Biology subjects

Bi, Y.

Publications and source records attributed to Bi, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Modeling Post-Gastrula Development via Bidirectional Pluripotent Stem Cells

The absence of stem cells capable of efficiently generating both trophoblast and epiblast lineages has hindered precise recapitulation of embryonic development. Through high-content chemical screening, we established an AL medium to generate mouse and human bidirectional pluripotent stem cells (BPSCs) characterized by concurrent OCT4/CDX2 expression. Mouse BPSCs demonstrated high plastic differentiation into trophoblast, epiblast and primitive endoderm lineages in vitro within 48 hours without exogenous induction factors and efficiently contributed to the embryo and extraembryonic tissues in vivo. Mechanistically, hyperactivation of the Wnt signaling pathway breaks the early lineage differentiation barrier by initiating a Lef1-dependent bypass. Remarkably, BPSCs can efficiently generated E8.5 embryoids that completed gastrulation and displayed advanced features such as brain development, a closed neural tube, a beating heart, somite formation, and primordial germ cells. These findings highlight BPSCs as a powerful tool for investigating early lineage specification and post-gastrulation embryonic development, with potential applications across multiple species.

developmental biology↗

bioRxiv2025

Common Gene Networks Orchestrate Organelle Architecture and Inter-Organelle Metabolic Flows for Mucin Production in High Endothelial and Goblet Cells

High endothelial cells (HECs) are specialized vascular gatekeepers that control lymphocyte entry into lymph nodes, a process essential for immune surveillance and adaptive responses. However, how HECs coordinate their high biosynthetic demands with the secretory apparatus to support immune function remains unclear. Using multi-omic approaches, we identify IRE1-XBP1-centered transcriptional networks that, together with CREB3L2-associated gene programs, coordinate inter-organelle metabolic and secretory pathways required for glycosylation and assembly of peripheral node addressin (PNAd), a sulfated sialomucin critical for lymphocyte homing. Genetic or pharmacological perturbation of these pathways disrupts HEC morphology and lymphocyte recruitment during homeostasis and impairs HEC induction during inflammation. Parallel transcriptional programs operate in mucin-producing intestinal goblet cells, suggesting common regulatory pathways linking metabolism and sulfated mucin specialization with the associated expansion of secretory organelles across immune and barrier tissues. Thus, our findings identify transcriptional programs that coordinately scale metabolic pathways and secretory organelles to support the biosynthetic infrastructure underlying the morphology and immune trafficking functions of HEVs. Key pointsXBP1-centered transcriptional networks coordinate metabolic and secretory programs for PNAd biosynthesis. IRE1 or S1P inhibition flattens HEVs, prevents ectopic HEV formation and, reduces lymphocyte recruitment Endothelial XBP1 deletion disrupts HEV morphology and lymphocyte trafficking. Goblet cells share secretory transcriptional programs and regulatory logic with HEV.

immunology↗

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2025

Semantics across the globe: A universal neurocognitive semantic structure adaptive to climate

Thousands of languages are used worldwide as the primary means of human thought communications. While both similarities and variations in word meaning (semantics) across different languages are well recognized, the underlying mechanisms remain enigmatic without a coherent theoretical model for semantic representation. Given that semantic representation is a product of the human brain, we address this issue through the lens of neurocognitive theories, with the consensus framework that semantics are derived from sensory experiences, with a set of dimensions being identified as biologically salient in neuroscientific studies. We operationalized word semantic representations with this set of specific dimensions, using computational models (53 languages word embedding data; Study 1), human behavioral ratings (253 subjects, 8 languages; Study 2), and brain activity data (86 subjects, 45 languages; Study 3), and analyzed the similarity and variation patterns of concepts across different languages. These three approaches converge on the finding that, across diverse language samples, word semantic representations along the neurocognitive dimensional structures exhibit strong commonalities, with variations along this structure being significantly and uniquely explained by climate, beyond sociocultural-centered variables. These results present a universal, biologically constrained semantic structure that is adaptive to environmental inputs, reconciling the classical universality and relativity debate.

neuroscience↗

bioRxiv2025

Impact of trait measurement error on quantitative genetic analysis of computer vision derived traits

BackgroundQuantitative genetic analysis of image- or video-derived phenotypes is increasingly being performed for a wide range of traits. Pig body weight values estimated by a conventional approach or a computer vision system can be considered as two different measurements of the same trait, but with different sources of phenotyping error. Previous studies have shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, can be influenced by genetics, suggesting that the error is systematic rather than random and is more likely to lead to misleading quantitative genetic analysis results. Therefore, we investigated the effect of trait measurement error on genetic analysis of pig body weight (BW). ResultsCalibrated scale-based and image-based BW showed high coefficients of determination and goodness of fit. Genomic heritability estimates for scale-based and image-based BW were mostly identical across growth periods. Genomic heritability estimates for trait measurement error were consistently negligible, regardless of the choice of computer vision algorithm. In addition, genome-wide association analysis revealed no overlap between the top markers identified for scale-based BW and those associated with trait measurement error. Overall, the deep learning-based regressions outperformed the adaptive thresholding segmentation methods. ConclusionsThis study showed that manually measured scale-based and image-based BW phenotypes yielded the same quantitative genetic results. We found no evidence that BW trait measurement error could be influenced, at least in part, by genetic factors. This suggests that trait measurement error in pig BW does not contain systematic errors that could bias downstream genetic analysis.

genetics↗

bioRxiv2025

NUSAP1 regulates mitotic processes via KIF2C interaction and AURKA phosphorylation in primary microcephaly

Primary microcephaly (PM) is a neurodevelopmental disorder characterized by a significantly smaller head than normal. Despite the identification of several genes associated with PM subtypes, the etiology remains unclear in a significant proportion of patients. Here, we reported two de novo nonsense mutations in the NUSAP1 gene identified in two independent PM families: c.1209 C>G (p.Tyr403*) and c.1213C>T (p.Gln405*). Nusap1-edited mouse models, designed to mimic Human NUSAP1 mutations, replicated the microcephaly phenotype, characterized by a small brain and thinner cerebral cortex. We demonstrated that Nusap1 was specifically expressed in neural stem/progenitor cells (NSPCs), and the number of NSPCs was significantly reduced in Nusap1-edited mice. In vitro experiments revealed that NUSAP1 mutations disrupted mitotic metaphase chromosome alignment, increased polyploidy, induced cell cycle arrest, and promoted apoptosis. Co-IP confirmed direct interactions between NUSAP1 and KIF2C, and showed that truncating mutations impaired their binding. Furthermore, NUSAP1 rescued the cell cycle arrest and increased apoptosis caused by KIF2C overexpression in NUSAP1 KO HEK293T cells, whereas the mutant truncated protein failed to exert such a rescue effect. We also uncovered an interaction between NUSAP1 and Aurora kinase A (AURKA). Intriguingly, subsequent experiments revealed that AURKA-mediated phosphorylation of NUSAP1 modulates its binding affinity to KIF2C. In conclusion, our findings established NUSAP1 as a novel pathogenic gene for PM. We proposed a model in which AURKA-mediated phosphorylation of NUSAP1 regulates its interaction with KIF2C, balancing spindle microtubule stability and depolymerization to ensure proper sister chromatid segregation. Disruptions in this pathway impair NSPCs mitosis, thereby affecting neocortical development.

genetics↗

bioRxiv2025

A synthetic biology toolkit for interrogating plasmid-dependent methylotrophy and enhancing methanol-based biosynthesis of Bacillus methanolicus

Bacillus methanolicus represents a thermophilic methylotroph whose methanol utilization depends on plasmid-encoded genes. It serves as a unique model for deciphering plasmid-dependent methylotrophy and an ideal chassis for low-carbon biomanufacturing using CO2-derived C1 substrates. Despite its evolutionary uniqueness and industrial potential, the lack of synthetic biology tools has hindered both mechanistic understanding and strain engineering. Here, we present a comprehensive synthetic biology platform comprising a high-efficiency electroporation protocol, a CRISPR method enabling robust and multiplex genome editing, diverse neutral loci for gene integration and overexpression, and a cloud-based genome-scale metabolic model iBM822 for user-friendly biodesign. Leveraging this toolkit, we systematically dissected plasmid-dependent methylotrophy, host restriction-modification systems, and functional significance of the chromosomal methylotrophic genes through targeted deletion. To address plasmid loss-induced strain degeneration, we integrated the large endogenous plasmid pBM19 into the chromosome for stable and intact methylotrophic growth. Finally, by integrating metabolic modeling with CRISPR editing, we engineered L-arginine feedback regulation to achieve the first L-arginine biosynthesis from methanol. This study establishes a synthetic biology framework for B. methanolicus, promoting mechanistic exploration of methylotrophy and low-carbon biomanufacturing.

synthetic biology↗

bioRxiv2025

KcatNet: advancing genome-wide enzyme turnover number prediction through structural enzymatic characterization

Enzyme turnover numbers (Kcat) are fundamental kinetic constants that quantify enzymatic efficiency. Systematic studies of Kcat are essential for characterizing the mechanisms underlying proteomic composition and cellular metabolism. However, experimental measurements of Kcat remain limited and prone to noise. To address this, we present KcatNet, a geometric deep learning model designed for high-throughput prediction of Kcat in metabolic enzymes across all organisms, leveraging paired enzyme sequence and substrate representations. KcatNet consistently outperforms existing predictors, particularly for enzymes with high catalytic efficiency, and demonstrates strong generalization to enzymes that are dissimilar to those in the training set. Furthermore, KcatNet uncovers structural mechanisms and interaction patterns within enzyme-substrate complexes, providing insights into architectural principles that are inaccessible with existing methods by harnessing the representational power of large-scale protein language models. We applied KcatNet to genome-scale Kcat prediction across diverse yeast species, improving proteome allocation predictions by integrating its outputs into metabolic models. Experimental validation further confirmed the models ability to identify enzyme mutants with enhanced activity. By bridging the gap between sequence, structure, and function, KcatNet establishes a robust foundation for advancing understanding of molecular-level mechanisms and accelerating enzyme engineering efforts.

bioinformatics↗

bioRxiv2025

Emergent modularity in large language models: Insights from aphasia simulations

Recent large language models (LLMs) have demonstrated remarkable proficiency in complex linguistic tasks and have been shown to share certain computational principles with human language processing. However, whether LLMs internal components perform distinct functions, like semantic and syntactic processing in human language systems, remains unclear. Here, we systematically disrupted components of LLMs to simulate the behavioral profiles of aphasia--a disorder characterized by specific language deficits resulting from brain injury. Our findings showed that lesioning specific components of LLMs could replicate behaviors characteristic of different aphasia subtypes. Notably, while semantic deficits as those observed in Wernickes and Conduction aphasia, were relatively straightforward to simulate, reproducing syntactic and lexical impairments, as seen in Brocas and Anomic aphasia, proved more challenging. Together, these results highlight both parallels and discrepancies between emergent modularity in LLMs and the human language system, providing new insights into how information is represented and processed in artificial and biological intelligence.

neuroscience↗

bioRxiv2025

Learning robust gene expression embeddings for disease subtyping, biomarker identification and cross-species alignment

High dimension gene expression measurements such as microarray and RNA-seq data, are often plagued by sources of unwanted variation. This variability can lead to the obscuring of meaningful biological signal by technical noise and non-interesting biological variation, thus resulting in failure to identify the same set of targets and biomarkers in independent studies. This phenomenon contributes to the so-called reproducibility crisis and makes preclinical drug development more challenging. There is an urgent need for an improved process to identify shared biological signals in large datasets from independent cohorts. In this article, we propose an innovative method called Joint-Embedding via Canonical Correlation Analysis (JECCA), which aims to capture relevant biological variation by learning of shared lower dimension embedding across multiple datasets. We demonstrate the efficacy of JECCA in analyzing multiple human cancer and inflammatory disease gene expression datasets, showcasing its ability to identify robust, biologically relevant signals. By leveraging these embeddings, our approach enables several important applications: (1) robust disease endotype identification by joint unsupervised analysis of multiple independent gene expression datasets (2) reproducible and biologically meaningful biomarker identification to enhance prediction of treatment outcome (3) cross-platform analysis, such as integration of RNA-seq and microarray data, which are accumulating exponentially in public databases (4) cross-species analysis to identify conserved molecular features between human and mouse. We provide comparative analyses that highlight the superior performance of JECCA over other approaches such as batch value-corrected methods based on joint analysis. As analysis of publicly available datasets becomes more prevalent, we foresee JECCA being employed in multiple disease contexts.

bioinformatics↗

bioRxiv2024

Synthesis of USC-093 and comparison with its promoiety enantiomer USC-093D against adenovirus in vitro and in a Syrian hamster model

Adenovirus infections of immunocompromised humans are a significant source of morbidity and mortality. At present, no drug has been approved by FDA for the treatment of adenovirus infections. A current treatment of such infections is off-label use of an antiviral acyclic nucleotide phosphonate, cidofovir (CDV, (S)-HPMPC), which requires i.v. administration and has dose-limiting kidney toxicity. We recently reported that USC-093, a homoserinamide analogue of the tyrosinamide (S)- HPMPA prodrug USC-087, was orally effective at a 10 mg/kg against disseminated human adenovirus infection (HAdV-C6) in a Syrian hamster model, although their efficacy was marginal after respiratory infection. Neither prodrug manifested GI toxicity. Unlike USC-087, USC-093 showed no significant nephrotoxicity at the effective dose. Here, we describe in detail the synthesis of USC-093 and also its D-homoserinamide analogue, USC-093D, in four steps (20-40% overall yield) starting from Boc-protected L-homoserine or D-homoserine lactone, respectively. The two stereoisomeric prodrugs had EC50 30-70 nM vs. Ad5 or 1-6 nM vs. Ad6 in HFF cells, with USC-093D giving the lower values. The prodrugs were 30-59x more potent vs. Ad5 and 82-332x more potent than Ad6 relative to the positive control, CDV. To ascertain whether D-chirality in the promoiety could enhance the performance of the prodrug in vivo, USC-093D and USC-093 were compared in the Syrian hamster model (treated from day 1 q.d at an experimentally determined maximum tolerated oral dose of 20 mg/kg)). In this study, the hamsters were instilled i.n. with vehicle or 4X1010 PFU/kg of HAdV-C6 to promote lung infection. Oral valganciclovir (VGCV) at 200 mg/kg b.i.d. was used as the positive control. The body weights were recorded daily, and at 3 days post challenge, gross pathological observation was performed. Lung samples were collected, and the virus burden was determined by TCID50 assay. The results show that altering homoserine stereochemistry did not markedly improve the efficacy of the orally administered prodrug, consistent with the premise that its mechanism of transport is likely not dependent on stereoselective pathways, such as hPEPT1-mediated uptake.

biochemistry↗

bioRxiv2024

Modulating DNA Polα Enhances Cell Reprogramming Across Species

As a fundamental biological process, DNA replication ensures the accurate copying of genetic information. However, the impact of this process on cellular plasticity in multicellular organisms remains elusive. Here, we find that reducing the level or activity of a replication component, DNA Polymerase (Pol), facilitates cell reprogramming in diverse stem cell systems across species. In Drosophila male and female germline stem cell lineages, reducing Pol levels using heterozygotes significantly enhances fertility of both sexes, promoting reproductivity during aging without compromising their longevity. Consistently, in C. elegans the pola heterozygous hermaphrodites exhibit increased fertility without a reduction in lifespan, suggesting that this phenomenon is conserved. Moreover, in male germline and female intestinal stem cell lineages of Drosophila, pol heterozygotes exhibit increased resistance to tissue damage caused by genetic ablation or pathogen infection, leading to enhanced regeneration and improved survival during post-injury recovery, respectively. Additionally, fine tuning of an inhibitor to modulate Pol activity significantly enhances the efficiency of reprogramming human embryonic fibroblasts into induced pluripotent cells. Together, these findings unveil novel roles of a DNA replication component in regulating cellular reprogramming potential, and thus hold promise for promoting tissue health, facilitating post-injury rehabilitation, and enhancing healthspan.

developmental biology↗

bioRxiv2024

Cortical connectivity supports motoric synchronization to both auditory and visual rhythms in a frontal-temporal network

Synchronizing motoric responses to metrical sensory rhythms is key to social activities, e.g., group singing and dancing. It remains elusive, however, whether there is a common neural network for motoric synchronization to metrical rhythms from different sensory modalities. Here, we separate sensorimotor responses from basic sensory responses by combining a metrical sensorimotor synchronization task with frequency-domain magnetoencephalography (MEG) analysis. A common frontal-temporal network, not including visual cortex, is observed during both visual- and auditory-motor synchronization, and the network remains in congenitally deaf participants during visual-motor synchronization, suggesting the network is formed by intrinsic cortical connections instead of auditory experience. Furthermore, activation of the left and right frontal-temporal areas, as well as the ipsilateral white matter connection, separately predict the precision of auditory and visual synchronization. These results reveal a common but lateralized frontal-temporal network for visual- and auditory-motor synchronization, which is generated based on intrinsic cortical connections.

neuroscience↗

bioRxiv2024

Genomic prediction of metabolic content in rice grain in response to warmer night conditions

It has been argued that metabolic content can be used as a selection marker to accelerate crop improvement because metabolic profiles in crops are often under genetic control. Evaluating the role of genetics in metabolic variation is a long-standing challenge. Rice, one of the worlds most important staple crops, is known to be sensitive to recent increases in nighttime temperatures. Quantification of metabolic levels can help measure rice responses to high night temperature (HNT) stress. However, the extent of metabolic variation that can be explained by regression on whole-genome molecular markers remains to be evaluated. In the current study, we generated metabolic profiles for mature grains from a subset of rice diversity panel accessions grown under optimal and HNT conditions. Metabolite accumulation was low to moderately heritable, and genomic prediction accuracies of metabolite accumulation were within the expected upper limit set by their genomic heritability estimates. Genomic heritability estimates were slightly higher in the control group than in the HNT group. Genomic correlation estimates for the same metabolite accumulation between the control and HNT conditions indicated the presence of genotype-by-environment interactions. Reproducing kernel Hilbert spaces regression and image-based deep learning improved prediction accuracy, suggesting that some metabolite levels are under non-additive genetic control. Joint analysis of multiple metabolite accumulation simultaneously was effective in improving prediction accuracy by exploiting correlations among metabolites. The current study serves as an important first step in evaluating the cumulative effect of markers in influencing metabolic variation under control and HNT conditions. Core ideasO_LIRice is sensitive to increases in nighttime and daytime temperatures C_LIO_LIMetabolite accumulation from rice grains was low to moderately heritable C_LIO_LINon-additive genomic prediction models improved prediction accuracy for some metabolites C_LIO_LIResults shed new light on the utility of genomic predictions for metabolite accumulation from rice grains C_LI

genetics↗

bioRxiv2024

Mesoscale chromatin confinement facilitates target search of pioneer transcription factors in live cells

Pioneer transcription factors (PTFs) possess the unique capability to access closed chromatin regions and initiate cell fate changes, yet the underlying mechanisms remain elusive. Here, we characterized the single-molecule dynamics of PTFs targeting chromatin in living cells, revealing a notable "confined target search" mechanism. PTFs like FOXA1, FOXA2, SOX2, OCT4 and KLF4 sampled chromatin more frequently than non-pioneer factor MYC, alternating between fast free diffusion in the nucleus and slower confined diffusion within mesoscale zones. Super-resolved microscopy showed closed chromatin organized as mesoscale nucleosome-dense domains, confining FOXA2 diffusion locally and enriching its binding. We pinpointed specific histone-interacting disordered regions, distinct from DNA-binding domain, crucial for confined target search kinetics and pioneer activity within closed chromatin. Fusion to other factors enhanced pioneer activity. Kinetic simulations suggested transient confinement could increase target association rate by shortening search time and binding repeatedly. Our findings illuminate how PTFs recognize and exploit closed chromatin organization to access targets, revealing a pivotal aspect of gene regulation.

molecular biology↗

bioRxiv2024

SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes

The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (i) consistently outperforms currently-available predictors; (ii) provides accurate results when applied to inferred enzyme structures; and (iii) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental data sets, we show that SCREENs predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations.

bioinformatics↗

bioRxiv2024

Collagen I is a critical organizer of scarring and CNS regeneration failure

Although axotomized neurons retain the ability to initiate the formation of growth cones and attempt to regenerate after spinal cord injury, the scar area formed as a result of the lesion in most adult mammals contains a variety of reactive cells that elaborate multiple extracellular matrix and enzyme components that are not suitable for regrowth1,2. Newly migrating axons in the vicinity of the scar utilize upregulated LAR family receptor protein tyrosine phosphatases, such as PTP{sigma}, to associate with extracellular chondroitin sulphate proteoglycans (CSPGs), which have been discovered to tightly entrap the regrowing axon tip and transform it into a dystrophic non-growing endball. The scar is comprised of two compartments, one in the lesion penumbra, the glial scar, composed of reactive microglia, astrocytes and OPCs; and the other in the lesion epicenter, the fibrotic scar, which is made up of fibroblasts, pericytes, endothelial cells and inflammatory cells. While the fibrotic scar is known to be strongly inhibitory, even more so than the glial scar, the molecular determinants that curtail axon elongation through the injury core are largely uncharacterized. Here, we show that one sole member of the entire family of collagens, collagen I, creates an especially potent inducer of endball formation and regeneration failure. The inhibitory signaling is mediated by mechanosensitive ion channels and RhoA activation. Staggered systemic administration of two blood-brain barrier permeable-FDA approved drugs, aspirin and pirfenidone, reduced fibroblast incursion into the complete lesion and dramatically decreased collagen I, as well as CSPG deposition which were accompanied by axonal growth and considerable functional recovery. The anatomical substrate for robust axonal regeneration was provided by laminin producing GFAP+ and NG2+ bridging cells that spanned the wound. Our results reveal a collagen I-mechanotransduction axis that regulates axonal regrowth in spinal cord injury and raise a promising strategy for rapid clinical application.

neuroscience↗

bioRxiv2024

A deep learning-based model integrating base-pairing patterns improves microRNA target site prediction

Micro RNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional, and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa/.

bioinformatics↗