Biology subjects

Kinchagawat, J.

Publications and source records attributed to Kinchagawat, J..

3 recordsLinked to original sources

bioRxiv2026

scCody distinguishes aging-aligned from divergent disease-associated cellular remodeling

Biological aging clocks summarize molecular changes as a single age estimate, but cannot distinguish disease-associated changes that follow healthy aging from those that deviate from it. Here we introduce scCody, a generative framework that learns age-dependent cellular population dynamics from cross-sectional single-cell cohorts using population-level optimal transport and endpoint-consistency regularization. scCody accurately reconstructed cellular populations at unseen ages, recovered age-associated transcriptional changes more effectively than existing generative dynamics models, and enabled donor-age inference without direct age-regression training. Using the learned healthy aging reference, scCody decomposed disease-associated cellular displacement into aging-aligned and orthogonal components. In Alzheimer's disease, selected neuronal populations showed substantial aging-aligned remodeling alongside shared orthogonal changes associated with established pathology signatures. In contrast, COVID-19 exhibited weaker aging alignment and more pronounced orthogonal immune remodeling. Further decomposition revealed patient-level interferon variation associated with symptom duration and increased transcriptional heterogeneity in CD16 monocytes. This framework provides an extension of biological age assessment beyond a single age estimate, distinguishing cellular changes that resemble healthy aging from those that depart from it.

systems biology↗

bioRxiv2026

FeatureMAP enables network-guided interpretation of untargeted metabolomics

Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics generates thousands of features, but incomplete metabolite annotation and reliance on predefined pathways limit their biological interpretation. Here we present FeatureMAP, an LC-MS feature-centric computational framework that integrates multi-evidence metabolite annotation, network-guided refinement, metabolic module discovery, and structured large language model-based functional interpretation. Across ten public datasets, FeatureMAP improved MS1-based metabolite candidate prioritization while reducing annotation redundancy. In semi-synthetic benchmarks, FeatureMAP recovered coordinated metabolic perturbations, achieving 92% recovery of a simulated perturbation, and distinguished concurrent metabolic processes. Controlled benchmarks further evaluated functional interpretation under competing metabolite-level signals. Based on FeatureMAP, we additionally developed PhenoMAP, a phenotype-aware knowledge graph comprising 12,929 dysregulated metabolic modules from 501 human metabolomics studies spanning 326 phenotypes. Module-level comparisons revealed reproducible metabolic similarities across independent studies and biologically supported relationships among phenotypes. Application to Alzheimer's disease cerebrospinal fluid and cellular datasets identified convergent membrane-lipid alterations and implicated galactose-linked glycosphingolipid remodeling. Together, FeatureMAP and PhenoMAP enable network-grounded interpretation of incompletely annotated LC-MS metabolomics data and systematic investigation of metabolic dysregulation across biological conditions.

bioinformatics↗

bioRxiv2020

LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSLung cancer is a chronic non-communicable disease and is the cancer with the worlds highest incidence in the 21st century. One of the leading mechanisms underlying the development of lung cancer in nonsmokers is an amplification of the epidermal growth factor receptor (EGFR) gene. However, laboratories employing conventional processes of drug discovery and development for such targets encounter several pain-points that are cost- and time-consuming. Moreover, high failure rates are caused by efficacy and safety problems during research and development. Therefore, it is imperative to develop improved methods for drug discovery. Herein, we developed a deep learning model with spatial graph embedding and molecular descriptors based on predicting pIC50 potency estimates of small molecules and classifying hit compounds against the human epidermal growth factor receptor (LigEGFR). The model was generated with a large-scale cell line-based dataset containing broad lists of chemical features. ResultsLigEGFR outperformed baseline machine learning models for predicting pIC50. Our model was notable for higher performance in hit compound classification, compared to molecular docking and machine learning approaches. The proposed predictive model provides a powerful strategy that potentially helps researchers overcome major challenges in drug discovery and development processes, leading to a reduction of failure to discover novel hit compounds. AvailabilityWe provide an online prediction platform and the source code that are freely available at https://ligegfr.vistec.ist, and https://github.com/scads-biochem/LigEGFR, respectively. Key pointsO_LILigEGFR is a regression model for predicting pIC50 that was developed for the human EGFR target. It can also be applied to hit compound classification (pIC50 [≥] 6) and has a higher performance than baseline machine learning algorithms and molecular docking approaches. C_LIO_LIOur spatial graph embedding and molecular descriptors based approach notably exhibited a high performance in predicting pIC50 of small molecules against human EGFR. C_LIO_LINon-hashed and hashed molecular descriptors were revealed to have the highest predictive performance by using in a convolutional layers and a fully connected layers, respectively. C_LIO_LIOur model used a large-scale and non-redundant dataset to enhance the diversity of the small molecules. The model showed robustness and reliability, which was evaluated by y-randomization and applicability domain analysis (ADAN), respectively. C_LIO_LIWe developed a user-friendly online platform to predict pIC50 of small molecules and classify the hit compounds for the drug discovery process of the EGFR target. C_LI

bioinformatics↗