Biology subjects

Niranjan, M.

Publications and source records attributed to Niranjan, M..

6 recordsLinked to original sources

bioRxiv2026

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

Cancer progression and outcomes are driven in part by changes to molecular networks that result from genetic and/or environmental perturbations. These network changes manifest across multiple interconnected network layers and include accumulation of somatic mutations, altered proteinprotein interactions and dysregulated gene-expression. Here we describe a graph autoencoder-based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across multi-omics data. Given samples stratified into two contrasting phenotypic groups and a prior gene interaction network, GAE-{Delta} constructs group-specific gene graphs for each omics modality and trains, for each modality, a single graph autoencoder jointly on both group graphs, so that the two group-conditional embeddings share a common latent space. Contrasting these embeddings defines a multi-omics embedding-shift representation for each gene that reflects how its network role reorganizes across phenotypic contexts. These gene-level shifts are subsequently used for unsupervised gene prioritization, multi-omics late fusion and sample-level classification. Applied to five TCGA cancer types with a survival endpoint, GAE-{Delta} achieves competitive or superior predictive performance compared with classical network-based methods and multi-omics matrix-factorisation methods (MOFA+, iNMF), with statistically significant AUC gains over MOFA+ in three of five cohorts and statistical ties on the remaining two. Beyond predictive performance, the consensus shift genes are significantly enriched for known cancer drivers in three of five cohorts (hypergeometric p < 0.01; 11-17x fold-enrichment), whereas matrix-factorisation baselines reach p < 0.05 in zero of five cohorts (best per-cancer p = 0.06), indicating that GAE-{Delta} captures biological signal that linear factor methods miss. In summary, the GAE-{Delta} approach provides for both improved outcome classification as well as for biological and mechanistic discovery through deep network-based integration of disease-associated multi-omics data.

bioinformatics↗

bioRxiv2025

sampleclusteR: A lightweight R package for automated clustering of transcriptomics samples using metadata

BackgroundAs technologies for genome-wide gene-expression analysis continue to develop, the databases storing the resulting data have grown accordingly. The Gene Expression Omnibus (GEO) has grown to over 250,000 data series across more than 25,000 omics platforms. Likewise, ArrayExpress is comprised of over 70,000 transcriptome and methylome datasets. Conducting meta-analyses of data from these databases can be challenging, typically requiring extensive manual grouping of samples to identify experimental groups for comparison. ResultsHere we present sampleclusteR, a lightweight R package which automates the clustering of gene-expression study samples based on their metadata. To demonstrate the utility of the approach to large scale analysis of GEO data series, 275 GEO data series were analysed using the package. sampleclusteR was able to correctly cluster 4694 of the 5081 samples across the 275 data sets in an unsupervised manner. In addition, 250 datasets from ArrayExpress were analysed by the package with 8547 of the 9154 samples being automatically clustered into correct groups. We show how sampleclusteR can be used to automate analysis of gene-expression datasets by conducting a meta-analysis of multiple GEO data series related to the Wnt signaling pathway. sampleclusteR correctly assigned all samples to the correct experimental groups and identified sets of differentially expressed genes for downstream analysis. ConclusionssampleclusteR enables large-scale analysis of data from GEO or ArrayExpress by automating the clustering of both GEO and ArrayExpress metadata tables using text mining of their associated metadata.

bioinformatics↗

bioRxiv2022

Rank Selection Genetic Algorithm optimises robust parameter estimation for systems biology models

The ability to reliably predict and infer cellular responses to environmental exposures would offer a major advance in the investigation of immune regulation in health and disease. One possible approach is the use of in silico modelling. Design of such a mathematical kinetic model would be based on existing knowledge of a biological system and utilise a partial data set to parameterise. However, the process of parameter estimation, key for the accuracy of the model, is difficult to conduct by hand, and thus a computational alternative is necessary. We report the utility of Genetic Algorithm with Rank Selection (GARS) as a parameter estimation tool on multiple biological models, including heat shock, signal transduction via ERK, circadian rhythm and NF{kappa}B systems, where it showed strong accuracy and superiority to the Extended Kalman Filter method, Algebraic Difference Equations, and MATLAB fminsearch approaches. GARS parameter estimation is a valuable tool for biological data because it reliably infers system behaviour from partial data sets, allowing for the prediction of cellular responses to environmental exposures.

systems biology↗

bioRxiv2021

Predicting Relative Protein Abundance via Sequence-Based Information

Understanding the complex interactions between transcriptome and proteome is essential in uncovering cellular mechanisms both in health and disease contexts. The limited correlations between corresponding transcript and protein abundance suggest that regulatory processes tightly govern information flow surrounding transcription and translation, and beyond. In this study we adopt an approach which expands the feature scope that models the human proteome: we develop machine learning models that incorporate sequence-derived features (SDFs), sometimes in conjunction with corresponding mRNA levels. We develop a large resource of sequence-derived features which cover a significant proportion of the H. sapiens proteome, demonstrate which of these features are significant in prediction on multiple cell lines, and suggest insights into which biological processes can be explained using these features. We reveal that (a) SDFs are significantly better at protein abundance prediction across multiple cell lines both in steady-state and dynamic contexts, (b) that SDFs can cover the domain of translation with relative efficiency but struggle with cell-line specific pathways and (c) provide a resource which can be plugged into many subsequent protein-centric analyses.

bioinformatics↗

bioRxiv2019

Mapping biology from mouse to man using transfer learning

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. Here, we show that transfer learning - the branch of machine learning that concerns passing information from one domain to another - can be used to efficiently map biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.

systems biology↗

bioRxiv2017

Genome-wide post-transcriptional dysregulation by microRNAs in human asthma as revealed by Frac-seq

MicroRNAs are small non-coding RNAs that inhibit gene expression post-transcriptionally, implicated in virtually all biological processes. Although the effect of individual microRNAs is generally studied, the genome-wide role of multiple microRNAs is less investigated. We assessed paired genome-wide expression of microRNAs with total (cytoplasmic) and translational (polyribosome-bound) mRNA levels employing Frac-seq in human primary bronchoepithelium from healthy controls and severe asthmatics. Severe asthma is a chronic inflammatory disease of the airways characterized by poor response to therapy. We found genes (=all isoforms of a gene) and mRNA isoforms differentially expressed in asthma, with novel inflammatory and structural mechanisms disclosed solely by polyribosome-bound mRNAs. Gene expression (=all isoforms of a gene) and mRNA expression analysis revealed different molecular candidates and biological pathways, with differentially expressed polyribosome-bound and total mRNAs also showing little overlap. We reveal a hub of six dysregulated microRNAs accounting for [~]90% of all microRNA targeting, displaying preference for polyribosome-bound mRNAs. Transfection of this hub in healthy cells mimicked asthma characteristics. Our work demonstrates extensive post-transcriptional gene dysregulation in asthma, where microRNAs play a central role, illustrating the feasibility and importance of assessing post-transcriptional gene expression when investigating human disease.

systems biology↗