Biology subjects

Baranwal, M.

Publications and source records attributed to Baranwal, M..

6 recordsLinked to original sources

bioRxiv2026

Age Stratified Meta Transcriptomic Analysis Reveals Early and Core Dysregulated Pathways in Parkinson's Disease

Parkinsons disease (PD) is strongly age-associated, yet how aging reshapes PD-related transcriptional changes remains unclear. We performed an age-stratified meta-analysis of 16 bulk RNA-seq datasets (646 samples: 314 PD, 332 controls) to distinguish early-onset (<60 years) from late-onset ([≥]60 years) signatures. In the full dataset, 131 significantly (padj<0.05, |log2FC|>1) differentially expressed genes (DEGs) were observed, spanning neuronal activity-dependent genes (NPAS4, PVALB, ARC, FOSB) and immune/stress-related transcripts (IL3RA, SLC25A6, HSPA1A/B). Age-specific analyses revealed 31 DEGs in <60, dominated by large-effect changes in uncharacterized lncRNA LINC02188, pseudogene loci (MUC20P1, RPS28P7), calcium-modulating gene CALML6, lipid{square}associated gene TLCD3B, and cytoskeletal regulators (TIAM2, KCTD8). In contrast, the [≥]60 group showed 181 DEGs enriched for neuronal markers (NPAS4, PVALB) and immune-metabolic genes (FGA, NPC1L1, UPK1A, HSPA1A/B). GO/KEGG analyses indicated that the <60 signature centers on actin remodeling, filopodia, axonogenesis, and Rap1-mediated adhesion/signaling, consistent with early neurite and structural reorganization. The [≥]60 signature was enriched for blood microparticles, chemokine activity, infection-related pathways, ER protein processing, and arachidonic/ether lipid and cytokine signaling, pointing to broad immune-metabolic and proteostasis dysregulation. Cross-age comparison showed that classical PD neuronal immediate-early gene changes are largely [≥]60-driven, whereas early-onset PD involves novel lncRNA-calcium-lipid and cytoskeletal modules. These findings highlight LINC02188, TLCD3B and related cytoskeletal/lncRNA genes as novel early-onset PD-associated candidates, and NPC1L1, IL3RA and PVALB as age-amplified markers within the broader PD transcriptomic signature.

bioinformatics↗

bioRxiv2025

STING: A Graph Neural Network Approach for Computational Inference of Spatial Transcriptomic Profiles

Spatial transcriptomics enables the measurement of mRNA counts at spatial locations within a tissue but faces challenges such as high experimental costs, technical expertise requirements, and low RNA detection efficiency at high resolution. We present STING (Spatial Transcriptomics Inference using Graph neural networks), a computational approach that infers spatial gene expression patterns with high efficiency. STING integrates convolutional neural networks (CNNs) pre-trained on histological images with graph neural networks (GNNs) to model spatial proximity, representing tissue sections as nearest-neighbor graphs where spatially adjacent spots are interconnected. We train GNNs on these graphs to predict gene expression at additional tissue locations, including unseen samples. Evaluated on two public spatial transcriptomics datasets (59 and 36 tissues), STING achieves a Pearson correlation coefficient (PCC) of up to 0.79 for super-resolution and 0.69 for tissue-wide inference, outperforming existing methods in accuracy. Our results demonstrate that STING is an effective and computationally efficient tool for predicting spatial gene expression, with significant applications in cancer research, personalized medicine, and beyond.

bioinformatics↗

bioRxiv2023

Neural differential equations enable early-stage prediction of preterm birth using vaginal microbiota

AO_SCPLOWBSTRACTC_SCPLOWPreterm births (PTBs), i.e., births before 37 weeks of gestation are completed, are one of the leading issues concerning infant health, and is a problem that plagues all parts of the world. Millions of infants are born preterm globally each year, resulting in developmental disorders in infants and increase in neonatal mortality. Although there are known risk factors for PTB, the current procedures used to assess PTB risk are effective only at the later stages of pregnancy, which reduces the impact of currently possible interventions administered to prevent PTB or mitigate its ill-effects. Vaginal microbial communities have recently garnered attention in the context of PTB, with the notion that a highly diverse microbiome is detrimental as far as PTB is concerned. Increased abundance or scarcity of certain microbial species belonging to specific genera has also been linked to PTB risk. Consequently, attempts have been made towards establishing a correlation between alpha-diversity indices associated with vaginal microbial communities, and PTB. However, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically and geographically diverse populations, which diversity indices may not be able to overcome. Machine learning (ML)-based approaches have also previously been explored, however, the success of these approaches reported thus far has been limited. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. Thus, alternative approaches are necessary to identify signatures in these microbial communities that are capable of distinguishing PTB from a full-term pregnancy. In this study, we have highlighted the limitations of diversity indices for prediction of PTB in racially diverse cohorts. We applied Deep Learning (DL)-based methods to vaginal microbial abundance profiles obtained at various stages of pregnancy, and Neural Controlled Differential Equations (CDEs) are able to identify a signature in the temporally-evolving vaginal microbiome during trimester 2 and can predict incidences of PTB (mean test set ROC-AUC = 0.81, accuracy = 75%, F1-score = 0.71) significantly better than traditional ML classifiers such as Random Forests (mean test set ROC-AUC = 0.65, accuracy = 66%, F1-score = 0.42) and Decision Trees (mean test set ROC-AUC = 0.48, accuracy = 46%, F1-score = 0.40), thus enabling effective early-stage PTB risk assessment. GO_SCPLOWRAPHICALC_SCPLOW AO_SCPLOWBSTRACTC_SCPLOW O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/558954v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@3da4e1org.highwire.dtl.DTLVardef@113e22aorg.highwire.dtl.DTLVardef@6552b9org.highwire.dtl.DTLVardef@1a232d_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Electroencephalogram Data Collection for Student Engagement Analysis with Audio-Visual Content

Recognizing and monitoring students attention during learning is crucial to successful knowledge acquisition since it influences cognitive function. As a result, gaining a precise picture of a learners mental state may enable interactive learning systems to alter tutoring content, devise effective help tactics, and improve learning outcomes. In computer-based learning environments, keeping track of students mental states is vital. Investigating the feasibility of utilizing active learning in enhancing student engagement index when exposed to various visual stimuli is the genesis of this work. The research includes collecting EEG data from 20 participants (ten males, ten females) while resting and being subjected to various virtual infotainment/educational content. The EEG data were collected using the Allengers Neuro PLOT, a 40-channel wet electrode system. The work includes raw and pre-processed EEG data under quiescent and audio-visual continuous cues. The recorded data is accommodated by a sophisticated EEG data pre-processing pipeline and will be available to the research community for usage. Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@340852org.highwire.dtl.DTLVardef@e5c51org.highwire.dtl.DTLVardef@cefb1borg.highwire.dtl.DTLVardef@c7d208org.highwire.dtl.DTLVardef@ae4824_HPS_FORMAT_FIGEXP M_TBL C_TBL

neuroscience↗

bioRxiv2021

Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current ordinary differential equation-based models fail to capture complex behaviors that fall outside of a predetermined ecological theory and do not scale well with increasing community complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of deep learning, the LSTM learns a high dimensional data-driven non-linear dynamical system model used to design communities with desired metabolite profiles. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model. We build methods decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to identify communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

systems biology↗

bioRxiv2020

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

BackgroundDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

bioinformatics↗