic mathematical programming model. The results provide an optimal culling strategy under the base model and a range of optimal strategies that vary with respect to different components such as fertility probabilities, market …