A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Computational Tools For Protein Classification In Metagenomes, Fawad Ullah

Dissertations, Master's Theses and Master's Reports

Advances in genomic sequencing have dramatically increased the amount and the speed at which genomic data is being generated. These technological advances have enabled the ability to profile the genetic information of organisms and communities at unprecedented scales. Many methods have been developed to identify and classify genes within these datasets. However, many generic pipelines for gene annotation struggle to accurately predict specific protein classes that may not be represented in their databases. Two major challenges exist for classification of specific protein classes in metagenomic databases. The first is the fact that many metagenomic assemblies are highly fragmented with many …

Mobula, Bioinspiration, Filter Feeding, Form And Function, J. B. Teeple, S. R. Kahane-Rapport, K. E. Cohen, L. Hamann, J. A. Strother, E. W.M. Paig-Tran

Biological Sciences Faculty Publications

Mobulas (manta and devil rays) are large-scale ram filter feeders that separate planktonic food particles from large volumes of water with minimal clogging. This contrasts with most human-made filters that can suffer from problematic clogging requiring additional mechanisms for clearing blocked surfaces and maintaining performance. Prior studies have shown that mobulas employ a unique mechanism referred to as ricochet separation to filter feed, whereby captive vortices in filter pores cause particles to bounce off the filter surfaces and away from the filter pores. This mechanism enables the filtration of particles smaller than the pore size and reduced clogging. However, few …

Copula-Based Bayesian Model For Detecting Differential Gene Expression, Prasansha Liyanaarachchi, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian …

Characterizing Somatic Variants In Nanopore Data With Machine Learning, Shwethal Sayeeram Trikannad

Master's Projects

Oxford Nanopore Technology (ONT) is a popular long-read sequencer in genomics. However, its high base-calling error rate produces several sequencing artifacts. Detection of somatic variants in ONT sequenced tumor-normal samples remains challenging due to low frequencies. In this study, machine learning was applied to a dataset created by benchmarking ClairS output against HCC1395 and colo829 truth sets to classify variants and artifacts. Relevant features were engineered from sequence context and variant site characteristics to model artifact profiles. HistGradientBoostingClassifier achieved 0.876950 accuracy, outperforming all other models. Variant quality was the top predictor with an aggregate accuracy of over 85%. This work …

Phylogenetic Analysis Of Metabolic Enzymes In Hypoxic/Anoxic Conditions In Cetaceans And Cancer, Samerna A. Masih

Electronic Theses & Dissertations (2024 - present)

Cancer cells often exhibit a Warburg-like metabolism, which includes increased fatty acid synthesis and storage that help them survive in hypoxic conditions. This shift is marked by the heightened synthesis and accumulation of fatty acids, acting as a survival mechanism in low-oxygen environments (Baumann et al., 2016). Interestingly, deep-diving whales may utilize a similar metabolic pathway to produce wax esters during their prolonged dives.

Our study focused on exploring the genetic and metabolic similarities between breast cancer cells and deep-diving whales, using computational analyses to investigate lipid metabolism genes. We looked for evolutionarily conserved variations that might reveal adaptive mechanisms …

Hidden Markov Model For Identifying Local Variants In Human Genomes Using Simulated Data, Scott Mccallum

Electronic Theses and Dissertations

Identifying adaptive mutations in genetic data is challenging due to the low frequency of occurrence of such events, and because signatures of selection are intertwined with the footprints of various other evolutionary forces that shape our genomes. Even when a larger region appears to be under selection, genomic sites that are linked to adaptive mutations have similar statistical signals, and thus can obfuscate the identification of the actual adaptive mutation. The new method described here uses a Hidden Markov Model that allows for classification of neutral, linked, and sweep (adaptive mutation) genomic sites. This model is general and can be …

From Sampling To Simulating: Single-Cell Multiomics In Systems Pathophysiological Modeling, Alexandra Manchel, Michelle M. Gee, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be …

Timigp: A Computational Framework To Determine The Tumor Immune Microenvironment Associated With Prognosis And Immunotherapy Response, Chenyang Li

Dissertations and Theses (Open Access)

Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development …

Exercise Therapy Rescues Skeletal Muscle Dysfunction And Exercise Intolerance In Cardiometabolic Hfpef, Heather Quiriarte, Robert C. Noland, James E. Stampley, Gregory Davis, Zhen Li, Eunhan Cho, Youyoung Kim, Jake Doiron, Guillaume Spielmann, Sujoy Ghosh, Sanjiv J. Shah, Brian A. Irving, David J. Lefer, Timothy D. Allerton

School of Graduate Studies Faculty Publications

Exercise intolerance, a hallmark of heart failure with preserved ejection fraction (HFpEF) exacerbated by obesity, involves unclear mechanisms related to skeletal muscle metabolism. In a “2-hit” model of HFpEF, we investigated the ability of exercise therapy (voluntary wheel running) to reverse skeletal muscle dysfunction and exercise intolerance. Using state-of-the-art metabolic cages and a multiomic approach, we demonstrate exercise can rescue dysfunctional skeletal muscle lipid and branched-chain amino acid oxidation and restore exercise capacity in mice with cardiometabolic HFpEF. These results underscore the importance of skeletal muscle metabolism to improve exercise intolerance in HFpEF.

Investigating The Rare Aneuploid Cells In Normal Breast And Therapeutic Resistance In Triple Negative Breast Cancer Using Single Cell Genomics, Yiyun Lin

Dissertations and Theses (Open Access)

Aneuploid epithelial cells are common in breast cancer, however their presence in normal breast tissues is not well understood. To address this question, we applied single cell DNA sequencing to profile copy number alterations (CNAs) in 83,206 epithelial cells from breast tissues of 49 healthy women and single cell DNA&ATAC co-assays to 19 women. Our data shows that all women harbored rare aneuploid epithelial cells (median 3.19%) that increased with age. Many aneuploid epithelial cells (median 82.22%) in normal breast tissues underwent clonal expansions and harbored CNAs reminiscent of invasive breast cancers (gains of 1q, losses of 10q, 16q and …

Quantifying Effects Of Partial Genetic Backgrounds To Decode Genetic Drivers Of Clinical Phenotypes, Rini Pauly

All Dissertations

Understanding partial genetic backgrounds illuminates the genetic architecture of complex traits and diseases, revealing how diverse genetic backgrounds contribute to phenotypic diversity. With this approach we could advance personalized medicine by identifying population-specific variants affecting drug metabolism, tailoring medical treatments to individual genetic profiles. Additionally, it offers evolutionary insights into human history, shedding light on past migrations and the spread of genetic traits. This research leverages cutting-edge statistical genetics techniques and novel machine learning approaches to efficiently analyze extensive population genomic datasets, distilling complex admixture signals into meaningful genetic markers.

The study introduces Admix-AI, an innovative convolutional neural network-based tool …

Multi-Organ Gene Expression Analysis And Network Modeling Reveal Regulatory Control Cascades During The Development Of Hypertension In Female Spontaneously Hypertensive Rat, Eden Hornung, Sirisha Achanta, Alison Moss, James S. Schwaber, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Hypertension is a multifactorial disease with stage-specific gene expression changes occurring in multiple organs over time. The temporal sequence and the extent of gene regulatory network changes occurring across organs during the development of hypertension remain unresolved. In this study, female spontaneously hypertensive (SHR) and normotensive Wistar Kyoto (WKY) rats were used to analyze expression patterns of 96 genes spanning inflammatory, metabolic, sympathetic, fibrotic, and renin-angiotensin (RAS) pathways in five organs, at five time points from the onset to established hypertension. We analyzed this multi-dimensional dataset containing ~15,000 data points and developed a data-driven dynamic network model that accounts for …

The Impact Of Temperature And Humidity On The Transcriptome Of Symbiochloris Reticulata In Relation To Lobaria Pulmonaria’S Genepools, Ioana Violeta Ardelean, Christoph Scheidegger, Mihai Miclaus

FRONTIERS UNBOUND: Exploring Extreme Environments

No abstract provided.

Noise Leads To The Perceived Increase In Evolutionary Rates Over Short Time Scales, Brian C. O'Meara, Jeremy M. Beaulieu

Biological Sciences Faculty Publications and Presentations

Across a variety of biological datasets, from genomes to conservation to the fossil record, evolutionary rates appear to increase toward the present or over short time scales. This has long been seen as an indication of processes operating differently at different time scales, even potentially as an indicator of a need for new theory connecting macroevolution and microevolution. Here we introduce a set of models that assess the relationship between rate and time and demonstrate that these patterns are statistical artifacts of time-independent errors present across ecological and evolutionary datasets, which produce hyperbolic patterns of rates through time. We show …

The Influence Of Environmental Change On Genetic Diversity Across Spatial And Taxonomic Scales, Connor M. French

Dissertations, Theses, and Capstone Projects

The spatial distribution of genetic diversity is of interest to biodiversity scientists and conservationists and is a fundamental metric of biodiversity. Genetic diversity patterns across spatial and taxonomic scales contain information about population and assemblage dynamics that can convey their resilience to environmental change. Ectotherms are especially linked to their environments and may be especially sensitive to fluctuations in the environment over time. Herein, I investigate global and regional patterns of genetic diversity in two groups of ectotherms, insects and lizards, to understand the relationship between environmental change and genetic diversity, from populations to assemblages. Overall, my research aims to …

Neuromodulatory Co-Expression In Cardiac Vagal Motor Neurons Of The Dorsal Motor Nucleus Of The Vagus, Eden Hornung, Shaina Robbins, Ankita Srivastava, Sirisha Achanta, Jin Chen, Zixi Jack Cheng, James Schwaber, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

Vagal innervation is well known to be crucial to the maintenance of cardiac health, and to protect and recover the heart from injury. Only recently has this role been shown to depend on the activity of the underappreciated dorsal motor nucleus of the vagus (DMV). By combining neural tracing, transcriptomics, and anatomical mapping in male and female Sprague-Dawley rats, we characterize cardiac-specific neuronal phenotypes in the DMV. We find that the DMV cardiac-projecting neurons differentially express pituitary adenylate cyclase-activating polypeptide (PACAP), cocaine- and amphetamine-regulated transcript (CART), and synucleins, as well as evidence that they participate in neuromodulatory co-expression involving catecholamines. …

Advancement In In-Silico Drug Discovery From Virtual Screening Molecular Dockings To De-Novo Drug Design Transformer-Based Generative Ai And Reinforcement Learning, Dony Ang

Computational and Data Sciences (PhD) Dissertations

The field of drug discovery has seen remarkable advancements over the past few decades, transitioning from traditional experimental methods to highly sophisticated computational approaches. One of the pivotal techniques in this evolution is virtual screening, which utilizes molecular docking to predict the interaction between small molecules and target proteins. This method has significantly accelerated the initial stages of drug discovery by enabling the high-throughput screening of large chemical libraries. By simulating the binding affinity and stability of potential drug candidates, virtual screening has become a cornerstone in identifying promising compounds for further development.

A notable application of virtual screening was …

Regulation Of Serpina1 Mrna Expression By Environmental Conditions In Hepatocyte Cells, Fnu Jiamutai

All Theses

The SERPINA1 gene encodes the critical protease inhibitor α-1-antitrypsin (A1AT). A1AT represses neutrophil elastase activity to protect lung tissue from inflammatory damage. A deficiency in α-1-antitrypsin can lead to chronic obstructive pulmonary disease (COPD). Pathogenic genetic variants in SERPINA1 are also associated with A1AT protein misfolding and liver cirrhosis. The regulatory mechanisms of SERPINA1 expression are not well understood, but previous studies suggest that alternative polyadenylation in the 3' untranslated region (3'UTR) affects A1AT protein expression. In this study, we used the liver cancer cell line HepG2 to determine how environmental conditions influence SERPINA1 mRNA expression and post-transcriptional regulation. We …