Biology subjects

Serra, A.

Publications and source records attributed to Serra, A..

17 recordsLinked to original sources

bioRxiv2026

Integrating Semantic Retrieval, LLM-based Refinement, and Structured Expert Curation for Scalable AOP Gene Mapping

Toxicogenomics can support regulatory toxicology, but its use is limited by the difficulty of translating molecular responses into mechanistic, decision-relevant interpretations. Adverse Outcome Pathways (AOPs) provide a framework for this translation, yet omics applications require scalable mapping of Key Events (KEs) to molecular features. Here, we present an AI-assisted, multi-step workflow for KE-to-gene mapping that uses embedding-based semantic retrieval to identify candidate ontology/pathway terms, large language model-assisted refinement to filter these candidates, and double-independent expert group curation with rule-based consolidation to finalize mappings and derive confidence scores. Compared with earlier NLP-based approaches, the workflow improves KE-to-ontology/pathway mapping performance and generates candidate annotations that better align with expert judgment while substantially reducing the need for manual augmentation. Explicit gene and protein mentions in KE titles were additionally grounded to improve specificity, and each curated mapping was assigned curator reason codes to support transparent, traceable, and confidence-aware reuse. Applied across AOP-Wiki, the workflow produced a comprehensive KE-to-gene set resource covering 1,254 KEs across 523 AOPs and linking 15,833 human genes. Utility is demonstrated through CTD-based AOP fingerprinting of curated reference chemical groups, highlighting expanded coverage and confidence-informed interpretation of chemical-associated gene signatures in an AOP context. The workflow and resulting resource provide a practical bridge between toxicogenomics and AOP-based mechanistic interpretation and support routine updating and future extension to additional omics layers within OECD Omics2AOP.

bioinformatics↗

bioRxiv2026

Natural microbial exposure imposes layered constraints on epithelial and type 2 immunity

The tuft cell-ILC2 amplification circuit has emerged as a central paradigm of anti-helminth immunity in laboratory animals, driving the so-called weep and sweep response, required for parasite expulsion. Yet soil-transmitted helminths (STHs) commonly establish chronic infections in humans. Whether tuft cell expansion is required for parasite clearance under naturalistic conditions remains unknown. Here, using wildlings, a naturalized mouse model exposed from birth to complex microbial communities and pathogens, we re-investigated anti-STH immunity in the context of ecological realism. Following infection with Nippostrongylus brasiliensis, specific pathogen-free (SPF) mice mounted markedly amplified type 2 responses in both lung and intestine compared to wildlings. In the intestine, SPF mice mounted robust tuft cell expansion, IL-25 production, ILC2 accumulation, and goblet cell hyperplasia. In contrast, infected wildlings exhibited delayed parasite expulsion, limited tuft cell expansion, reduced IL-25 and ILC2 responses, and attenuated goblet cell expansion. Wildling tuft cells, but not goblet cells, displayed markedly reduced expansion in response to succinate or exogenous IL-13, indicating selective hypo-responsiveness of the epithelial sensory compartment. Microbial transfer into adult SPF mice selectively conferred tuft cell hypo-responsiveness without impairing ILC2 accumulation or goblet cell expansion. Tuft cell hypo-responsiveness in wildlings and FMT recipients was associated with enrichment of fermentative bacteria and increased levels of the short chain fatty acids acetate and propionate. Together, these findings indicate that ecological microbial exposure imprints systemic type 2 immunity during early-life, whereas epithelial responsiveness remains plastic and microbiome-dependent, thereby revealing regulatory constraints not evident under SPF conditions. One Sentence SummaryOur findings reveal that in a naturalized immune-microbiome context, intestinal tuft cells are surprisingly hypo-responsive, highlighting how environmental microbial exposure can calibrate type 2 immunity and helminth resistance.

immunology↗

bioRxiv2025

OpTiles: An R Package for Adaptive Tiling and Methylation Variability Profiling

SummaryOpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AvailabilityOpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961293 Contactdario.greco@tuni.fi

bioinformatics↗

bioRxiv2025

Anti-isoDGR Antibody Inhibits Atherosclerosis Induced by Western Diet in ApoE-/- mice

BackgroundDegenerative protein modifications (DPMs) accumulate with aging and can alter biomolecule structure and function, including via spontaneous conversion of Asn-Gly-Arg (NGR) to isoAsp-Gly-Arg (isoDGR) motifs that can bind integrins and drive chronic inflammation. Since isoDGR-modified extracellular matrix proteins are enriched in atherosclerosis and have been associated with rupture-prone plaque characteristics, we hypothesized that antibody neutralisation can inhibit key pathological features including atherosclerotic vascular plaque formation and metabolic dysfunction. MethodsWe first examined Pcmt1-/- mice which rapidly accumulate isoDGR due to lack of the corresponding repair enzyme to assess the extent of vascular protein damage. We then treated 6-8 week old atherosclerosis-prone (ApoE-/-) mice which were fed a high-fat Western diet (WD) with weekly dose of 1mg/kg isoDGR-specific monoclonal antibody (isoDGR-mAb) or isotype-matched control (while on diet) for 2 months duration. A regular chow-fed ApoE-/- group served as baseline control. Aortic atherosclerotic burden, plaque composition, systemic inflammation, lipid profiles, hepatic steatosis, and metabolic parameters (indirect calorimetry) were assessed. ResultsPcmt1-/- mice displayed extensive isoDGR deposition and degeneration of the aortic wall, linking this DPM to vascular structural damage. In the ApoE-/- mice, WD induced large aortic root plaques with abundant isoDGR and macrophage infiltration. IsoDGR-mAb treatment decreased plaque size by [~]30% with reduced lipid and collagen content (p=0.001). Furthermore, plaques in treated mice contained significantly fewer CD68+ macrophages that also exhibited limited activation. Systemically, isoDGR-mAb modified lipoprotein profiles by decreasing atherogenic VLDL/IDL/LDL cholesterol (p=0.04) while slightly increasing HDL, accompanied by a reduction in circulating inflammatory proteins. IsoDGR-mAb also protected against hepatic lipid accumulation which was reduced by [~]60% in treated animals (p<0.001), with indirect calorimetry confirming [~]30% higher oxygen consumption and energy expenditure without change in food intake or physical activity. ConclusionWe identified isoDGR as a key pathological factor involved in the progression of atherosclerosis. Remarkably, isoDGR neutralization diminished plaque inflammation and improved atherosclerotic plaque stability. Our findings support isoDGR neutralization as a promising therapeutic strategy to mitigate both atherosclerosis and aging-associated metabolic dysfunction.

pharmacology and toxicology↗

bioRxiv2025

Curated and harmonised transcriptomics datasets of interstitial lung diseases

This study provides manually curated and homogenised transcriptomics data of interstitial lung disease (ILD) patients retrieved from the NCBI Gene Expression Omnibus and European Nucleotide Archive repositories. The compendium includes 30 transcriptomics datasets generated with DNA microarrays and RNA sequencing technologies for a total of 1,371 samples. All the datasets underwent metadata curation and harmonisation, data quality check, and preprocessing with standardised procedures. Furthermore, a robust data model was developed to standardise phenotypic data, thereby enhancing comparability across heterogeneous datasets. Gene expression data and lists of differentially expressed genes computed between ILD and healthy samples are provided. Among the ILDs included in this study, idiopathic pulmonary fibrosis (IPF) is the most represented worldwide. Co-expression networks of IPF and healthy samples were inferred, which are also included in this study. This work significantly improves the Findability, Accessibility, Interoperability, and Reusability (FAIR) of publicly available transcriptomics data of ILDs, providing a platform to implement and validate integrated systems biology and pharmacology approaches for novel interstitial lung disease diagnostics and therapeutics.

bioinformatics↗

bioRxiv2025

MUUMI: an R package for statistical and network-based meta-analysis for MUlti-omics data Integration

Disentangling physiopathological mechanisms of biological systems through high-level integration of omics data has become a standard procedure in life sciences. However, platform heterogeneity, batch effects, and the lack of unified methods for single- and multi-omics analyses represent relevant drawbacks that hinder the extrapolation of a meaningful biological interpretation. Statistical meta-analysis is widely used in order to integrate several omics datasets of the same type, leading to the extrapolation of robust molecular signatures within the investigated system. Conversely, statistical meta-analysis does not allow the simultaneous investigation of different molecular layers, and, therefore, the integration of multi-modal data deriving from multi-omics experiments. Although in the last few years a number of valid tools designed for multi-omics data integration have emerged, they have never been combined with statistical meta-analysis tools in a unique analytical solution in order to support meaningful biological interpretation. Network science is at the forefront of systems biology, where the inference of molecular interactomes allowed the investigation of perturbed biological systems, by shedding light on the disrupted relationships that keep the homeostasis of complex systems. Here, we present MUUMI, an R package that unifies network-based data integration and statistical meta-analysis within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytic methods, inference and analysis of molecular interactomes and the integration of multiple omics layers through similarity network fusion. We demonstrate the functionalities of MUUMI by presenting two case studies in which we analysed 1) 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and 2) multi-omics data of THP-1 macrophages exposed to different polarising stimuli. In both examples, MUUMI revealed biologically coherent signatures, underscoring its value in elucidating complex biological processes. Availability and implementationMUUMI is freely available at https://github.com/fhaive/muumi.

bioinformatics↗

bioRxiv2025

Endothelial Sensitivity to Pro-Fibrotic Signals Links Systemic exposure to Pulmonary Fibrosis

Pulmonary fibrosis (PF) is a life-threatening condition characterised by excessive extracellular matrix deposition and tissue scarring. While much of PF research has focused on alveolar epithelial cells and fibroblasts, endothelial cells have emerged as active contributors to the disease initiation, especially in the context of systemic exposure to pro-fibrotic substances. Here, we investigate early transcriptomic and secretory responses of human umbilical vein endothelial cells (HUVEC) to subtoxic doses of bleomycin, a known pro-fibrotic agent, and TGF-beta, a key cytokine in fibrosis. Bleomycin exposure induced a rapid and extensive shift in the endothelial transcriptional programme, including signatures of endothelial to mesenchymal transition, cellular senescence, and immune cell recruitment. These findings suggest endothelial cells as early initiators of pro-fibrotic signals, independent of contributions from other cell types. In contrast, TGF-beta effects were limited and transient, indicating its pro-fibrotic action may require another initial stimulus and interplay with other cells like fibroblasts. This study highlights the sensitivity of endothelial cells to systemic pro-fibrotic exposure and provides a blueprint of early pro-fibrotic mechanisms, emphasising their pivotal role in PF pathogenesis.

molecular biology↗

bioRxiv2024

Toxicogenomic Assessment of In Vitro Macrophages Exposed to Profibrotic Challenge.

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, providing information on mechanistic immune responses, are underdeveloped. This study utilises a comprehensive analysis of RNA sequencing data from macrophages exposed in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints. Using a toxicogenomic framework, we performed dose-dependent analysis to filter genes truly altered by bleomycin exposure from noise and identified a subset of immune genes with a sustained dose-dependent and differential expression response to profibrotic challenge. We performed an immunoassay and revealed cytokines and proteinases responding to bleomycin exposure that closely correlate to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through implementation of a time and resource efficient in vitro methodology.

pharmacology and toxicology↗

bioRxiv2024

Methylation and Transcriptomic Profiling Reveals Short Term and Long Term Regulatory Responses in Polarized Macrophages

Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Moreover, epigenetic alterations play an important role in the pathophysiology of macrophages, including phenomena related to the trained innate immunity, which allow faster and more efficient inflammatory responses upon subsequent encounters with the same pathogen. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, unravelling the long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure suggesting a potential mechanism for rapid adaptation to various stimuli. These conserved epigenetic modifications might represent a fundamental state that allows for flexible responses to various environmental cues. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study unveils a novel set of genes critically linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes. Highlights- Environmental signals trigger gene changes in macrophages, leaving a long-lasting epigenetic reprogramming - Epigenetic changes and metabolic shifts in polarized macrophages suggest training mechanisms - Common gene set epigenetically altered across different cues, suggest common adaptation to various stimuli Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/599278v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1006cacorg.highwire.dtl.DTLVardef@deb49eorg.highwire.dtl.DTLVardef@12428e9org.highwire.dtl.DTLVardef@f9e1c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering is used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes and Alzheimer disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

bioinformatics↗

bioRxiv2024

A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasises how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, we developed a network-based approach to characterise toxicological responses in the context of a biological system, inferring biological system specific networks. We directly linked molecular alterations to the adverse outcome pathway (AOP) framework, establishing connections with toxicologically relevant phenotypic events. We applied this framework on a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrated how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve our understanding of toxicological mechanisms from a systems biology perspective, guiding the hazard assessment of nanomaterials and other advanced materials.

pharmacology and toxicology↗

bioRxiv2022

Substrate-binding Glycine Residues are Major Determinants for Hydrolase and Ligase Activity of Plant Legumains

Peptide asparaginyl ligases (PALs) are Asn/Asp(Asx)-specific ligases that are useful for precision modifications of proteins and live-cell surfaces. However, PALs share high structural similarity to the far more common asparaginyl endopeptidases (AEPs), also known as legumains that hydrolyze peptide bonds after Asx, thus making it challenging to identify PALs in a sea of AEPs. Previously we identified sequences flanking the catalytic site as ligase activity determinants (LADs) for legumains. Here we show that two conserved substrate-binding Gly residues are critical, but negative determinants for ligase activity, based on a combined bioinformatics analysis of 1,500 plant legumains, mutagenesis and functional study of 16 novel legumains, plus identification of seven new PALs. We also show that PALs are rare and AEPs are much more common, accounting for about 1% and 88%, respectively. Our results suggest that specific glycine residues are molecular determinants to identify PALs and AEPs as two different legumain subfamilies.

plant biology↗

bioRxiv2022

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Mechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established. Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derived in vitro biomarkers for pulmonary fibrosis.

pharmacology and toxicology↗

bioRxiv2021

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

New affordable therapeutic protocols for COVID-19 are urgently needed despite the increasing number of effective vaccines and monoclonal antibodies. To this end, there is increasing attention towards computational methods for drug repositioning and de novo drug design. Here, we systematically integrated multiple data-driven computational approaches to perform virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the set of prioritized drugs, we selected a subset of representative candidates to test in human cells. Two compounds, 7-hydroxystaurosporine and bafetinib, showed synergistic antiviral effects in our in vitro experiments, and strongly inhibited viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, we extracted and prioritized the chemical substructures of the identified drugs, providing a chemical vocabulary that may help to design new effective drugs.

pharmacology and toxicology↗

bioRxiv2020

Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground, and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.

neuroscience↗

bioRxiv2018

eUTOPIA: Solution for omics data preprocessing and analysis

Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R that have become standard for high-throughput analytics. However, there is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results, which is computational skill neutral. We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods. eUTOPIA is free for academic use and can be obtained from the GitHub repository https://github.com/Greco-Lab/eUTOPIA.

bioinformatics↗

bioRxiv2017

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

IntroductionGlioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge.\n\nMethodsA total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS).\n\nResultsThe MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013). Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region.\n\nDiscussionThis study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.

bioinformatics↗