Bioinformatics Commons^™

Integrated Rules Classifier For Predicting Pathogenic Non-Synonymous Single Nucleotide Variants In Human, Ahmed Barakat Hosseny, Marwa Said Hassan, A A. Shalan, Shymaa Khamis, M I. Dessouky

Basic Science Engineering

The most prevalent kind of genetic variants in humans are non-synonymous single nucleotide variants (nsSNVs). Several prediction tools have been launched to forecast the effect of amino acid substitutes on human protein function. These tools sort variants as pathogenic or neutral. We developed an Integrated Rules Classifier (Integration Score through JRip “ISTJRip”), which integrates the four individual tools that are publicly available; iFish, Mutation Assessor, FATHMM, and SIFT-based on the JRip machine learning technique. Additionally, we compared the ISTJRip approach with the other three created integration classifiers; Integration Score through J48 “ISTJ48”, Integration Score through RF “ISTRF”, and Integration …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Computational Exploration: Docking Analysis Of Compounds From Foeniculum Vulgare As Potential Aromatase Inhibitors For Endometriosis Candidate Therapy, Dwi Anita Suryandari, Puji Sari, Hadi Sunaryo, Khaerunissa Anbar Istiadi

Indonesian Journal of Medical Chemistry and Bioinformatics

Aromatase inhibitors (AI) have controlling symptoms and size of endometriotic implants, making them a promising second-line therapy for endometriosis treatment.pretreatment with letrozole, an AI, combined with leuprolide acetate and resveratrol has been found to improve in vitro fertilization (IVF) outcomes in women mild endometriosis.in this study we screening and analysis of ten phenolic compounds from Foeniculum vulgare using molecular docking with Mcole server.from this results showed that three phenolic trans resveratrol (TR), caempherol coumaril (CC) have low gibbs energy compare with resveratrol (R). The binding modalities of compound TR and compound R were hydrogen-bonding between the hydroxyl and oxygen atom …

Exploring Soil Microbial Dynamics In Southern Appalachian Forests: A Systems Biology Approach To Prescribed Fire Impacts, Saad Abd Ar Rafie

Doctoral Dissertations

Prescribed fires in Southern Appalachian forests are vital in ecosystem management and wildfire risk mitigation. However, understanding the intricate dynamics between these fires, soil microbial communities, and overall ecosystem health remains challenging. This dissertation addresses this knowledge gap by exploring selected aspects of this complex relationship across three interconnected chapters.

The first chapter investigates the immediate effects of prescribed fires on soil microbial communities. It reveals subtle shifts in porewater chemistry and significant increases in microbial species richness. These findings offer valuable insights into the interplay between soil properties and microbial responses during the early stages following a prescribed fire. …

Determining The Effects Of Glycocalyx Modifications On The Electrophysical Properties Of Human Mesenchymal Stem Cells, Rominna E. Valentine Ico

Electronic Theses, Projects, and Dissertations

Human mesenchymal stem cells (hMSCs) have gained popularity in clinical trials due to their multipotent differentiation characteristics, ability to secrete bioactive molecules, migrate into diseased or damaged tissues, and their immunosuppressive properties. HMSC cultures are heterogeneous, containing stem cells, partially differentiated progenitor cells, and fully differentiated cells. One of the major challenges with hMSCs therapeutic potential is the inability to select specific cell subpopulations due to an insufficient number of biomarkers. Often the biomarkers used, like those for fluorescence-activated cell sorting, are not sufficient to define hMSCs because they overlap with other cell types. Consequently, there is a need to …

Motif-Cluster: A Spatial Clustering Package For Repetitive Motif Binding Patterns, Mengyuan Zhou

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Previous efforts in using genome-wide analysis of transcription factor binding sites (TFBSs) have overlooked the importance of ranking potential significant regulatory regions, especially those with repetitive binding within a local region. Identifying these homogenous binding sites is critical because they have the potential to amplify the binding affinity and regulation activity of transcription factors, impacting gene expression and cellular functions. To address this issue, we developed an open-source tool Motif-Cluster that prioritizes and visualizes transcription factor regulatory regions by incorporating the idea of local motif clusters. Motif-Cluster can rank the significant transcription factor regulatory regions without the need for experimental …

In Silico Prediction Of Sodium-Glucose Co-Transporter-2 (Sglt2) Inhibition Activity By Allium Fistulosum Compound Based On Skelspheres Molecular Descriptor, Rudy Heryanto, Aryo Tedjo, Dimas Noor

Indonesian Journal of Medical Chemistry and Bioinformatics

The Sodium-Glucose co-transporter-2 (SGLT2) inhibitor represents a novel agent for the treatment of type 2 diabetes. Drugs of this class function by inhibiting glucose reabsorption in the kidneys, thereby controlling blood glucose levels. It is known that SGLT2 inhibitors activate the AMPK signaling pathway by increasing the expression and activity of AMP-activated protein kinase (AMPK). In vivo tests have demonstrated that ethanolic and aqueous extracts of Welsh onion leaves (Allium fistulosum L) can reduce body weight, liver weight, adipocyte size, and enhance AMP-activated protein kinase (AMPK) expression. In this study, the inhibitory activity (IC50) of compounds within Allium fistulosum against …

Fortifying Iot Against Crimpling Cyber-Attacks: A Systematic Review, Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir

Karbala International Journal of Modern Science

The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to …

Metabolomic Insights Into Tuberculosis: Machine Learning Approaches For Biomarker Identification, Miftahul Khair Akbar, Arief Aulia Rahman

Indonesian Journal of Medical Chemistry and Bioinformatics

The lung parenchyma is largely impacted by the infectious condition known as pulmonary tuberculosis (pulmonary TB) when the immune system creates a wall around the germs in the lungs, a tiny, hard bulge known as a tubercle develops, earning the disease the name tuberculosis. Although the majority of TB germs target the lungs, they can also harm other bodily organs. The identification of TB biomarkers, which are crucial for diagnosis, treatment monitoring, risk analysis, and prognosis, has been the subject of extensive research. Differences in metabolites between normal cells and tuberculosis are considered to be able to support the diagnosis …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Serum Metabolomic Profiling For Colorectal Cancer Using Machine Learning, Ria Nur Puspa Sari, Diah Balqis Ikfi Hidayati, Arleni Bustami

Indonesian Journal of Medical Chemistry and Bioinformatics

Background: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aims to use serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered to be able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Assesment Of Structure, Function, And Microevolutionary Dynamics Of Extrachromosomal Circular Dna In Chinese Hamster Ovary Cells, Dylan Chitwood

All Dissertations

Chinese hamster ovary (CHO) cell lines are among the most popular expression hosts used in biopharmaceutical manufacturing due to relative ease of culture, capacity to perform human-like post-translational modifications, and non-susceptibility to viruses. However, the intrinsic plasticity of the CHO genome can lead to undesired genetic rearrangements, phenotypic shifts, reduced product quality, and early culture termination that prevents continuous biomanufacturing. A characteristic of plastic and unstable genomes that is poorly understood in CHO cells is extrachromosomal circular DNA (eccDNA). EccDNAs are focal amplifications of the genome that reside in the extranuclear space. These plasmid-like entities are structurally complex and are …

Deep Learning Methods For Prediction Of And Escape From Protein Recognition, Bowen Dai

Dartmouth College Ph.D Dissertations

Protein interactions drive diverse processes essential to living organisms, and thus numerous biomedical applications center on understanding, predicting, and designing how proteins recognize their partners. While unfortunately the number of interactions of interest still vastly exceeds the capabilities of experimental determination methods, computational methods promise to fill the gap. My thesis pursues the development and application of computational methods for several protein interaction prediction and design tasks. First, to improve protein-glycan interaction specificity prediction, I developed GlyBERT, which learns biologically relevant glycan representations encapsulating the components most important for glycan recognition within their structures. GlyBERT encodes glycans with a branched …

Editorial: Special Issue “Protein Modeling And Simulation: Selected Articles From The Computational Structural Bioinformatics Workshop 2021”, Negin Forouzesh, Kamal Ai Nasr

Computer Science Faculty Research

No abstract provided.

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh

SDSU Data Science Symposium

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …

Therapeutic Options For Covid-19: Drug Repurposing Of Serine Protease Inhibitor Against Tmprss2, Mohammad Wildan Abiyyi, Surya Dwira, Arleni Bustami, Linda Erlina

Indonesian Journal of Medical Chemistry and Bioinformatics

The SARS-Coronavirus 2 (SARS-CoV-2) outbreak is a serious global public health threat. Researchers around the world are conducting mass research to control this epidemic, starting from the discovery of vaccines, to new drugs that have specific activities as antivirals. Drug repurposing is a potential method of using drugs with known activity for reuse as COVID-19 therapy. This method has the advantage that it can reduce costs and also the duration in the development of potential drugs. The initial step in drug repurposing can be done computationally to determine the effectiveness and specificity of the drug on the target protein. Molecular …

Virtual Screening On Indonesian Herbal Compounds As Sars-Cov-2 Spike (S2) Glycoprotein Inhibitors: Pharmacophore Modelling & Molecular Docking Approaches, Syailendra Karuna Sugito, Artha Uli Cristina, Putri Saskia Harimurti, Gabriella Regita Cendani, Fauzi Azhar Insani, Linda Erlina, Rafika Indah Paramita, Fadilah Fadilah

Indonesian Journal of Medical Chemistry and Bioinformatics

Background: There are still no specific treatments for coronavirus disease (COVID-19) until present days. Several studies have been conducted to determine whether herbal medicine can be an option to be used as a definitive medicine for COVID-19. S2 subunit of spike protein which is responsible for SARS-CoV-2 entry to the host cell, is a potential drug target to inhibit the viral infection. In this study, we aimed to find some compounds from the HerbalDB database that have potential as SARS-CoV-2 spike (S2) glycoprotein inhibitor.

Methods: The 6LXT protein was used as the target protein. The procedure in this study consisted …

Development And Validation Of A Three-Dimensional Optical Imaging System For Chest Wall Deformity Measurement, Nahom Kidane

Electrical & Computer Engineering Theses & Dissertations

Congenital chest wall deformities (CWD) are malformations of the thoracic cage that become more pronounced during early adolescence. Pectus excavatum (PE) is the most common CWD, characterized by an inward depression of the sternum and adjacent costal cartilage. A cross-sectional computed tomography (CT) image is mainly used to calculate the chest thoracic indices. Physicians use the indices to quantify PE deformity, prescribe surgical or non-surgical therapies, and evaluate treatment outcomes. However, the use of CT is increasingly causing physicians to be concerned about the radiation doses administered to young patients. Furthermore, radiographic indices are an unsafe and expensive method of …

Analysis And Visualization Of Molecular Docking 2hi4 Protein, Innas Widiasti

Indonesian Journal of Medical Chemistry and Bioinformatics

The crystal structure of the human microsomal complex P450 1A2 with alpha-naphthoflavone, a cytochrome P450 (CYP) enzyme is particularly important, as it is abundant in the human liver and alters a more diverse xenobiotic array than any other group of metabolic enzymes. CYP1A2 is abundantly found in the liver and involved in the metabolism of about 10% of clinically used drugs metabolized by CYP enzymes. The current drug discovery and development mostly uses high-throughput screening (HTS). However, this regular method is time-consuming and costly. To address the issue, an advanced drug discovery and development method namely chemical compound database screening …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

The Effects Of Host-Like Environmental Signals And Gene Expression On Capsule Growth In Cryptococcus Neoformans, Yu Min Jung

McKelvey School of Engineering Theses & Dissertations

Cryptococcus neoformans is a fungal pathogen that causes cryptococcosis, a disease that kills almost 200,000 people worldwide each year. A unique feature of this deadly yeast is its polysaccharide capsule, which is known to be associated with its virulence. Here, we systematically explore the effects of all possible combinations of 4 capsule-inducing signals on gene expression, cell size, and capsule size. These signals are medium (YPD, DMEM or RPMI), temperature (30°C or 37°C), CO₂ (room air or 5%), cAMP (0 mM or 20 mM), and pH buffer (HEPES/no HEPES). We explore the effects of exogenous cAMP at a range …

Specialized Named Entity Recognition For Breast Cancer Subtyping, Griffith Scheyer Hawblitzel

Master's Theses

The amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There …

Effects Of Continuous In Situ Low-Dose Ionizing Radiation On Microorganisms, Molly E. Wintenberg

All Dissertations

Precise detection and monitoring of nuclear fuel cycle, enrichment, and weapon development activities are critical for supporting warfighter preparation in chemical, biological, radiological, nuclear, and explosives (CBRNE) operations, clandestine activities, and nuclear compliance. A biological sensing system could serve as an alternative to traditional detection methods by using organic material naturally present in the environment to discreetly detect residual trace nuclear material. Microorganisms provide an optimal platform for an alternative sensing system; however, their response to low levels of ionizing radiation is poorly characterized. Combining the power of next-generation sequencing and transcriptomic analysis, this dissertation takes an approach to obtain …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

The Low Abundance Of Cpg In The Sars-Cov-2 Genome Is Not An Evolutionarily Signature Of Zap, Ali Afrasiabi, Hamid Alinejad-Rokny, Azad Khosh, Mostafa Rahnama, Nigel Lovell, Zhenming Xu, Diako Ebrahimi

Plant Pathology Faculty Publications

The zinc finger antiviral protein (ZAP) is known to restrict viral replication by binding to the CpG rich regions of viral RNA, and subsequently inducing viral RNA degradation. This enzyme has recently been shown to be capable of restricting SARS-CoV-2. These data have led to the hypothesis that the low abundance of CpG in the SARS-CoV-2 genome is due to an evolutionary pressure exerted by the host ZAP. To investigate this hypothesis, we performed a detailed analysis of many coronavirus sequences and ZAP RNA binding preference data. Our analyses showed neither evidence for an evolutionary pressure acting specifically on CpG …

The Relationship Between First Case On-Time Starts, Turnover Times, And Operating Room Productivity, Rhafia Bucoy

Walden Dissertations and Doctoral Studies

Operating room (OR) managers struggle to manage day-to-day surgical operations while meeting and exceeding organizational productivity amid the COVID-19 pandemic. The deferral of surgeries contributes to millions of backlogs of surgical case volume and unintended negative consequences. Grounded in the proposition that first-case on-time starts (FCOTS) and turnover time (TOT) are correlates of OR productivity, the purpose of this quantitative correlational study was to examine the relationship between FCOTS, TOT, and OR productivity. Archival data from 136 electronic surgical records were collected from two free-standing ambulatory surgery centers and analyzed using multiple regression. The results of the one-service, eye-specialized, ambulatory …

Framework For The Evaluation Of Perturbations In The Systems Biology Landscape And Inter-Sample Similarity From Transcriptomic Datasets — A Digital Twin Perspective, Mariah Marie Hoffman

Dissertations and Theses

One approach to interrogating the complexities of human systems in their well-regulated and dysregulated states is through the use of digital twins. Digital twins are virtual representations of physical systems that are descriptive of an individual's state of health, an object fundamentally related to precision medicine. A key element for building a functional digital twin type for a disease or predicting the therapeutic efficacy of a potential treatment is harmonized, machine-parsable domain knowledge. Hypothesis-driven investigations are the gold standard for representing subsystems, but their results encompass a limited knowledge of the full biosystem. Multi-omics data is one rich source of …

A Novel Computational Network Methodology For Discovery Of Biomarkers And Therapeutic Targets, Qing Ye

Graduate Theses, Dissertations, and Problem Reports

Lung cancer has the second highest cancer incidence rate and the top cancer-related mortality worldwide. An estimate from the American Cancer Society shows that, in 2022, there will be about 236,740 lung cancer cases (117,910 men and 118,830 women) in the US. To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. There is an unmet clinical need to identify patients with early-stage NSCLC who are likely to develop recurrence and to predict their therapeutic responses. This dissertation developed a novel computational methodology for modeling molecular gene association networks …