Biology subjects

Dip, S. A.

Publications and source records attributed to Dip, S. A..

6 recordsLinked to original sources

bioRxiv2026

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

Predicting transcriptional responses to genetic perturbations is a central challenge in functional genomics. CRISPR Perturb-seq experiments measure gene expression changes induced by targeted perturbations, yet experimentally testing all possible perturbations remains infeasible. Computational models that infer responses for unseen perturbations are therefore essential for scalable functional discovery. We introduce PerturbGraph, a biologically informed graph-learning framework for predicting transcriptional responses of unseen gene perturbations by integrating interaction networks, functional annotations, and transcriptional features. Our approach is motivated by the observation that perturbation effects propagate through molecular interaction networks and manifest as coordinated transcriptional programs. Starting from single-cell CRISPR perturbation data, we construct perturbation signatures representing expression shifts relative to control cells and project them into a compact latent program space that captures stable transcriptional variation while reducing noise. Each gene is represented using enriched biological features integrating protein-protein interaction network embeddings, network topology statistics, baseline transcriptional characteristics, and Gene Ontology annotations. A graph neural network propagates information across the interaction network to infer perturbation programs for genes whose effects are not observed during training. Across unseen-perturbation benchmarks, PerturbGraph consistently outperforms classical machine learning models, perturbation-specific deep learning approaches such as scGen and CPA, and alternative graph neural architectures. The model achieves up to 6% improvement in cosine similarity over strong tree-based baselines and more than 20% improvement over linear models while improving recovery of differentially expressed genes. These results show that integrating biological interaction networks with graph representation learning enables accurate prediction of transcriptional effects for previously unobserved genetic perturbations. Code is publicly available at https://github.com/Sajib-006/PerturbGraph.

bioinformatics↗

bioRxiv2025

NicheAgent: LLM-Guided Zero-Shot Niche Identification for Spatial Transcriptomics

Spatial transcriptomics provides high-resolution maps of gene expression within intact tissue architecture, enabling the study of cellular niches, functional layers, and microenvironmental structure. Yet, accurately assigning niche or layer identities remains challenging across platforms such as 10x Visium, MERFISH, and STARmap due to batch variability, incomplete marker panels, and the lack of universally consistent domain boundaries. Existing methods including SpaGCN, BayesSpace, STAGATE, and DeepST rely heavily on supervised labels, dataset-specific fine-tuning, or deep representation learning, which often over-smooth boundaries, fail to generalize across technologies, and provide limited interpretability. We introduce NicheAgent, a zero-shot, training-free framework for spatial niche identification guided by lightweight large language models (LLMs). NicheAgent constructs interpretable nichecards for each region, encoding canonical marker genes and prototype expression centroids. Each cell is first assigned using a deterministic nearest-prototype rule based solely on gene expression and 2-hop spatial neighborhoods. Low-confidence assignments are then selectively reviewed and corrected by an LLM using only interpretable signals: marker-gene coherence, neighborhood label consistency, and an allowed label set. A final spatial smoothing step enforces local structural coherence. Applied across Visium, MERFISH, and STARmap tissues without any retraining or domain-specific supervision, NicheAgent achieves robust and biologically meaningful niche delineation, outperforming many supervised and graph-based baselines on homogeneity, completeness, and mutual information, while offering transparent reasoning traces for each corrected decision. Our results demonstrate that LLM-guided refinement, when coupled with lightweight rule-based prototypes, provides a scalable, explainable, and cross-platform alternative to heavy deep learning models for spatial transcriptomics annotation.

bioinformatics↗

bioRxiv2025

Can Lightweight LLM Agents Improve Spatial TranscriptomicsAnnotation?

Spatial transcriptomics (ST) links gene expression to tissue organization, yet automated annotation of spatial regions remains a persistent challenge. Recent studies have explored large language models (LLMs) for biological reasoning, but their applicability in low-compute, freetier settings is largely unexplored. We investigate whether lightweight LLM agents can improve ST annotation by integrating rule-based heuristics, prototype discovery, and multirole reasoning (Analyst-Consensus-Reviewer) within a unified agentic framework. Across six STARmap and MERFISH datasets, we benchmark single- and multi-agent variants using standard clustering and spatial coherence metrics (NMI, ARI, CHAOS, ASW). Our results show that small open-weight models such as llama3.2 and qwen3 match or slightly exceed deterministic baselines in cluster recovery, while producing more spatially consistent and interpretable predictions. These findings high-light the potential of modular LLM agents as resource-efficient components in future spatial omics annotation pipelines.

genomics↗

bioRxiv2024

UnCOT-AD: Unpaired Cross-Omics Translation Enables Multi-Omics Integration for Alzheimer's Disease Prediction

Alzheimers Disease (AD) is a progressive neurodegenerative disorder, posing a growing public health challenge. Traditional machine learning models for AD prediction have relied on single omics data or phenotypic assessments, limiting their ability to capture the diseases molecular complexity and resulting in poor performance. Recent advances in high-throughput multi-omics have provided deeper biological insights. However, due to the scarcity of paired omics datasets, existing multi-omics AD prediction models rely on unpaired omics data, where different omics profiles are combined without being derived from the same biological sample, leading to biologically less meaningful pairings and causing less accurate predictions. To address these issues, we propose UnCOT-AD, a novel deep learning framework for Unpaired Cross-Omics Translation enabling effective multi-omics integration for AD prediction. Our method introduces the first-ever cross-omics translation model trained on unpaired omics datasets, using two coupled Variational Autoencoders and a novel cycle consistency mechanism to ensure accurate bidirectional translation between omics types. We integrate adversarial training to ensure that the generated omics profiles are biologically realistic. Moreover, we employ contrastive learning to capture the disease specific patterns in latent space to make the cross-omics translation more accurate and biologically relevant. We rigorously validate UnCOT-AD on both cross-omics translation and AD prediction tasks. Results show that UnCOT-AD empowers multi-omics based AD prediction by combining real omics profiles with corresponding omics profiles generated by our cross-omics translation module and achieves state-of-the-art performance in accuracy and robustness. Source code is available at https://github.com/abrarrahmanabir/UnCOT-AD

bioinformatics↗

bioRxiv2024

DeepAge: Harnessing Deep Neural Network for Epigenetic Age Estimation FromDNA Methylation Data of human blood samples

Accurate prediction of biological age from DNA methylation data is a critical endeavor in understanding the molecular mechanisms of aging and developing age-related disease interventions. Traditional epigenetic clocks rely on linear regression or basic machine learning models, which often fail to capture the complex, non-linear interactions within methylation data. This study introduces DeepAge, a novel deep learning framework utilizing Temporal Convolutional Networks (TCNs) to enhance the prediction of biological age from DNA methylation profiles using selected CpGs by a Dual-Correlation based apparoach. DeepAge leverages a sequence-based approach with dilated convolutions to effectively capture long-range dependencies between CpG sites, addressing the limitations of prior models by incorporating advanced network architectures including residual connections and dropout regularization. The dual correlation feature selection enhances our models predictive capabilities by identifying the most age-relevant CpG sites. Our model outperforms existing epigenetic clocks across multiple datasets, offering significant improvements in accuracy and providing deeper insights into the epigenetic determinants of aging. The proposed method not only sets a new standard in age estimation but also highlights the potential of deep learning in biologically relevant feature extraction and interpretation, contributing to the broader field of computational biology and precision medicine.

bioinformatics↗

bioRxiv2024

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public health. Traditional alignment-based methods, though widely used, are computationally intense and reliant on extensive reference databases, often failing to detect novel pathogens due to their low sensitivity and specificity. Similarly, conventional machine learning techniques, while promising, require large annotated datasets and extensive feature engineering and are prone to overfitting. Addressing these challenges, we introduce PathoLM, a cutting-edge pathogen language model optimized for the identification of pathogenicity in bacterial and viral sequences. Leveraging the strengths of pre-trained DNA models such as the Nucleotide Transformer, PathoLM requires minimal data for fine-tuning, thereby enhancing pathogen detection capabilities. It effectively captures a broader genomic context, significantly improving the identification of novel and divergent pathogens. We developed a comprehensive data set comprising approximately 30 species of viruses and bacteria, including ESKAPEE pathogens, seven notably virulent bacterial strains resistant to antibiotics. Additionally, we curated a species classification dataset centered specifically on the ESKAPEE group. In comparative assessments, PathoLM dramatically outperforms existing models like DciPatho, demonstrating robust zero-shot and few-shot capabilities. Furthermore, we expanded PathoLM-Sp for ESKAPEE species classification, where it showed superior performance compared to other advanced deep learning methods, despite the complexities of the task.

bioinformatics↗