Computational Biology Commons^™

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 …

18s Metabarcode Analyses Of Eukaryotic Species In The Respiratory Microbiomes Of Wild Canids From New Hampshire, Collin Sinclair Blake

Honors Theses and Capstones

This study is of an exploratory nature and focuses on characterizing the eukaryotic microbiota present in the respiratory tissues of six wild canids and one domestic canine. The contents of this document largely pertain to dry lab analyses of 18S barcodes in bioinformatics programs – primarily QIIME2 running in the GitBash command line, service for which was hosted by the UNH Ron Bioinformatics training server. All procedures listed within the section below were performed by second parties at the UNH Hubbard Center for Genomics Studies (HCGS), the New Hampshire Veterinary Diagnostics Lab (NHVDL), and the UNH Microbial Ecology and Emerging …

Genomic Epidemiology Of Staphylococcus Aureus Sequence-Type 72, Sachitaa Senthilkumar

Honors Undergraduate Theses

Staphylococcus aureus (S. aureus, SA) is a gram-positive bacterial colonizer and pathogen commonly carried on the skin and in the anterior nares of humans. SA colonization may lead to a variety of non-invasive (e.g., skin and soft tissue infections, SSTIs) and invasive (e.g., bacteremia and osteomyelitis) infections. The SA population is comprised of multiple lineages that can be delineated through multi-locus sequence typing (MLST), which compares nucleotide sequences within seven housekeeping genes. Strains belonging to an MLST share a common evolutionary history and phenotypic characteristics like antibiotic resistance or virulence. As a result, the epidemiology of lineages can be …

Enhancing Clinical Trial Matching In Molecular Diagnostics: Using Natural Language Processing And Clustering Approaches In Hematological Malignancies, Gillian Fanning

Theses and Dissertations

Clinical trial matching is a critical component of personalized medicine, particularly in the management of hematologic malignancies. At Virginia Commonwealth University (VCU) Health, the Molecular Diagnostics (MDX) Lab produces somatic variant reports and recommends clinical trials based on the presence of clinically significant mutations. However, the current manual trial recommendation process is time-intensive and lacks scalability.

This study introduces a computational framework to streamline and standardize clinical trial matching using natural language processing (NLP) and unsupervised clustering. Trial brief descriptions were analyzed to extract frequent terms, and trials were grouped based on term similarity using joint dimensionality reduction and clustering. …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

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 …

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 …

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.

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 …

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 …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Motif-Vi Loop Acts As A Nucleotide Valve In The West Nile Virus Ns3 Helicase, Priti Roy, Zachary Walter, Lauren Berish, Holly Ramage, Martin Mccullagh

Department of Microbiology and Immunology Faculty Papers

The Orthoflavivirus NS3 helicase (NS3h) is crucial in virus replication, representing a potential drug target for pathogenesis. NS3h utilizes nucleotide triphosphate (ATP) for hydrolysis energy to translocate on single-stranded nucleic acids, which is an important step in the unwinding of double-stranded nucleic acids. Intermediate states along the ATP hydrolysis cycle and conformational changes between these states, represent important yet difficult-to-identify targets for potential inhibitors. Extensive molecular dynamics simulations of West Nile virus NS3h+ssRNA in the apo, ATP, ADP+Pi and ADP bound states were used to model the conformational ensembles along this cycle. Energetic and structural clustering analyses depict a clear …

Computational Analysis Of O6-Methylated Guanine And Thioguanine Complexes, Kirsten Stinson, Michael Bowman

Lux et Fides: A Journal for Undergraduate Christian Scholars

DNA methylation occurring on the O⁶ position of guanine has been linked to the formation of cancer. DNA complexes with O⁶-methylated guanine have been studied experimentally, yet questions remain concerning the carcinogenic properties of O⁶-methylguanine. This present research explored the interaction between O⁶-methylguanine and its potential nucleobase pairs of cytosine and adenine in hopes of elucidating the mutagenic characteristics of O⁶-methylguanine. A variety of computational methods including Density Functional Theory (DFT), Symmetry Adapted Perturbation Theory (SAPT), Noncovalent Interaction (NCI) analysis, and Natural Bond Orbital (NBO) analysis were employed to comprehensively probe …

Optimizing Immunotherapies For Improved Cancer Treatment, Anne Talkington, Anthony Kearsley

Biology and Medicine Through Mathematics Conference

No abstract provided.

Integrated Transcriptomics And Histopathology Approach Identifies A Subset Of Rejected Donor Livers With Potential Suitability For Transplantation, Ankita Srivastava, Alexandra Manchel, John Waters, Manju Ambelil, Benjamin K. Barnhart, Jan B. Hoek, Ashesh P. Shah, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

BACKGROUND: Liver transplantation is an effective treatment for liver failure. There is a large unmet demand, even as not all donated livers are transplanted. The clinical selection criteria for donor livers based on histopathological evaluation and liver function tests are variable. We integrated transcriptomics and histopathology to characterize donor liver biopsies obtained at the time of organ recovery. We performed RNA sequencing as well as manual and artificial intelligence-based histopathology (10 accepted and 21 rejected for transplantation).

RESULTS: We identified two transcriptomically distinct rejected subsets (termed rejected-1 and rejected-2), where rejected-2 exhibited a near-complete transcriptomic overlap with the accepted livers, …

Mismatch Repair Deficient Neoantigen And Associated Circulating T-Cell Receptor Repertoires In Lynch Syndrome, Ana Bolivar

Dissertations and Theses (Open Access)

Lynch Syndrome (LS) is the most common inherited colorectal cancer (CRC) syndrome. It constitutes the perfect model to understand DNA mismatch repair deficient (MMRd) carcinogenesis, which underlies 15% of early-stage CRC. LS patients develop MMRd tumors with high loads of shared neoantigens (neoAgs), which are recognized by the immune system. Previous research has concentrated on discovering neoAgs and their potential as targets for vaccines in LS patients. However, these studies have primarily identified shared neoAgs from cancers, lacking detailed information on targetable neoAgs present in precancerous lesions. Understanding this landscape of pre-cancer derived neoAgs is crucial for intercepting cancer development …

Uncovering Capillary Endothelial Cells Response During Lung Injury-Repair, Celine Shuet Lin Kong

Dissertations and Theses (Open Access)

Once thought to be a homogenous population, capillary endothelial cells (ECs) have embodied organotypic specialization and heterogenous properties, both during homeostasis and tissue injury. In the lung, capillary ECs consist of two distinct populations, CAP1 and CAP2s; how each population responds to diverse tissue injury is incompletely understood. In this thesis, I report the induction and function of a truncated isoform of Ntrk2, Ntrk2^-tk (lacking the tyrosine kinase domain) in multiple injury models. Using a combinatorial approach of single-cell multiome, mouse genetics and viral infection models, I found that Ntrk2^-tk is broadly induced in CAP1s after the initial …

In Silico Analysis Of C-Type Lectins As Co-Infection Receptors Of Dengue And Chikungunya Viruses In Aedes Aegypti, Munawir Sazali, R. C. Hidayat Soesilohadi, Nastiti Wijayanti, Tri Wibawa, Arif Nur Muhammad Ansori

Makara Journal of Science

Aedes aegypti is a primer vector of dengue virus (DENV) and chikungunya virus (CHIKV). The susceptibility of mosquitoes to DENV and CHIKV depends on their recognition receptor of pathogens. C-type lectins (CTLs) are an important mediator of virus infection in A. aegypti. This study aims to identify potential receptors and determine the binding affinity between ligand–receptor interaction, CTLs and virus envelopes (DENV-1, 2, 3, and 4 and CHIKV) interaction based on in silico analysis. Sample sequences were obtained from GenBank (NCBI), and 10 CTLs were acquired from VectorBase. Homology modeling based on a minimum standard of 20% was processed …

Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang

Computer Science Faculty Publications

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …

Developing Cellular Systems To Elucidate Rna Structural Dynamics Of Cag Expansion Transcripts In Spinocerebellar Ataxias, Victoria Demeo

Electronic Theses & Dissertations (2024 - present)

Spinocerebellar ataxias (SCAs) are a diverse group of over 40 genetically heterogeneous neurodegenerative disorders, many of which are caused by a trinucleotide CAG repeat expansion in the coding region of specific genes. These expansions lead to the production of polyglutamine (polyQ) tracts that interfere with normal protein function, triggering cellular dysfunction and contributing to disease pathogenesis. The most well-known of these SCAs, such as SCA1, SCA2, and SCA3, exhibit progressive neurodegeneration, yet the precise mechanisms through which these mutations cause disease remain poorly understood.

A significant challenge in studying CAG repeat expansion disorders lies in the complexity of the disease …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Machine Learning And Rna Bioinformatics, Jason Rafe Miller

Graduate Theses, Dissertations, and Problem Reports (ETD)

The applied science of bioinformatics encompasses computational analysis of molecular biology data. Advances in genomics and DNA sequencing technology have enabled computational analysis of ribonucleic acids (RNAs), which play diverse and critical roles in most cells. To assist the study of human RNA, we trained machine learning models on RNA nucleotide sequences, devoid of domain knowledge. We built models that distinguish long non-coding lncRNA from protein-coding mRNA, and models that predict the cytoplasmic vs. nuclear preferences of lncRNAs. In a review of published lncRNA subcellular localization classifiers, we show that the commonly used validation protocol generates optimistic performance measures, and …

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Increased Coding Potential Of Bovine Herpesvirus 1, Victoria Jefferson

Theses and Dissertations

Bovine respiratory disease (BRD) costs the cattle industry millions of dollars in costs in treatment and loss every year in the United States. A significant pathogen often contributes to BRD is Bovine Herpesvirus 1 (BoHV-1), a double stranded DNA virus with the ability to establish latency in the trigeminal ganglia and neurons. Primary infection with BoHV-1 results in immunosuppression that increases the risk of secondary bacterial infection and pneumonia. Because herpesviruses infect their hosts for life and can be reactivated in times of stress, BoHV-1 can present a recurring risk of BRD. The following research aims to expand the knowledge …

The Role Of Non-Coding Rnas In Myelodysplastic Neoplasms, Vasileios Georgoulis, Epameinondas Koumpis, Eleftheria Hatzimichael

Computational Medicine Center Faculty Papers

Myelodysplastic syndromes or neoplasms (MDS) are a heterogeneous group of myeloid clonal disorders characterized by peripheral blood cytopenias, blood and marrow cell dysplasia, and increased risk of evolution to acute myeloid leukemia (AML). Non-coding RNAs, especially microRNAs and long non-coding RNAs, serve as regulators of normal and malignant hematopoiesis and have been implicated in carcinogenesis. This review presents a comprehensive summary of the biology and role of non-coding RNAs, including the less studied circRNA, siRNA, piRNA, and snoRNA as potential prognostic and/or predictive biomarkers or therapeutic targets in MDS.

Genome-Scale Methylation Analysis In Blood And Tumor Identifies Immune Profile, Age Acceleration, And Dna Methylation Alterations Associated With Bladder Cancer Outcomes, Ji-Qing Chen

Dartmouth College Ph.D Dissertations

Bladder cancer patients receive frequent screening due to the high tumor recurrence rate (more than 60%). Nowadays, the conventional monitoring method relies on cystoscopy which is highly invasive and increases patient morbidity and burden to the health care system with frequent follow-up. As a result, it is urgent to explore novel markers related to the outcomes of bladder cancer. Immune profiles have been associated with cancer outcomes and may have the potential to be biomarkers for outcomes management. However, little work has been conducted to investigate the associations of immune cell profiles with bladder cancer outcomes. Here, I utilized the …

Exploring The Interactions Between Sars-Cov-2 And Host Proteins, Sojan Shrestha

School of Biological Sciences: Dissertations, Theses, and Student Research

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the current pandemic, Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 is considered to be of zoonotic origin; it originated in non-human animals and was transmitted to humans. Since the early stage of the pandemic, however, the evidence of transmissions from humans to animals (reverse zoonoses) has been found in multiple animal species including mink, white-tailed deer, and pet and zoo animals. Furthermore, secondary zoonotic events of SARS-CoV-2, transmissions from animals to humans, have been also reported. It is suggested that non-human hosts can act as SARS-CoV-2 reservoirs where accumulated …

Atomistic Assessment Of Drug-Phospholipid Interactions Consequent To Cancer Treatment: A Study Of Anthracycline Cardiotoxicity, Yara Elsayed Ahmed

Theses and Dissertations

Despite being one of the most effective chemotherapeutic agents developed to date, Anthracyclines are notorious for their cardiotoxicity. Their clinical use is frequently limited both in dosage and in prescription due to the severe cardiac damage they cause. The mechanism of anthracycline-induced cardiotoxicity is not yet fully understood. However, it is hypothesized that interactions with the myocardial membrane play an important role in imparting cardiotoxicity. In this study, we use molecular dynamics simulations and density functional theory calculations to study the anthracycline drug molecules and the interactions that they have with the myocardial membrane. We construct a myocardial membrane model …