Biology subjects

Palaniappan, S. K.

Publications and source records attributed to Palaniappan, S. K..

6 recordsLinked to original sources

bioRxiv2025

Clustering Gene Co-expression Using 2D Contour Analysis and Autoencoder-based Embedding

Identifying co-expressed genes is crucial for understanding biological processes; however, common methods such as Pearson and Spearman correlation rely on assumptions of linearity and monotonicity, respectively, that may not hold for complex biological data. To address these limitations, we propose a framework that captures visual features of gene expression profiles without relying solely on correlation-based methods. Our approach involves converting 3D gene expression data, which contain rich information on gene envelopes over time, into 2D contours that retain important visual information. We then train an autoencoder on the 2D contour images and generate embeddings from them, followed by clustering on the generated embeddings. We also introduce a new clustering algorithm, TAHC, based on hierarchical clustering, which performs better than existing methods in higher dimensions while using cosine similarity. We apply this framework to the Percellome database, which contains gene expression data with two variables--time and dosage--across various experimental conditions (e.g., tissue type). The resulting clusters exhibit good visual coherence, with an overall average Pearson correlation coefficient of 0.81, demonstrating the effectiveness of our approach.

pharmacology and toxicology↗

bioRxiv2024

IntegrIBS: Towards Building a Robust IBS Classifier with Integrated Microbiome Data

Irritable Bowel Syndrome (IBS) is a condition that is quite complicated and shares its symptoms with other related diseases, making it difficult to diagnose. In this study, we initially trained machine learning models on individual microbiome datasets and tested their performance on other datasets, observing variability and low precision among them. To mitigate this, we hypothesised that integrating multiple publicly available microbiome datasets will capture a wide spectrum of microbiome variations across different geographies and demographics. Utilizing this integrated dataset, the XGBoost model achieved a mean accuracy of 0.75 with a standard deviation of 0.04 in 10-fold cross-validation, demonstrating its potential for robust IBS prediction. Explainability analysis identified key bacterial taxa influencing predictions, aligning with existing literature. However, the models performance declined significantly when using a leave-one-dataset-out approach, where the model was trained on all but one dataset and tested on the excluded dataset. The results highlight the challenges of generalizing across diverse datasets due to biological and technical variability. These findings present a cautionary tale regarding the integration of datasets and interpretation of results, emphasizing the need for more comprehensive approaches to develop reliable diagnostic tools for IBS.

bioinformatics↗

bioRxiv2024

Large Language Models for Pathway Curation: A Preliminary Investigation

The pathway curation task involves analyzing scientific literature to identify and represent cellular processes as pathways. This process, often time-consuming and labor-intensive, requires significant curation efforts amidst the rapidly growing biomedical literature. Natural Language Processing (NLP) offers a promising method to automatically extract these interactions from scientific texts. Despite immense progress, there remains room for improvement in these systems. The emergence of Large Language Models (LLMs) provides a promising solution for this challenge. Our study conducts a preliminary investigation into leveraging LLMs for the pathway curation task. This paper first presents a review of the current state-of-the-art algorithms for the pathway curation task. Our objective is to check the feasibility and formulate strategies of using these LLMs to improve the accuracy of pathway curation task. Our experiments demonstrate that our GPT-3.5 based fine-tuned models outperforms existing state-of-the-art methods. Specifically, our model achieved a 10 basis point improvement in over-all recall and F1 score compared to the best existing algorithms. These findings highlight the potential of LLMs in pathway curation tasks, warranting further research and substantial efforts in this direction. Keypoints/ObjectivesO_LIStudy evaluates the feasibility of using Large Language Models (LLMs) for pathway curation in scientific literature. C_LIO_LIUsing GPT-3.5 based fine tuned models for pathway curation, we compare its performance with existing methods, focusing on precision, recall and F1 score metrics. C_LIO_LIEmphasize the promise and need for further research on using LLMs for pathway curation. C_LI

systems biology↗

bioRxiv2024

Replicating a High-Impact Scientific Publication Using Systems of Large Language Models

Publications focused on scientific discoveries derived from analyzing large biological datasets typically follow the cycle of hypothesis generation, experimentation, and data interpretation. The reproduction of findings from such papers is crucial for confirming the validity of the scientific, statistical, and computational methods employed in the study, and it also facilitates the foundation for new research. By employing a multi-agent system composed of Large Language Models (LLMs), including both text and code generation agents built on OpenAIs platform, our study attempts to reproduce the methodology and findings of a high-impact publication that investigated the expression of viral-entry-associated genes using single-cell RNA sequencing (scRNA-seq). The LLM system was critically evaluated against the analysis results from the original study, highlighting the systems ability to perform simple statistical analysis tasks and literature reviews to establish the purpose of the analyses. However, we also identified significant challenges in the system, such as nondeterminism in code generation, difficulties in data procurement, and the limitations presented by context length and bias from the models inherent training data. By addressing these challenges and expanding on the systems capabilities, we intend to contribute to the goal of automating scientific research for efficiency, reproducibility, and transparency, and to drive the discussion on the role of AI in scientific discovery.

bioinformatics↗

bioRxiv2024

MelAnalyze: Fact-Checking Melatonin claims using Large Language Models and Natural Language Inference

With the explosion of health related information in mainstream discourse, distinguishing accurate health-related claims from misinformation is important. Using computational tools and algorithms to help is key. Our focus in this paper is on the hormone Melatonin which is claimed to have broad health benefits and largely sold as a supplement. This paper introduces MelAnalyze, a framework for using generative and transformer-based deep learning models adapted as a natural language inference (NLI) task, to semi-automate the fact-checking of general melatonin claims. MelAnalyze is built upon a comprehensive collection of melatonin-related scientific abstracts from PubMed for validation. The framework incorporates components for precise extraction of information from scientific literature, semantic similarity and NLI. At its core, MelAnalyze leverages pre trained NLI models that are fine-tuned on melatonin-specific claims along with semantic search based on vectorized representation of the articles. The best models, fine-tuned on LLaMA1 and RoBERTa, attain good precision, recall, and F1-scores of approximately 0.92. We also introduce a user-friendly web-based tool for fact-checking algorithm evaluation and use. In summary, we show MelAnalyzes role in empowering users and researchers to assess melatonin-related claims using evidence-based decision-making.

bioinformatics↗

bioRxiv2019

Systemic Lymphoid Architecture Response Assessment (SYLARAS): An approach to multi-organ, discovery-based immunophenotyping implicates a role for CD45R/B220+ CD8T cells in glioblastoma immunology

Accurately profiling systemic immune responses to cancer initiation and progression is necessary for understanding tumor surveillance and, ultimately, improving therapy. Here, we describe the SYLARAS software tool (SYstemic Lymphoid Architecture Response ASsessment) and a data set collected with SYLARAS that describes the frequencies of immune cells in primary and secondary lymphoid organs and in the tumor microenvironment of mice engrafted with a standard syngeneic glioblastoma (GBM) model. The data resource involves profiles of 5 lymphoid tissues in 48 mice and shows that GBM causes wide-spread changes in the local and systemic immune architecture. We perform in-depth analysis of one significant tumor-induced change: depletion of a specialized subset of CD45R/B220+ CD8+ T cells from the circulation and their accumulation in the tumor mass. Immunoprofiling of tissue microarrays demonstrates the presence of similar cells in human GBM.

immunology↗