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Eed Gene Variants And Irritable Bowel Syndrome: A Genetic Approach To Understanding Ibs, Elaine Vanterpool, Ted Howard Jan 2025

Eed Gene Variants And Irritable Bowel Syndrome: A Genetic Approach To Understanding Ibs, Elaine Vanterpool, Ted Howard

Student Posters

Irritable Bowel Syndrome is a disease linked with the gastrointestinal system and seen to cause bloating, abdominal pain, and harmful alterations in the digestive system. Its cause is complex and is known to have both environmental and genetic factors. Recent studies have shown that IBS occurs due to genetic variations, which leads to the pathogenesis of IBS. One gene, the EED (Embryonic Ectoderm Development) gene has been involved in various biological processes, particularly gastrointestinal functions. The purpose of this study is to explore the potential relationship between the EED gene and IBS.

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Analyzing The Inhibition Of Chemical Compounds On Collagenase In P. Aeruginosa, Elaine Vanterpool, Marielle Cooper, Kiara Cameron Jan 2025

Analyzing The Inhibition Of Chemical Compounds On Collagenase In P. Aeruginosa, Elaine Vanterpool, Marielle Cooper, Kiara Cameron

Student Posters

Pseudomonas aeruginosa is an opportunistic pathogen that poses significant challenges in clinical treatment due to its production of collagenase. This bacterium can cause several infections including pneumonia, meningitis, septicemia, and a host of other diseases. Collagenase acts as a key virulence factor by breaking down collagen in the host’s extracellular matrix, allowing bacteria to invade tissues. This study hypothesized that the effects of cell secreted collagenase would be inhibited by metronidazole, vanillin, and silver nanoparticles to reduce the pathogenicity of Ps. aeruginosa related infections.

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Analysis Of Cys294arg Mutation In Cs1 Associated With Periodontal Ehlers-Danlos Syndrome & Collagen Degradation, Elaine Vanterpool, Amy Aboki Jan 2025

Analysis Of Cys294arg Mutation In Cs1 Associated With Periodontal Ehlers-Danlos Syndrome & Collagen Degradation, Elaine Vanterpool, Amy Aboki

Student Posters

C1s enzyme plays a crucial role in the immune system so that the body can fight infection and repair damaged tissues. However, if it is not working properly, it will do more harm than good. This is shown in the case of periodontal Ehlers-Danlos syndrome (pEDS), a rare genetic disorder whereby C1s breaks down collagen I, a protein that gives structure and strength to connective tissues. Severe gum disease, loose teeth and premature teeth is a result of this. The purpose of this study is to identify and analyze the pathogenicity of C1s mutations and association in pEDS.

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Impact Of Feeding Different Fat Sources And Levels Of Vitamin E Isoforms To Heavy Slaughter Weight (150 Kg) Pigs: Ii. Belly And Bacon Quality, Marlee Kelley, Ding Wang, Ana Paula A.A. Salim, Merlin D. Lindemann, Gregg Rentfrow, Surendranath P. Suman Jan 2025

Impact Of Feeding Different Fat Sources And Levels Of Vitamin E Isoforms To Heavy Slaughter Weight (150 Kg) Pigs: Ii. Belly And Bacon Quality, Marlee Kelley, Ding Wang, Ana Paula A.A. Salim, Merlin D. Lindemann, Gregg Rentfrow, Surendranath P. Suman

Animal and Food Sciences Faculty Publications

Two independent experiments were performed to evaluate the effect of dietary fat sources and vitamin E supplementation on the characteristics of belly and bacon from heavy slaughter weights (150 kg) pigs. In experiment 1, 64 individually fed pigs (32 barrows and 32 gilts) were randomly assigned to 1 of the 8 dietary treatments in a 4 × 2 factorial arrangement. Four fat sources included cornstarch (CS), tallow (TW), corn oil (CO), and coconut oil (CN). Vitamin E supplementation included alpha-tocopheryl acetate (ATA) at 11 and 200 ppm. In experiment 2, individually fed pigs (n = 72; 36 barrows, 36 gilts) …

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Impact Of A 24 H Feed Withdrawal On Active Nutrient Transport, Intestinal Morphology, And Gene Expression In The Equine Small And Large Intestine, Blaire E. Aldridge-Dean, Timothy B. Lescun, John Scott Radcliffe Jan 2025

Impact Of A 24 H Feed Withdrawal On Active Nutrient Transport, Intestinal Morphology, And Gene Expression In The Equine Small And Large Intestine, Blaire E. Aldridge-Dean, Timothy B. Lescun, John Scott Radcliffe

Animal and Food Sciences Faculty Publications

Horses are often subjected to short-term feed withdrawal (FW) pre- or post-surgery to reduce anesthetic complications. However, removing nutrients from the intestinal lumen may negatively impact intestinal health. Thirteen horses were used to determine the effects of a 24 h FW on gut barrier function, active nutrient transport, transporter gene expression, and intestinal morphology. Following 0 or 24 h FW (0FW or 24FW, respectively), horses were euthanized via overdose of sodium pentobarbital and sodium phenytoin, and segments of proximal jejunum (PJ), mid jejunum (MJ), ileum (Il), and right ventral colon (RVC) were harvested for histology (PJ and Il), gene expression, …

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Piglet Nursing Location Along The Sow’S Udder Line Affects Piglet Weight Gain And Subsequent Weaning Weight, Shannon Dierking, Harold J. Monegue, Merlin Lindemann Jan 2025

Piglet Nursing Location Along The Sow’S Udder Line Affects Piglet Weight Gain And Subsequent Weaning Weight, Shannon Dierking, Harold J. Monegue, Merlin Lindemann

Animal and Food Sciences Faculty Publications

Background: There is a linear correlation between piglet weaning weight and average daily gain during the post-nursery period. A key factor that influences piglet weight gain during lactation is milk intake. Thus, the variation in piglet weaning weight is hypothesized to be, to some extent, a result of differences in milk production among individual mammary glands. Objective: The objective of this study was to evaluate the impact of piglet nursing location throughout lactation on piglet weaning weight, with a secondary objective of determining the impact of piglet birth weight on nursing location selection. Methods: Teat pairs were labeled from anterior …

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Post-Weaning Weight Gain In Pigs Is Not Affected By Moderate Duration Transport At 20 Days Of Age, Isabel B. Walpole, Alyssa A. Smith, Kaylyn G. Rudy, Dayeon Jeon, Sarah M. Innis, Brian T. Richert, J. Scott Radcliffe, J. Alex Pasternak Jan 2025

Post-Weaning Weight Gain In Pigs Is Not Affected By Moderate Duration Transport At 20 Days Of Age, Isabel B. Walpole, Alyssa A. Smith, Kaylyn G. Rudy, Dayeon Jeon, Sarah M. Innis, Brian T. Richert, J. Scott Radcliffe, J. Alex Pasternak

Animal and Food Sciences Faculty Publications

Transportation at weaning is an integral component of the American swine industry. However, the long-term effects on growth performance have not been well characterized. Previous research suggests transportation causes weight loss immediately following weaning, but few studies have followed this effect further than 7 d post-weaning, with transport causing decreased body weight in those that have. In experiment 1, average weight pigs at 20 ± 1.3 d of age were weaned and either 1) transported for 9 hour without feed and water (TR), 2) had their feed and water restricted for 9 hour (FR), or 3) were weaned and provided …

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Impact Of Feeding Different Fat Sources And Levels Of Vitamin E Isoforms To Heavy Slaughter Weight (150 Kg) Pigs: I. Carcass Characteristics And Fresh Pork Quality, Marlee Kelley, Ding Wang, Gregg Rentfrow, Merlin D. Lindemann, Ana Paula A.A. Salim, Surendranath P. Suman Jan 2025

Impact Of Feeding Different Fat Sources And Levels Of Vitamin E Isoforms To Heavy Slaughter Weight (150 Kg) Pigs: I. Carcass Characteristics And Fresh Pork Quality, Marlee Kelley, Ding Wang, Gregg Rentfrow, Merlin D. Lindemann, Ana Paula A.A. Salim, Surendranath P. Suman

Animal and Food Sciences Faculty Publications

The objective of this study was to evaluate the effect of supplementing α-tocopheryl-acetate (ATA) and γ-tocopherol (GT) vitamin E isoforms with corn oil (CO) and tallow (TW) on carcass characteristics and meat quality characteristics of pigs grown to heavy weights (150 kg). Individually fed pigs (n = 72; 36 barrows, 36 gilts) were randomly assigned to 12 dietary treatments in a 2 × 6 factorial arrangement. Fat treatments were 5% TW and 5% CO. The vitamin E treatments included 4 levels of ATA (11, 40, 100, and 200 ppm) and 2 levels of mixed tocopherols (primarily GT; 40 and 100 …

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Wet Aging For 21 Days Improves Tenderness Of Beef Biceps Femoris Muscle Without Affecting Color Stability, Ana Paula A.A. Salim, Mahesh N. Nair, Yifei Wang, Anna C.V.C.S. Canto, Gregg Rentfrow, Surendranath P. Suman Jan 2025

Wet Aging For 21 Days Improves Tenderness Of Beef Biceps Femoris Muscle Without Affecting Color Stability, Ana Paula A.A. Salim, Mahesh N. Nair, Yifei Wang, Anna C.V.C.S. Canto, Gregg Rentfrow, Surendranath P. Suman

Animal and Food Sciences Faculty Publications

Beef whole-muscle cuts containing biceps femoris (BF) exhibit inferior tenderness compared to cuts from the ribs and loins. The BF is a sizeable muscle in beef round, and enhancing tenderness of BF may increase the value of beef carcasses. While wet aging for 21 d improves beef tenderness, it can compromise color. Therefore, we evaluated the effects of wet aging on color and tenderness of beef BF to identify an optimal aging duration for these quality traits. The BF muscles were collected 24 h postmortem from 8 beef carcasses (n = 8), divided into equal sections, vacuum packaged, and aged …

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Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja Jan 2025

Simple Vs. Complex Human Activity Classification Via Hybrid Machine Learning Models, Anusha Kukreja

Master's Projects

In-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., …

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Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem Jan 2025

Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem

Master's Projects

This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets …

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Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse Jan 2025

Faux Capabilities: A Novel Approach For Code Analysis, Tanay Godse

Master's Projects

When using third-party packages or libraries, it is crucial to understand their behavior. Typically, this requires developers to either conduct code reviews or set up sandbox environments for testing or write unit tests with mocked values for every function used in their code. However, these approaches are often inefficient and time-consuming. A more effective solution would provide developers with a broad understanding of the functionality required by the code they plan to import. This can be done using object capabilities, where a particular functionality is the capability that an object must possess, in order to be able to perform the …

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Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra Jan 2025

Traceai: Intelligent Distributed Tracing Using Large Language Models, Mihir Dhirajlal Satra

Master's Projects

Distributed systems are difficult to trace using traditional methods due to the scale of data volume and complexity, and they usually require a lot of manual analysis. TraceAI tries to solve these problems by integrating Large Language Models with the tracing tools to automatically enhance the trace data evaluation. The project aims to provide an AI-driven solution for monitoring and understanding the flow of requests across services, anomaly detection, root cause analysis and performance optimization. It can thus automate finding out systems problems using LLMs thereby carrying out large scale trace data analysis. Anticipated results from the effort will be …

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Proxy Cap - La Lua Protector, Swift Sheng Jan 2025

Proxy Cap - La Lua Protector, Swift Sheng

Master's Projects

Inspired by the object capability model and sandbox, this project, Proxy Cap, introduces a new Lua access control model that improves the language’s security without sacrificing usability. Object capability is an unconventional but powerful security model. The security model closely observes the principle of least authority. Ambient authority, the omnipresent global environment, does not exist in the object capability computation world, and no resource is accessible unless explicitly assigned. Only connectivity begets connectivity. Lua is an extensible and high-performing scripting language based on ANSI C. The language is popular in many fields but faces security challenges. Lua has a non-traditional …

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Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo Jan 2025

Combining Esm Models With Experimentally Derived Structural Stability To Identify Functional Missense Mutations, Rucha Deo

Master's Projects

Missense mutations can impact protein function and structure, yet their effects on protein function are difficult to predict. In this study, I compared two deep learning models, ESM1v and ESM1b, by evaluating their mutation predictions against experimental structural stability data. ESM1v showed a stronger correlation with experimental structural stability scores compared to ESM1b. A sigmoid curve was fitted to explore this relationship further. Over 100,000 mutations were identified where experimental stability differed significantly from model predictions. Many mutations that remained structurally stable experimentally but were predicted as harmful by the ESM models were frequently found at known functional sites. Structural …

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Automated Sponge Segmentation With Mask R-Cnn On Arms Plate Images For Reef Monitoring, Mrunali Abhijit Thokadiwala Jan 2025

Automated Sponge Segmentation With Mask R-Cnn On Arms Plate Images For Reef Monitoring, Mrunali Abhijit Thokadiwala

Master's Projects

Climate change-driven ocean warming and acidification are disrupting the ecological balance of coral reefs. Notably, these oceanic conditions are undermining coral health and accelerating their decline which is favoring some sponge species in outcompeting them for dominance. Although functional, these altered reefs destabilize the reef architecture, hinder nutrient cycling, and support fewer marine species. Thus, monitoring the growth and abundance of various sponges and understanding their roles at different stages of ecological succession in coral reefs is vital. Autonomous reef monitoring structures (ARMS) are often used for this purpose, but manual taxonomic analysis using their images is time-consuming, inconsistent and …

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Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu Jan 2025

Hierarchical Bloom Filter Tree (Hbft): Scalable Geospatial Metadata Indexing For Bigdata Systems, Mrudula Patteparapu

Master's Projects

Big Data infrastructure growth has produced overwhelming metadata volumes which create extensive problems for spatial indexing and both system scalability and database queries. Traditional solutions consisting of R-trees and conventional Bloom filters manage to provide either range query support or approximate membership testing, yet they face performance issues when used at large-scale metadata management. This research proposes Hierarchical Bloom Filter Tree (HBFT) as an improved framework that integrates hierarchical spatial partitioning with partitioned, scalable, cuckoo, and striped Bloom filter variants based on existing studies in hierarchical and probabilistic indexing. The complete evaluation process shows that HBFT outperforms PostGIS (an industry-standard …

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Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla Jan 2025

Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla

Master's Projects

The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is …

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Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar Jan 2025

Transformer Integration, Fine-Tuning And Zero-Shot Learning For State Of Health Estimation In Li-Ion Batteries Using Large Language Models, Chinmay Nilesh Mahagaonkar

Master's Projects

In this thesis, we present a comparative analysis of the use of transformerbased and Large Language Model (LLM) models for State of Health (SoH) and Remaining Useful Life (RUL) prediction of lithium-ion batteries. With electric cars and renewable energy systems based on batteries at the forefront, the need to predict degradation accurately in order to enhance the performance and reduce maintenance costs has become imperative. Most traditional prediction methods lag the complex and non-linear characteristics of degradation in batteries, and hence the usage of sophisticated methods becomes a necessity. The research employs the CALCE dataset, which includes long-horizon cycling data …

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Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula Jan 2025

Physiotrack: A Gamified Physiotherapy System, Pranavi Chaturvedula

Master's Projects

Traditional physiotherapy methods tend to be non-interactive and provide little to no personalized instruction, even though physiotherapy is critical to stroke recovery. This thesis explores a fully adaptive, sensor-based, feedback architecture intended for stroke patients which remotely supervises movement and personalizes exercises enabled by multimodal sensors. The system uses filtering and windowed segmentation of accelerometer and skeletal data to compute features like jerk, speed, and joint movement angular range. A game engine applies accelerometer and skeletal features together with optimized, lightweight ML models to drive adaptive feedback, scoring, and difficulty adjustment. The architecture supports responsive continuous sensor streaming within the …

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Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi Jan 2025

Edurag: Improving Ai Teaching Assistants With Retrieval-Augmented Generation, Geethika Vadlamudi

Master's Projects

This paper is based on the emerging need for AI-driven teaching assistants to deliver personalized, effective, and responsive educational assistance. Earlier proposals for educational technology such as rule-based systems and adaptive learning platforms have been limited by the lack of flexibility, domain accuracy, and slow, non-interactive support. With the launch of large language models (LLMs) like GPT-3, GPT-4, they have demonstrated that they can generate the kind of human-responsive text. When it comes to more substantive education matters, though, such models suffer from domain accuracy, in addition to whether accurate information can be imparted. And that is where the aspect …

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Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary Jan 2025

Galora: A Lightweight Graph-Aware Llm Framework For Node Classification On Text-Attributed Graphs, Mayur Choudhary

Master's Projects

With the exponential rise of language models (LMs) and their potential to understand semantic relationships, large LMs are being used across a wide range of applications. Text-attributed graphs (TAGs) are one notable example where LLMs can be combined with Graph Neural Networks (GNNs) to enhance node classification results. TAGs associate textual content with each node and are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively modeling TAGs would enable deeper insights into different aspects of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework to integrate structural information …

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Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran Jan 2025

Extraction Of A Knowledge Graph Of Biomedical Relationships, Brian Tran

Master's Projects

Rapid release in biomedical literature poses a challenge in linking information. This thesis aims to extract data from expanding datasets to identify and form meaningful relationships between biomedical entities. Large language models (LLMs) enable us to learn at a rapid pace. Creation of LLms from scratch are impractical. This thesis aims to collect a small dataset, containing biomedical papers, and use it to train large language models (LLMs) to extract entities from the text and learn the relationships between these entities. The experiment will be divided into two stages and utilize EU-ADR and ChemProt dataset. Starting with named entity recognition …

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Improving Contextual Retrieval For Long Documents In Q & A Systems, Sakshi Garg Jan 2025

Improving Contextual Retrieval For Long Documents In Q & A Systems, Sakshi Garg

Master's Projects

With the vast amount of information available on the internet distributed across several lengthy documents, finding relevant information has become more important and challenging. The goal of this project is to develop advanced techniques to retrieve information from long texts in order to deliver accurate and relevant results while ensuring speed and efficiency. As part of this work, we employ techniques to address unique difficulties posed by large and complex documents. This paper presents a custom Retrieval-Augmented Generation (RAG) framework designed to improve contextual retrieval in long and multi-document settings. In this paper, we employ several techniques like summarization, semantic …

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Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder Jan 2025

Evorgcn: Harnessing Esm-2 Evolutionary Embeddings With Relational Gcns For High-Fidelity Protein-Protein Interaction Prediction, Mohit Kunder

Master's Projects

Accurately predicting protein-protein interactions (PPIs) is essential for understanding cellular function and advancing biomedical discovery. We model PPIs as graphs, where nodes represent proteins and edges denote interactions. Using interaction data from the STRING database, we use two samples of it, namely the benchmark datasets—SH27K and SH148K—filtered by confidence score and annotated by interaction mode (multiple relations). In this project, we present EvoRGCN, a graph-based machine learning framework for PPI prediction that integrates both sequence-level (ESM-2 embeddings) and network-level information. We incorporate various Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, and Relational Graph Convolutional Networks. Our …

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Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana Jan 2025

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …

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Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul Jan 2025

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul

Master's Projects

Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …

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Gen Ai For Malicious Network Data, Aneesh Maturu Jan 2025

Gen Ai For Malicious Network Data, Aneesh Maturu

Master's Projects

Though botnet attacks are on the rise, they also have become sophisticated and difficult to detect. Such a rising threat demands more and more sophisticated cybersecurity that leverages machine learning technology. Nevertheless, one of the biggest bottlenecks remains the unavailability of large and well-balanced datasets, particularly for malicious traffic, which hampers the efficacy of detection models. In an attempt to address this issue, our research utilizes Generative Adversarial Networks (GANs) to produce synthetic samples of botnet traffic from the CTU-13 dataset. While the majority of generative models have been targeting image data, we use GANs for a new application: generating …

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Enhancing Code Review Automation With Large Language Models Using Qlora Fine-Tuning And Rags, Sumukh Naveen Aradhya Jan 2025

Enhancing Code Review Automation With Large Language Models Using Qlora Fine-Tuning And Rags, Sumukh Naveen Aradhya

Master's Projects

In this technological era where Artificial Intelligence and Machine Learning are revolutionizing various domains, Large Language Models (LLMs) are emerging as a very powerful tool. In the software development lifecycle, it is extremely important to have reliable code reviews to ensure security and maintain code quality. This project aims to survey various existing methodologies to aid creation of efficient code review automation agents and also research on ways to make this process more efficient. Parameter Efficient Fine-Tuning (PEFT) methodologies such as LoRA and QLoRA have been explored with an additional focus on a hybrid model that combines adaptive QLoRA with …

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Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan Jan 2025

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

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