Biology subjects

Yada, Y.

Publications and source records attributed to Yada, Y..

7 recordsLinked to original sources

bioRxiv2026

Monju: Multi-criteria clustering in single-cell omics

Clustering is a fundamental step in single-cell omics analysis. Although single-cell omics data can, in principle, be partitioned according to multiple biologically meaningful criteria, existing methods typically cluster cells using a single criterion. To address this problem, we developed Monju, a multi-criteria clustering method based on a deep generative mixture model. Monju divides cells into biologically reasonable submodels, each of which is equipped with an interpretable latent space. Furthermore, although the partitioning of cells into submodels varies across random seeds, each solution remains biologically plausible, collectively yielding multi-criteria clustering. Moreover, by integrating these multiple clustering solutions to perform meta-clustering, Monju enables the assessment of cluster stability. We applied Monju to human peripheral blood CITE-seq data and demonstrated that it can achieve multi-criteria clustering. Monju therefore provides a powerful and practical framework for dissecting cellular heterogeneity from multiple biological perspectives.

bioinformatics↗

bioRxiv2026

hB-PAC: A non-invasive aging clock for quantifying individual differences in aging

Quantifying differences in aging among individuals of the same chronological age could provide a direct measure of biological aging. However, existing methods often estimate biological age by predicting chronological age and treat such individual differences as prediction errors. We developed InferAging, a framework that estimates biological age by explicitly modeling individual deviations from chronological age. In progeroid zebrafish (klotho mutant; kl-/-), InferAging identified accelerated and delayed agers within the same chronological age group. A non-invasive variant using behavioral and morphological snapshots reproduced the transcriptome-integrated estimates without molecular input or lifelong tracking. The inferred aging state was associated with metabolic decline, intestinal barrier dysfunction, inflammation, and mucosal immune abnormalities beyond chronological age. These results demonstrate that non-invasive phenotypes can reveal molecularly supported individual aging states.

systems biology↗

bioRxiv2026

The mental conflict in risk-taking behavior: Decoding bias between optimism and pessimism

Humans and animals often face risky situations that require decision-making. Such decisions can be high-risk, high-return at some times, and low-risk, low-return at other times, depending on the balance between optimism and pessimism. However, how this optimism-pessimism bias is regulated across contexts remains unclear. Here, we introduced a computational model of decision-making in a risk-taking task based on the free-energy principle, together with a machine-learning framework that inversely estimates cognitive updating and optimism-pessimism bias from behavioral data. Applying this framework to monkey behavioral data, we found that a monkey quickly and accurately recognized the degree of risk, while frequently switching between optimism and pessimism during the task. In addition, we identified a characteristic control rule for optimism-pessimism bias that is distinct from reward-dependent regulation. Our framework provided a principled tool for understanding the latent cognitive processes underlying risky decision-making in animals and humans.

neuroscience↗

bioRxiv2025

Sperrfy the brain: A data-driven realization of Sperry's Chemoaffinity theory in the neural connectome

Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperrys chemoaffinity theory posits that molecular gradients provide positional cues for axonal projections, its application has been largely limited to localized sensory systems. Here, we present SPERRFY (Spatial Positional Encoding for Reconstructing Rules of axonal Fiber connectivitY), a data-driven framework that operationalizes Sperrys theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY deciphers latent positional gradients that underlie axonal wiring. Using canonical correlation analysis (CCA), we extract top gradient pairs that align with observed neural connectivity patterns, capturing both global (inter-regional) and local (intra-regional) organizational principles. Connectivity reconstruction based on these gradients achieves high predictive performance (AUC = 0.90), and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY identifies candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperrys foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

neuroscience↗

bioRxiv2023

Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers

Understanding the decision-making mechanisms underlying trust is essential, particularly for patients with mental disorders who experience difficulties in developing trust. We aimed to explore biomarkers associated with trust-based decision-making by quantitative analysis. However, quantification of decision-making properties is difficult because it cannot be directly observed. Here, we developed a machine learning method based on Bayesian hierarchical model to quantitatively decode the decision-making properties from behavioral data of a trust game. By applying the method to data of patients with MDD and healthy controls, we estimated model parameters regulating trusting decision-making. The estimated model was able to predict behaviors of each participant. Although there is no difference of the estimated parameters between MDD and healthy controls, several biomarkers were associated with the decision-making properties in trusting behavior. Our findings provide valuable insights into the trusting decision-making, offering a basis for developing targeted interventions to improve their social functioning and overall well-being.

bioinformatics↗

bioRxiv2023

Chronic BCR signaling generates and maintains age-associated B cells from anergic B cells

Accumulation of age-associated B cells (ABCs) with autoreactive properties contributes to the pathogenesis of autoimmune diseases1-5. However, the mechanisms whereby ABCs are generated and maintained are not understood1, 2, 4. Here, we show that continuous stimulation of the B-cell receptor (BCR) with self-antigens plays a crucial role in ABC generation from anergic B cells and that this signal is vital for sustaining ABCs during aging and autoimmunity. In ABCs, BCR signaling was constitutively activated and the surface BCR was internalized in vivo, as occurs in autoreactive B cells chronically exposed to self-antigens6. With aging, ABCs were generated from autoreactive anergic B cells, but not from B cells expressing non-self-reactive BCR. In vitro stimulation of anergic B cells with self-antigen, interleukin-21, and Toll-like receptor 7/9 agonists promoted their differentiation to ABCs. Furthermore, the cellular phenotype of ABCs in Bm12-induced lupus mice7, 8 resembled that of ABCs in aged mice, showing activation of BCR signaling, expression of activation markers, and BCR internalization. Importantly, Btk was persistently activated in ABCs of aged/autoimmune mice and humans with lupus. Pharmacological Btk inhibition resulted in a marked reduction in the number of ABCs and pathogenicity in lupus mice. Our findings have implications for accumulating ABCs and developing therapies for autoimmune diseases.

immunology↗

bioRxiv2022

Prediction of amyloid β accumulation from multiple biomarkers using a hierarchical Bayesian model

Accumulation of amyloid-beta (A{beta}) in the brain is associated with neurodegeneration in Alzheimers disease and can be an indicator of early disease progression. Thus, the non-invasively and inexpensively observable features related to A{beta} accumulation are promising biomarkers. However, in the experimental discovery of biomarkers in preclinical models, A{beta} and biomarker candidates are usually not observed in identical sample populations. This study established a hierarchical Bayesian model that predicts A{beta} accumulation level solely from biomarker candidates by integrating incomplete information. The model was applied to 5xFAD mouse behavioral experimental data. The predicted A{beta} accumulation level obeyed the observed amount of A{beta} when multiple features were used for learning and prediction. Based on the evaluation of predictability, the results suggest that the proposed model can contribute to discovering novel biomarkers, that is, multivariate biomarkers relevant to the accumulation state of abnormal proteins.

neuroscience↗