Biology subjects

Majumdar, A.

Publications and source records attributed to Majumdar, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Addressing efficacy of everyday hygiene cleansing products in context of sustainable handwashing behavior in the post-pandemic era

Simple hygiene behaviour such as washing hands is key to improving health of individuals and reducing community transmission of communicable diseases such as respiratory and enteric infections. Consistent and relentless messaging by global and local health authorities had resulted in heightened hygiene awareness amongst the public during the COVID-19 pandemic. Unfortunately, many of the hygiene behaviours practiced during the pandemic have proven to be unsustainable in the immediate period following the pandemic. While CDC guidelines suggest washing hands with soap for a minimum of 20 seconds to prevent the spread of germs, a hygiene interventions effectiveness must be evaluated in context of the prevalent hygiene behaviour. Here we report our findings from an observational study conducted across India, Pakistan, Saudi Arabia, and United Arab Emirates focusing on handwashing habit of individuals in the post-pandemic period. Across all geographies, the time spent for lathering product on skin (contact time) for a significant majority of individuals recruited for the study was found to be 10 seconds and less. To ensure that marketed hygiene formulations such as liquid cleansers and sanitizers are efficacious in inactivating pathogens under conditions practiced by the majority as a part of their daily hand washing practices, we have investigated the in-vitro antimicrobial efficacy of several hygiene cleansing formulations at 10 seconds of contact time. Our results show that well-formulated cleansing solutions can reduce the input titre of both bacterial and viral pathogens by 99.9% or more, even with brief contact. With the global resurgence of both existing and emerging pathogens in the post-pandemic world, promoting sustainable handwashing practices for infection prevention remains a challenge as it is deeply rooted in the socio-economic fabric of many countries. Therefore, it is crucial to conduct regular studies like this to reassure end-users and combat complacency regarding the composition of everyday hygiene products. HighlightsO_LIStudies of handwashing behavior in 4 countries reveals significantly short lathering duration C_LIO_LIAntimicrobial efficacy testing of marketed products was conducted against a range of pathogens C_LIO_LISuitably formulated hygiene products can inactivate pathogens at short durations of contact C_LI

microbiology↗

bioRxiv2025

General model with biclustering for target selection in severe preeclampsia

Preeclampsia is a complex and multifactorial disease and a leading cause of death in pregnancy with no current effective treatment strategies, especially severe preeclampsia. A new general regression model with biclustering scalability is developed to quantify causal relationship from genes to gene products to clinical phenotypes among multiple datasets. It can identify those key genes and relationships between gene expression and clinical phenotype, which will be interpreted further, for molecular mechanism disclosure and effective drug development by pathway enrichment analysis. We observed pattern variation of gene expression relating to clinical phenotype among three preeclampsia datasets in parallel. Eighty-one genes are recommended as potential druggable targets for severe preeclampsia by their highly correlation between gene expression variation with preeclampsia clinical phenotypes in hypertension, HELLP (Hemolysis, Elevated Liver enzymes and Low Platelets) syndrome, proteinuria, and gestation week. Genes ENG, Flt and NEU1 and NEU2 show overexpression in severe preeclampsia in attenuating hypertension and proteinuria in multiple datasets of preeclampsia. Those genes are involved in the angiogenic factors and vascular molecular function. Treatment with either Flt or NEU1 have exciting clinical implications, and likely will transform the detection and treatment for patients with severe preeclampsia. The novel network mathematical models can quantify a causal relationship from genes to gene products to clinical phenotypes, and they can model causal influences of genes that are either monomorphic or polymorphic among heterology datasets, which is suggesting that key gene expression variation may be more likely to influence complex clinical phenotype variation in preeclampsia among multiple datasets.

bioinformatics↗

bioRxiv2024

Covalent inhibition of the SARS-CoV-2 NiRAN domain via an active-site cysteine

The kinase-like NiRAN domain of nsp12 in SARS-CoV-2 catalyzes the formation of the 5 RNA cap structure. This activity is required for viral replication, offering a new target for the development of antivirals. Here, we develop a high-throughput assay to screen for small molecule inhibitors targeting the SARS-CoV-2 NiRAN domain. We identified NCI-2, a compound with a reactive chloromethyl group that covalently binds to an active site cysteine (Cys53) in the NiRAN domain, inhibiting its activity. NCI-2 can enter cells, bind to, and inactivate ectopically expressed nsp12. A cryo-EM reconstruction of the SARS-CoV-2 replication-transcription complex (RTC) bound to NCI-2 offers a detailed structural blueprint for rational drug design. Although NCI-2 showed limited potency against SARS-CoV-2 replication in cells, our work lays the groundwork for developing more potent and selective inhibitors targeting the NiRAN domain. This approach presents a promising therapeutic strategy for effectively combating COVID-19 and potentially mitigating future coronavirus outbreaks.

biochemistry↗

bioRxiv2024

Loss of KDM6A-mediated genomic instability and metabolic reprogramming differentially regulates responses to immune checkpoint therapy and chemotherapy in bladder cancer.

Mutations in genes encoding critical epigenetic regulators are frequently noted in bladder cancer, however, the impact of these mutations on therapeutic efficacy is unclear. One of the most common driver mutations in bladder cancer occurs in the KDM6A gene, which encodes a histone demethylase that promotes gene transcription. Retrospective analyses of patients with bladder cancer demonstrated that KDM6A mutations correlate with improved overall survival (OS) with immune checkpoint therapy (ICT), while they are associated with lower OS in patients undergoing cisplatin-based chemotherapy. Mechanistic studies utilizing CRISPR-Cas9 mediated deletion of Kdm6a showed reduced expression of DNA mismatch repair (MMR) and DNA double-stranded base repair (DSBR) genes in tumor cells with improved response to anti-PD-1 therapy and attenuated sensitivity to cisplatin-based chemotherapy in preclinical models of bladder cancer. Additionally, the loss of Kdm6a-mediated reduction in glycolysis and intratumoral lactate accumulation impaired histone 3 lysine 9 lactylation (H3K9la) and histone 3 lysine 18 lactylation (H3K18la) in Tregs with concurrent decrease in the expression of key genes including Foxp3, Tgfb and Pdcd1 and their immune-suppressive function. Further, reduced expansion of PD-1hi Tregs improved the ratio of cytotoxic T cells to Tregs and response to anti-PD-1 therapy in Kdm6a deficient tumor-bearing mice. Collectively, this study provided key insights into the role of KDM6A-mediated epigenetic regulation of DNA repair and metabolic reprogramming which potentially govern response to chemotherapy and ICT thus highlighting the utility of KDM6A mutation status for patient stratification and development of personalized treatment algorithms.

immunology↗

bioRxiv2024

Distinct representations of economic variables across regions and projections of the frontal cortex

Economic decision-making requires evaluating information about available options, such as their expected value and economic risk. Previous studies have shown that frontal cortical neurons encode these variables, but how this encoding is structured across different frontal regions and projection pathways remains unclear. We developed a decision-making task for head-fixed mice in which we varied the expected value and risk associated with reward-predicting stimuli. Using large-scale electrophysiology and projection-specific optotagging across multiple frontal regions, we identified distinct spatial gradients for these variables, with stronger expected value coding in dorsal regions and stronger risk coding in medial regions. We then demonstrated that this encoding further depends on the neuronal projections: frontal neurons projecting to the dorsomedial striatum and claustrum differentially encoded economic variables. Our findings illustrate that frontal cortical representation of economic variables is jointly determined by spatial organization and downstream connectivity of neurons, revealing a structured, multi-scale code for economic variables.

neuroscience↗

bioRxiv2024

Regulating IL-2 immune signaling function via a core allosteric structural network

Human interleukin-2 (IL-2) is a crucial cytokine for T cell regulation, with therapeutic potential in cancer and autoimmune diseases. However, IL-2s pleiotropic effects across different immune cell types often lead to toxicity and limited efficacy. Previous efforts to enhance IL-2s therapeutic profile have focused on modifying its receptor binding sites. Yet, the underlying dynamics and intramolecular networks contributing to IL-2 receptor recognition remain unexplored. This study presents a detailed characterization of IL-2 dynamics compared to two engineered IL-2 mutants, "superkines" S15 and S1, which exhibit biased signaling towards effector T cells. Using NMR spectroscopy and molecular dynamics simulations, we demonstrate significant variations in core dynamic pathways and conformational exchange rates across these three IL-2 variants. We identify distinct allosteric networks and excited state conformations in the superkines, despite their structural similarity to wild-type IL-2. Furthermore, we rationally design a mutation (L56A) in the S1 superkines core network, which partially reverts its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2. Our results reveal that IL-2 superkine core dynamics play a critical role in their enhanced receptor binding and function, suggesting that modulating IL-2 dynamics and core allostery represents an untapped approach for designing immunotherapies with improved immune cell selectivity profiles. HighlightsO_LINMR and molecular dynamics simulations revealed distinct conformational dynamics and allosteric networks in computationally re-designed IL-2 superkines compared to wild-type IL-2, despite their similar crystal structures. C_LIO_LIThe superkines S1 and S15 exhibit altered sampling of excited state conformations at an intermediate timescale, with slower conformational exchange rates compared to wild-type IL-2. C_LIO_LIA rationally designed mutation (L56A) in the S1 superkines core allosteric network partially reverted its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2. C_LIO_LIOur study demonstrates that IL-2 core dynamics play a critical role in receptor binding and signaling function, providing a foundation for engineering more selective IL-2-based immunotherapies. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=152 HEIGHT=200 SRC="FIGDIR/small/617024v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1e298adorg.highwire.dtl.DTLVardef@195b602org.highwire.dtl.DTLVardef@1e06205org.highwire.dtl.DTLVardef@4351e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

SARS-CoV-2 Survival on Skin and its Transfer from Contaminated Surfaces

Understanding the transmission dynamics of SARS-CoV-2, particularly its transfer from contaminated surfaces (fomites) to human skin, is crucial for mitigating the spread of COVID-19. While extensive research has examined the persistence of SARS-CoV-2 on various surfaces, there is limited understanding of how efficiently it transfers to human skin, and how long it survives on the skin. This study investigates two key aspects of SARS-CoV-2 transmission: (1) the transfer efficiency of SARS-CoV-2 from non-porous (plastic and metal) and porous (cardboard) surfaces to a 3D human skin model (LabSkin), and (2) the survival of SARS-CoV-2 on the skin under different temperature conditions. First, we validated LabSkin as a suitable surrogate for human skin by comparing the transfer efficiency of the bacteriophage Phi 6 from surfaces to LabSkin and to human volunteers fingers. No significant differences were observed, confirming LabSkins suitability for these studies. Subsequently, the transfer of SARS-CoV-2 from surfaces to LabSkin was assessed, showing that plastic and metal surfaces had similar transfer efficiencies ([~]13%), while no transfer occurred from cardboard once the inoculum had dried on the surface. Finally, the survival of SARS-CoV-2 on skin was assessed, showing a rapid decay at higher temperatures, with a half-life ranging from 2.8 to 17.8 hours depending on the temperature. These findings enhance our understanding of viral transmission via fomites and inform public health strategies to reduce the risk of SARS-CoV-2 transmission through surface contact.

microbiology↗

bioRxiv2024

A unified Bayesian approach to transcriptome-wide association study

The transcriptome-wide association study (TWAS) has identified novel gene-trait associations, providing essential biological insights. TWAS combines reference panel transcriptome and genome-wide association study (GWAS) data. Traditional TWAS methods construct a prediction model for gene expression based on the transcriptome data, which is then employed to impute gene expression in the GWAS data. The complex trait in GWAS is regressed on the predicted expression to identify gene-trait associations. Such a two-step approach ignores the uncertainty of the imputed expression and can lead to reduced inference accuracy. We develop a unified Bayesian approach for TWAS that avoids the need for a two-step approach by modeling the two datasets simultaneously. We consider the horseshoe prior to model the relationship between gene expression and local SNPs, and the spike and slab prior to test for an association between the genetic component of expression and the trait to build an integrated Bayesian framework. We extend the method to conducting a multi-ancestry TWAS, focusing on discovering genes that affect the trait in either or both ancestries. Using extensive simulations, we demonstrate that the new approach performs better than existing methods in terms of correctly classifying non-null genes and the accuracy of effect size estimation. To demonstrate our approach, we perform a single and multi-ancestry TWAS for intraocular pressure (IOP), integrating the Geuvadis transcriptome and UK Biobank GWAS data. We find that the identified set of IOP-associated genes is enriched in relevant pathways.

genomics↗

bioRxiv2024

On the relationship between serotype, chemotype and genotype for Cryptococcus spp. including a method for including polysaccharide structure in strain characterization

Over the past eight decades the classification of cryptococcal strains has relied on the technologies available to discriminate among isolates, such as serology, exopolysaccharide (EPS) NMR, and most recently multi-locus sequencing, which yielded serotypes, chemotypes, and genotypes, respectively. However, as one method superseded the other, the relationship between classification schemes became uncertain, resulting in assumptions that have not been rigorously validated. Here we compared the serotype, chemotype, and genotype as defined by multi-locus sequence typing (MLST) of 63 strains for which both serotype and chemotype characterization was available. None of the three strain typing methods were correlative with each other, although for about 50% of the strains there was correlation between chemotype and genotype. To address this, we updated the methodology of GXM motif categorization for cryptococcal strains through analyzing filter-isolated EPS by 1D [1H] NMR and assigning GXM motifs along with the O-acetylation level. The result is a facile method using minimally processed EPS material coupled with simple cryptococcal strain classification by GXM motif expression. While this method increases the correlation between MLST genotype and GXM motif expression, further studies to establish the polysaccharide regulatory genes in cryptococcus will be necessary to understand the polysaccharide differences. In summary, none of the current classification methods correlated with each other, indicating a dissociation between genotype and phenotype, which poses a challenge to the cryptococcal field for explaining how phenotypic characteristics arise and are maintained. Author SummaryThis work examines the relationship between methods of cryptococcal strain classification and the primary structure of the predominant polysaccharide glucuronoxylomannan (GXM). We find no strong correlation between the expression of trimannose repeat motifs of GXM by a strain and serotype, MLST genotype, or the previous polysaccharide classification system, chemotyping. With an understanding that none of these classification systems accurately represent the GXM sequences in cryptococcal polysaccharides, we describe a new, facile, rapid method of polysaccharide isolation and GXM motif expression characterization. Application of this method reveals a strong association between high levels of GXM O-acetylation, necessary for antibody binding, and the expression of a single motif of GXM. Additionally, the association between MLST genotype and GXM motif expression is higher when utilizing this new method. While much remains to be understood about this complex system, this work provides a new method to include GXM motif expression in cryptococcal strain expression allowing for more accurate strain descriptions in the future.

microbiology↗

bioRxiv2024

Central activation of catecholamine-independent lipolysis drives the end-stage catabolism of all adipose tissues

Several adipose depots, including constitutive bone marrow adipose tissue (cBMAT), resist conventional lipolytic cues, making them metabolically non-responsive. However, under starvation, wasting, or cachexia, the body can eventually catabolize these stable adipocytes through unknown mechanisms. To study this, we developed a mouse model of brain-evoked depletion of all fat, including cBMAT, independent of food intake. Genetic, surgical, and chemical approaches demonstrated that depletion of stable fat required adipose triglyceride lipase-dependent lipolysis but was independent of local nerves, the sympathetic nervous system, and catecholamines. Instead, concurrent hypoglycemia and hypoinsulinemia activated a potent catabolic state by suppressing lipid storage and increasing catecholamine-independent lipolysis via downregulation of cell-autonomous lipolytic inhibitors Acvr1c, G0s2, and Npr3. This was also sufficient to delipidate classical adipose depots. Overall, this work defines unique adaptations of stable adipocytes to resist lipolysis in healthy states while isolating a potent in vivo neurosystemic pathway by which the body can rapidly catabolize all adipose tissues.

physiology↗

bioRxiv2024

Enabling non-viral DNA delivery using lipid nanoparticles co-loaded with endogenous anti-inflammatory lipids

Lipid nanoparticles (LNPs) have transformed genetic medicine, recently shown by their use in COVID-19 mRNA vaccines. While loading LNPs with mRNA has many uses, loading DNA would provide additional advantages such as long-term expression and availability of promoter sequences. However, here we show that plasmid DNA (pDNA) delivery via LNPs (pDNA-LNPs) induces acute inflammation in naive mice which we find is primarily driven by the cGAS-STING pathway. Inspired by DNA viruses that inhibit this pathway for replication, we co-loaded endogenous lipids that inhibit STING into pDNA-LNPs. Specifically, loading nitro-oleic acid (NOA) into pDNA-LNPs (NOA-pDNA-LNPs) ameliorates serious inflammatory responses in vivo enabling prolonged transgene expression (at least 1 month). Additionally, we demonstrate the ability to iteratively optimize NOA-pDNA-LNPs expression by performing a small LNP formulation screen, driving up expression 50-fold in vitro. Thus, NOA-pDNA-LNPs, and pDNA-LNPs co-loaded with other bioactive molecules, will provide a major new tool in the genetic medicine toolbox, leveraging the power of DNAs long-term and promoter-controlled expression.

bioengineering↗

bioRxiv2024

A multivariate approach to joint testing of main genetic and gene-environment interaction effects

Gene-environment (GxE) interactions crucially contribute to complex phenotypes. The statistical power of a GxE interaction study is limited mainly due to weak GxE interaction effect sizes. To utilize the individually weak GxE effects to improve the discovery of associated genetic loci, Kraft et al. [1] proposed a joint test of the main genetic and GxE effects for a univariate phenotype. We develop a testing procedure to evaluate combined genetic and GxE effects on a multivariate phenotype to enhance the power by merging pleiotropy in the main genetic and GxE effects. We base the approach on a general linear hypothesis testing framework for a multivariate regression for continuous phenotypes. We implement the generalized estimating equations (GEE) technique under the seemingly unrelated regressions (SUR) setup for binary or mixed phenotypes. We use extensive simulations to show that the test for joint multivariate genetic and GxE effects outperforms the univariate joint test of genetic and GxE effects and the test for multivariate GxE effect concerning power when there is pleiotropy. The test produces a higher power than the test for multivariate main genetic effect for a weak genetic and substantial GxE effect. For more prominent genetic effects, the latter performs better with a limited increase in power. Overall, the multivariate joint approach offers high power across diverse simulation scenarios. We apply the methods to lipid phenotypes with sleep duration as an environmental factor in the UK Biobank. The proposed approach identified six independent associated genetic loci missed by other competing methods.

genomics↗

bioRxiv2024

Antibiotic Bacteria Interaction: Dataset and Benchmarking.

Withdrawal StatementThe authors have withdrawn their manuscript owing to inability to reproduce the results. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioengineering↗

bioRxiv2023

Evidence for a Causal Dissociation of the McGurk Effect and Congruent Audiovisual Speech Perception via TMS

Congruent visual speech improves speech perception accuracy, particularly in noisy environments. Conversely, mismatched visual speech can alter what is heard, leading to an illusory percept known as the McGurk effect. This illusion has been widely used to study audiovisual speech integration, illustrating that auditory and visual cues are combined in the brain to generate a single coherent percept. While prior transcranial magnetic stimulation (TMS) and neuroimaging studies have identified the left posterior superior temporal sulcus (pSTS) as a causal region involved in the generation of the McGurk effect, it remains unclear whether this region is critical only for this illusion or also for the more general benefits of congruent visual speech (e.g., increased accuracy and faster reaction times). Indeed, recent correlative research suggests that the benefits of congruent visual speech and the McGurk effect reflect largely independent mechanisms. To better understand how these different features of audiovisual integration are causally generated by the left pSTS, we used single-pulse TMS to temporarily impair processing while subjects were presented with either incongruent (McGurk) or congruent audiovisual combinations. Consistent with past research, we observed that TMS to the left pSTS significantly reduced the strength of the McGurk effect. Importantly, however, left pSTS stimulation did not affect the positive benefits of congruent audiovisual speech (increased accuracy and faster reaction times), demonstrating a causal dissociation between the two processes. Our results are consistent with models proposing that the pSTS is but one of multiple critical areas supporting audiovisual speech interactions. Moreover, these data add to a growing body of evidence suggesting that the McGurk effect is an imperfect surrogate measure for more general and ecologically valid audiovisual speech behaviors.

neuroscience↗

bioRxiv2023

Identification of two novel DnaJ chaperone family proteins as modifiers of Huntingtin aggregates

Huntingtons disease (HD) is a rare neurodegenerative disease. It is caused due to aggregation of Huntingtin (HTT) protein containing Q repeats more than 40. Similar protein aggregation is also a hallmark of several other neurodegenerative diseases related to loss of cognitive function. In search of modifiers of HTT aggregation, we have screened putative chaperone proteins from Drosophila in both fly and yeast model of HD. DnaJ chaperones were screened by evaluating HTT protein aggregation related phenotypes using growth assays for studying the growth rate of the cells, imaging studies, and gel-based approaches like semi-denaturing detergent agarose gel electrophoresis (SDD-AGE). Our screening led us to categorize several proteins as suppressors and enhancers of the HTT associated phenotypes. Out of the 40 chaperones and co-chaperones, two chaperones that came up strikingly were CG5001 and P58IPK. Protein aggregation was found to be reduced in both S2 cells and Drosophila transgenic lines with HTT103Q in presence of these class of chaperones. As these DnaJ chaperones have protein sequence similarity across species, these might be used as possible tools to combat the effects of neurodegenerative diseases, as evidenced specifically in Huntingtons disease and Amyotrophic Lateral Sclerosis (ALS).

genetics↗

bioRxiv2023

Temporal regularities shape perceptual decisions and striatal dopamine signals

Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that mice adapt their perceptual choice history biases to different temporal regularities. This adaptation is well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that striatal dopamine tracks predictions of the model and behavior, pointing towards the involvement of dopamine in forming adaptive history biases. Our results reveal the adaptive nature of perceptual choice history biases, and shed light on their underlying computational principles and neural implementation.

neuroscience↗

bioRxiv2022

Adjustment for the uncertainty of predicted expression in transcriptome-wide association study: a fusion of measurement error theory and bootstrapping

Transcriptome-wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene-phenotype associations. We propose a novel approach to adjust for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor that needs to be incorporated in the unadjusted TWAS. We base the framework on adaptive Lasso. We use extensive simulations to show that the traditional TWAS inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of an inflated false positive rate, the unadjusted TWAS offers a limited increase in power than the adjusted TWAS, which is statistically unjustified. Our approach produces more accurate estimates of the genes effect size than a traditional approach. We demonstrate the merits of the adjusted approach by conducting TWAS for height and lipid phenotypes, LDL, HDL, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.

genomics↗

bioRxiv2022

Graph Regularized Probabilistic MatrixFactorization for Drug-Drug Interactions Prediction

Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts.

bioinformatics↗