Biology subjects

Imoto, S.

Publications and source records attributed to Imoto, S..

At least 19 recordsLinked to original sources

bioRxiv2026

PHI-Reason: evidence-grounded species-level phage-host prediction from structured biological text profiles

Biological interaction inference often requires integrating heterogeneous evidence that differs in biological meaning, provenance, specificity, and reliability. Existing computational approaches typically compress such evidence into numerical features, aggregate scores or latent representations before prediction, obscuring the contribution of individual evidence records and limiting interpretation when evidence is conflicting or incomplete. Phage-host interaction prediction provides a demanding test case for this problem, requiring the integration of genomic, functional, and reference-derived evidence with differing coverage, specificity, and reliability. Here we introduce a structured-evidence inference paradigm that retains heterogeneous biological observations as modular, named, and experimentally perturbable records rather than compressing them before prediction. We implement this paradigm in PHI-Reason, which constructs structured evidence profiles from genomic annotations, receptor-binding protein homology, nucleotide-neighbour relationships, alignment-free genomic similarity, and available CRISPR spacer information. A general-purpose large language model (LLM) performs inference directly over these structured profiles without task-specific training or manually engineered evidence-fusion rules. To validate this paradigm, we evaluated it across two distinct biological interaction domains: prokaryotic phage-host prediction and eukaryotic virus-host prediction. Across phage-host benchmarks, PHI-Reason achieved species-level top-1 accuracies of 63.6% and 53.2% on RefSeq-634 and VHDB-3150, respectively, and a multi-host accuracy of 0.571 on the Hi-C cohort, outperforming established numerical methods. Besides, systematic evidence perturbations quantified how individual evidence supported, complemented, or misled inference, identifying nucleotide-neighbour context as the dominant signal. Analyses of LLM intermediate representations using a target-conditioned local Jacobian readout, together with output-grounding analyses, further characterized evidence-dependent inference and quantified the extent to which generated rationales departed from the supplied evidence profiles. Applying the same framework to eukaryotic virus-host prediction using domain-appropriate evidence also preserved high predictive accuracy (66.6%), supporting the generalizability of the proposed paradigm across biological prediction. These results demonstrate that LLMs can serve as an evidence-grounded inference interface, integrating heterogeneous biological evidence while making the boundaries of evidential support directly testable.

bioinformatics↗

bioRxiv2026

RiboPipe: efficient per-transcript codon-resolution ribo-seq coverage imputation for low-coverage transcripts

MotivationRibosome profiling (Ribo-seq) provides codon-resolution measurements of translation; however, many transcripts exhibit sparse or low read coverage, which limits downstream quantitative analyses. Reliable prediction and imputation of codon-resolution coverage for low-coverage transcripts remain computationally challenging. ResultsWe present RiboPipe, an efficient framework for per-transcript codon-resolution Ribo-seq coverage imputation for low-coverage transcripts. RiboPipe is designed around three key principles. First, it jointly optimizes transcript-level mean ribosome load (MRL) prediction and codon-level coverage modeling within a unified objective, enabling consistent learning across both local and transcript-level scales. Second, it introduces a peak-weighted loss that emphasizes high-signal codon positions associated with translational pausing, improving the recovery of functionally relevant coverage peaks. Third, the framework is lightweight and data-efficient, achieving stable performance even when trained on only a small fraction of high-coverage transcripts. Based on two publicly available Ribo-seq datasets (GSE233886 and GSE133393) as instances, we demonstrate stable convergence and consistent prediction accuracy across multiple train-test split ratios. Comparative evaluation of embedding strategies shows that simple one-hot representations achieve superior performance compared with pre-trained language model embeddings under identical training conditions. Overall, RiboPipe provides a computationally efficient and scalable framework for Ribo-seq coverage imputation in low-coverage transcripts. Availability and ImplementationThe source code and associated data can be accessed at https://github.com/yaozhong/riboPipe Contactyaozhong@ims.u-tokyo.ac.jp or imoto@hgc.jp

bioinformatics↗

bioRxiv2025

Phylogenetic tree-aware positive-unlabeled deep metric learning for phage-host interaction identification

Phages are viruses that infect bacteria and play essential roles in shaping microbial communities. Identifying phage-host interactions (PHIs) is crucial for understanding infection dynamics and developing phage-based therapeutic strategies. Recent deep learning approaches have shown great promise for PHI prediction; however, their performance remains constrained by the limited number of experimentally validated positive pairs and the overwhelming abundance of unlabeled or non-validated samples. Moreover, most existing models overlook higher-level phylogenetic relationships among hosts, which could provide valuable structural priors for guiding representation learning. To address these challenges, we propose a phylogenetic tree-aware positive-unlabeled deep metric learning framework for phage-host interaction (PHI) identification. Unlike traditional approaches that train classification models to strictly separate positive and negative phage-host pairs, the proposed method learns representations under supervision from both confirmed positive PHIs and host phylogenetic tree constraints on non-positive samples. The proposed method can seamlessly formalize contrastive learning and deep metric learning within the same framework that explicitly optimizes PHI encoders with biological constraints in the learning functions. We show that this metric learning formulation outperforms conventional contrastive learning approaches that enforce separation between positive and negative samples without consistently aligning the learned representations with evolutionary distances. Experiments on the Cherry benchmark dataset and metagenome Hi-C multi-host dataset demonstrate that our approach enhances species-level prediction accuracy, improves cross-host generalization, and yields more interpretable representations of phage-host relationships.

bioinformatics↗

bioRxiv2025

Causal assessment of gene regulatory network in single-cell transcriptomics data based on Bayesian networks

Gene regulatory network (GRN) inference is an essential tool for revealing dysregulated relationships between genes in different cell types from single-cell transcriptomic (SCT) data. GRNs based on Bayesian networks (BNs) learned from SCT data can elucidate directed regulatory relationships representing complex disease mechanisms and their interplay through graphical modeling. However, software for learning BNs from SCT data is not widely available, nor is evaluating the BNs structural accuracy in representing causal relationships between genes. Here, we describe the scstruc R package. This package provides a suite of BN structure learning algorithms specifically designed for handling SCT data; evaluating the resulting networks based on the causal relationships they represent regardless of the availability of established molecular interaction networks; and comparing regulatory relationships between conditions. We demonstrated that scstruc can identify biologically relevant differential regulatory relationships between groups on a per-cell basis. The package is available at https://github.com/noriakis/scstruc.

bioinformatics↗

bioRxiv2025

Rethinking Representation Complexity in Drug-Target Prediction via Supervised Vector Quantization

Accurate prediction of drug-target interactions (DTIs) is crucial for computational drug discovery. Although pretrained language models offer richer molecular and protein representations, their increasing complexity does not always lead to better predictive performance. In many cases, the inclusion of redundant or irrelevant features may obscure biologically relevant patterns. In this study, we systematically evaluate the contribution of complex features in DTI prediction and demonstrate that only a portion of these features is truly informative. Based on this insight, we propose a Vector Quantization (VQ)-based module that functions as a plug-and-play feature selection layer within deep learning architectures. When combined with a simple fully connected classifier, this supervised VQ (SVQ) framework not only surpasses recent state-of-the-art DTI methods in performance, but also enhances interpretability through the learning of discriminative codewords. This work highlights the importance of input feature selection in deep learning and offers a new perspective for constructing robust and interpretable DTI prediction models.

bioinformatics↗

bioRxiv2025

Simulating The Effect of the Radiotherapy on Cancer Stem Cell Evolution

Radiotherapy is one of the most effective treatments for cancer. However, the radiation on cancer often leads to further tumor evolution and resistance. Accumulating evidence suggests that the cancer stem cells (CSCs) play a significant role in the tumors radioresistance and invasiveness. Therefore, we set out to investigate the aggressiveness and radioresistance that emerge because of radiation-induced selective pressures on CSCs. We developed an agent-based model (ABM) that tracks the evolution and growth of the cancer after treated by different regimens of radiotherapy. In the ABM, each cell is an agent that follows some predefined rules for their behaviors and interactions with other agents. We showed that the radiotherapy treatment increased the resistance, proliferation rate, and the CSC proportion of the tumor. Our simulation also showed that the CSC-targeted treatment is important for eliminating tumor completely.

cancer biology↗

bioRxiv2024

Tissue-Specific Cell Type Annotation with Supervised Representation Learning using Split Vector Quantization and Its Comparisons with Single-cell Foundation Models

Cell-type annotation in single-cell data involves identifying and labeling the cell types based on their gene expression profiles or molecular features. Recently, with advances in single-cell foundation models (FMs), unsupervised annotation and transfer learning with FMs have been explored for cell-type annotation tasks. However, because FMs are usually pre-trained in an unsupervised manner on data spanning a wide variety of tissues and cell types, their representations for specific tissues may lack specificity and become overly generalized. In this work, we propose a novel supervised representation learning method using split-vector-quantization, single-cell Vector-Quantization Classifier (scVQC). We evaluated scVQC against both supervised and unsupervised representation learning approaches, with a focus on foundation models pretrained on large-scale single-cell datasets, such as scBERT and scGPT. The experimental results highlight the importance of label supervision in cell-type annotation tasks and demonstrate that the learned codebook effectively profiles and distinguishes different cell types.

bioinformatics↗

bioRxiv2024

Weakly Supervised Vector Quantization for Whole Slide Images Classification

Whole Slide Images (WSIs) are gigapixel, high-resolution digital scans of microscope slides, providing detailed tissue profiles for pathological analysis. Due to their gigapixel size and lack of detailed annotations, Multiple Instance Learning (MIL) becomes the primary technique for WSI analysis. However, current MIL methods for WSIs directly use embeddings extracted by a pretrained vision encoder, which are not task-specific and often exhibit high variability. To address this, we introduce a novel method, VQ-MIL, which maps the embeddings to a discrete space using weakly supervised vector quantization to refine the embeddings and reduce the variability. Additionally, the discrete embeddings from our methods provides clearer visualizations compared to other methods. Our experiments show that VQ-MIL achieves state-of-the-art classification results on two benchmark datasets. The source code is available at https://github.com/aCoalBall/VQMIL.

pathology↗

bioRxiv2024

Enhanced spatially resolved transcriptomics analysis by matching between expression profiles and spatial topology

Spatially resolved transcriptomics (SRT) quantifies gene expression covering contextual heterogeneity for the tissue section. Exploratory data analysis using SRT data sheds light on diverse biomedical research fields. We propose STForte, a pairwise graph autoencoding-based approach for SRT data analysis, which is capable of matching the information between expression profiles and spatial topology in the latent space. STForte benefits from the designed framework to provide encodings with justifiable spatial correlations for the down-stream analysis of both homogeneous and heterogeneous SRT data. Moreover, STForte can unravel the biological patterns of unobserved locations or recover deficient measurements to enable spatial enhancement. Latent encodings generated by STForte can be used to perform spatial region identification or other downstream tasks to gain biological insights and elucidate biological processes. In this work, we presented various analyses to show that STForte is scalable for analyzing SRT data under different scenarios with considerable performance.

bioinformatics↗

bioRxiv2024

PharaCon: A new framework for identifying bacteriophages via conditional representation learning

MotivationIdentifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples. ResultsTo overcome this limitation, this study proposed a novel conditional BERT framework that incorporates labels during pre-training. We developed an approach using a conditional BERT model for pre-training labeled data, incorporating label constraints with modified language modeling tasks. This approach allows the model to acquire label-conditional sequence representations. Additionally, we proposed a solution that utilizes conditional BERT in the fine-tuning phase as a classifier. We applied this conditional BERT framework to identify phages using a novel fine-tuning strategy, introducing PharaCon. We evaluated PharaCon against several existing methods on both simulated sequence datasets and real metagenomic contig datasets. The results demonstrate PharaCon's potential as an effective and efficient method for phage identification, highlighting the effectiveness of conditional B ERT as a solution for learning label-specific representations during pre-training on mixed sequence data. AvailabilityThe codes of PharaCon are now available in: https://github.com/Celestial-Bai/PharaCon. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp

bioinformatics↗

bioRxiv2023

Predicting cell types with supervised contrastive learning on cells and their types

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique that provides high-resolution expression profiling of individual cells. It significantly advances our understanding of cellular diversity and function. Despite its potential, the analysis of scRNA-seq data poses considerable challenges relate to multicollinearity, data imbalance, and batch effect. One of the pivotal tasks in single-cell data analysis is cell type annotation, which classifies cells into discrete types based on their gene expression profiles. In this work, we propose a novel modeling formalism for cell type annotation with a supervised contrastive learning method, named SCLSC (Supervised Contrastive Learning for Single Cell). Different from the previous usage of contrastive learning in single cell data analysis, we employed the contrastive learning for instance-type pairs instead of instance-instance pairs. More specifically, in the cell type annotation task, the contrastive learning is applied to learn cell and cell type representation that render cells of the same type to be clustered in the new embedding space. Through this approach, the knowledge derived from annotated cells is transferred to the feature representation for scRNA-seq data. The whole training process becomes more efficient when conducting contrastive learning for cell and their types. Our experiment results demonstrate that the proposed SCLSC method consistently achieves superior accuracy in predicting cell types compared to four state-of-the-art methods. SCLSC also performs well in identifying cell types in different batch groups. The simplicity of our method allows for scalability, making it suitable for analyzing datasets with a large number of cells. In a real-world application of SCLSC to monitor the dynamics of immune cell subpopulations over time, SCLSC demonstrates a capability to discriminate cell subtypes of CD19+ B cells that were not present in the training dataset.

bioinformatics↗

bioRxiv2022

Dysfunctional analysis of the pre-training model on nucleotide sequences and the evaluation of different k-mer embeddings

Pre-training has attracted much attention in recent years. Although significant performance improvements have been achieved in many downstream tasks using pre-training, the mechanism of how a pre-training method works for downstream tasks is not fully illustrated. In this work, focusing on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into embedding and encoding modules to illustrate what a pre-trained model learns from pre-training data. Through dysfunctional analysis on both data and model level, we demonstrate that the context-consistent k-mer representation is the primary product that a typical BERT model learns in the embedding layer. Surprisingly, single usage of the k-mer embedding pre-trained on the random data can achieve comparable performance to that of the k-mer embedding pre-trained on actual biological sequences. We further compare the learned k-mer embeddings with other commonly used k-mer representations in downstream tasks of sequence-based functional predictions and propose a novel solution to accelerate the pre-training process. Contactyaozhong@ims.u-tokyo.ac.jp or imoto@hgc.jp Supplementary informationThe source code and relevant data are available at https://github.com/yaozhong/bert_investigation.

bioinformatics↗

bioRxiv2022

Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases

BackgroundThe human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. ResultsWe use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. ConclusionsThe discovered candidates important microbiome genes and their functions provide novel insights into microbe functional contribution.

bioinformatics↗

bioRxiv2021

Identification of Bacteriophages Using Deep Representation Model with Pre-training

MotivationBacteriophages/Phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. ResultsWe propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AvailabilityThe codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2021

Independent adoptions of a set of proteins found in the matrix of the mineralized shell-like eggcase of Argonaut octopuses

The Argonaut octopus, commonly called the paper nautilus, has a spiral-coiled shell-like eggcase. As the main characteristics, the eggcase has no internal septum, is composed entirely of calcite with chitosan being the main polycarbonate and is reportedly formed by organic materials secreted from the membranes of the arms. Meanwhile, the biomineralized external "true" shells of the Mollusks, which includes the Cephalopods, are secreted from the mantle tissue. Therefore, the histological origin of the two shells is completely different. The question of how the Argonauts, which phylogenetically diverged from the completely shell-less octopuses, could form a converging shell-like external structure has thus intrigued biologists for a long time. To answer this question, we performed a multi-omics analysis of the transcriptome and proteome of the two congeneric Argonaut species, Argonauta argo and A. hians. Our result indicates that the shell-like eggcase is not a homolog of the shell, even at the protein level, because the Argonauts apparently recruited a different set of protein repertoires to as eggcase matrix proteins (EcMPs). However, we also found the homologs of three shell matrix proteins (SMPs) of the Conchiferan Mollusks, Pif-like, SOD, and TRX, in the eggcase matrix. The proteins were also found in the only surviving shelled Cephalopods, the nautiloid Nautilus pompilius. Phylogenetic analysis revealed that homologous genes of the Conchiferan SMPs and EcMPs were found in the draft genome of shell-less octopuses. Our result reported here thus suggests that the SMP-coding genes are conserved in both shelled and shell-less Cephalopods. Meanwhile, the Argonauts adopted some of the SMP-coding genes and other non-SMP-coding genes, to form a convergent, non-homologous biomineralized external structure, the eggcase, which is autapomorphic to the group.

evolutionary biology↗

bioRxiv2021

Combined landscape of single-nucleotide variants and copy-number alterations in clonal hematopoiesis

Implicated in the development of hematological malignancies (HM) and cardiovascular mortality, clonal hematopoiesis (CH) in apparently healthy individuals has been investigated by detecting either single-nucleotide variants and indels (SNVs/indels) or copy number alterations (CNAs), but not both. Here by combining targeted sequencing of 23 CH-related genes and array-based CNA detection of blood-derived DNA, we have delineated the landscape of CH-related SNVs/indels and CNAs in a general population of 11,234 individuals, including 672 with subsequent HM development. Both CH-related lesions significantly co-occurred, which combined, affected blood count, hypertension, and the mortality from HM and cardiovascular diseases depending on the total number of both lesions, highlighting the importance of detecting both lesions in the evaluation of CH.

cancer biology↗

bioRxiv2021

On the application of BERT models for nanopore methylation detection

MotivationDNA methylation is a common epigenetic modification, which is widely associated with various biological processes, such as gene expression, aging, and disease. Nanopore sequencing provides a promising methylation detection approach through monitoring abnormal signal shifts for detecting modified bases in target motif regions. Recently, model-based approaches, especially those with deep learning models, have achieved significant performance improvements on nanopore methylation detection. In this work, we explore using bidirectional encoder representations from transformers (BERT) for doing the task, which can provide non-recurrent neural structures for fast parallel computation. ResultsWe find original BERT architecture does not work as well as the bidirectional recurrent neural network (biRNN) on the nanopore methylation prediction task. Through further analysis, we observe recurrent patterns of positional-signal-shift in the context window surrounding target 5-methylcytosine (5mC) and N6-methyladenine (6mA) motifs. We propose a refined BERT with relative position representation and center hidden units concatenation, which takes account of task-specific characters into modeling. We perform systematic evaluations in-sample and cross-sample. The experiment results show that the refined BERT model can achieve competitive or even better results than the state-of-the-art biRNN model, while the model inference speed is about 6x faster. Besides, on the cross-sample evaluation of datasets from the different research groups, BERT models demonstrate a good generalization performance. AvailabilityThe source code and data are available at https://github.com/yaozhong/methBERT Contactyaozhong@ims.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2019

A multifunctional R package for identification of tumor-specific neoantigens

It is known that some mutated peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T-cells on the surface of a tumor cell. These peptides are termed neoantigen and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions, and structural variants. Beyond the existing applications, this package is capable of attaching and reflecting several additional information, e.g., wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information, and combinations of mutations to filter out infeasible peptides as neoantigen. AvailabilityThe R package is available at http://github/hase62/Neoantimon.

bioinformatics↗