Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Life Sciences
Optimization And Application Of Graph Neural Networks, Shuo Zhang
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 And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane
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 …
Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud
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 …
The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku
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 …
Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn
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 …
Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi
Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi
All Works
No abstract provided.