Frontiers in Bioinformatics

Motivation: Comparative studies of trajectory inference (TI) methods evaluate complete computational pipelines, making it impossible to isolate how much distortion is introduced specifically by the dimensionality reduction (DR) step. To our knowledge, no study has directly and systematically evaluated how well DR methods alone preserve a known reference path when projecting high-dimensional single-cell data to two dimensions, and no current study has introduced a dedicated set of metrics to quantify the degree of path-preservation quality after dimensionality reduction. This gap matters because DR is a universal preprocessing choice that shapes all downstream trajectory analysis, yet its independent geometric effect on path structure remains uncharacterized, and practitioners have no principled way to quantify it. Methods: We tested a panel of candidate path-preservation metrics on two single-cell datasets with known reference trajectories, one linear and one cyclic, to determine whether the resulting metric values, DR-method rankings, and overall conclusions are sensitive to the number of points used to construct and display the path, and whether they remain stable once that choice is fixed. The primary linear dataset is a CD4+ T-cell surface-protein dataset (3,096 cells, 51 proteins); a ground-truth reference path was constructed from cells lying close to the first principal component (PC1) of a single cluster, providing a known linear trajectory in the high-dimensional space. Sixteen DR methods were applied and twelve geometric path-preservation metrics were computed, spanning log-ratio distortions of length, curvature, and spatial similarity; Spearman rank correlations of pairwise distances and segment lengths; and structural complexity measures including self-intersection frequency and coiling. To test the sensitivity of this evaluation framework to path density, we varied the fraction of cells used to define the reference path from 1% to 10% (31-310 path points) and tracked how method rankings responded. The same analysis was repeated on a topologically distinct reference, a closed B-cell cell-cycle loop detected by persistent homology in a separate CyTOF dataset, to test whether these conclusions about metric and method stability hold for cyclic as well as linear trajectories. Results: The central sensitivity question, whether the number of points used to construct the reference path changes the evaluation's conclusions, was answered negatively on both datasets. On the linear PC1 trajectory, absolute values of all twelve metrics shifted smoothly as the path-density threshold was varied from 1% to 10% (31-310 points), reflecting the broadening of the reference band, but each method's composite rank remained stable across every threshold: no method changed performance tier as the hyperparameter varied. A composite rank aggregating all twelve metrics identified the same consistently high-performing methods (UMAP, MDS, CNPE, TSNE, SPE, LPMIP) and consistently low-performing methods (SPMDS, LPP, DVE, LAPEIG, PHATE) at every density level tested. Considered on its own, `SpatDistSpear`, the single most discriminating metric, separated a high-fidelity group (LPMIP, DM, MDS, SPMDS, DVE, CISOMAP, CNPE; all r > 0.80) from a mid-range group (LAPEIG, SPE, PHATE, UMAP, TSNE, FOSMOD) and a low-fidelity group (PFA, NNP, LPP); global distance preservation and overall composite performance therefore do not always agree on the same "top tier" of methods, but this disagreement in which metric identifies the best methods is itself density-independent rather than an artifact of the specific threshold chosen. The cyclic loop reproduced the same density-independence: absolute metric values drifted with the per-segment band width, yet each method's composite rank again held constant across all eleven density levels. The identity of the best and worst performers was largely, though not entirely, conserved between the two topologies, with CNPE, LPMIP, SPE, and MDS as top performers and LAPEIG, DVE, and SPMDS as poor performers on both the linear path and the closed loop. UMAP and TSNE were exceptions, dropping from top performers on the linear path to the middle of the sixteen-method panel, rather than the worst tier, on the closed loop. This topology-dependence is a property of the reference geometry rather than of path density: it holds consistently regardless of how many points are used to define the path. Significance: This work introduces a direct, pipeline-independent evaluation of how DR methods distort trajectory geometry, a benchmarking dimension absent from existing TI comparisons. The within-dataset rank stability result, demonstrated on both a linear and a cyclic reference trajectory, validates the use of a fixed reference-path threshold as a robust operating point for large-scale DR benchmarking; however, the partial reordering of top performers between topologies shows that a method's DR benchmark ranking is trajectory-shape-dependent and should not be assumed to transfer from a linear to a cyclic reference.

Protein structure databases have grown from thousands of experimentally determined structures to hundreds of millions of AI-predicted models, creating an urgent need for search methods that combine high accuracy with practical scalability. Here, I present the third generation of Reseek, a protein structure search algorithm achieving the highest overall accuracy (median rank 1) according to diverse metrics among tested methods including DALI, Foldseek and TM-align. Improved accuracy is obtained by parallel sequence alignment of many discrete alphabets capturing primary, secondary and tertiary features, giving a combined space of[~] 1022 possible states. Separate statistical models are optimized for family, superfamily and fold discrimination, respectively, revealing distinct combinations of features that characterize each level. Hundreds of query structures can be searched a against a multimillion-structure database on a server computer in minutes, making large-scale structure search at state-of-the-art accuracy practical on commodity hardware.

External quality assessment (EQA) of multianalyte assays is commonly interpreted analyte by analyte, although many panels contain known relations among measured features that may reveal joint quality patterns. We propose PathEQA, a feature-graph-guided random forest framework in which a user-supplied graph can represent biochemical pathways, molecular interactions, shared measurement processes, or other domain relations. The same graph is allowed to influence feature representation, node-level candidate generation, and split selection, with an optional local grouped decision. We evaluated the framework in graph-aligned and graph-misspecified simulations and used a six-analyte catecholamine-related liquid chromatography-tandem mass spectrometry EQA data set as an illustrative case study (929 records from 58 laboratories and 117 complete multianalyte panels). In graph-aligned simulations, the grouped variant reduced test root mean squared error by 7.4-9.4% relative to ordinary random forest across training sizes of 60-240, whereas graph misspecification could worsen prediction. In the catecholamine case study, full PathEQA was comparable with ordinary random forest in laboratory-grouped cross-validation (RMSE 0.570 versus 0.569) and modestly better in the final-round temporal holdout (0.307 versus 0.318); a simpler static network-sampling baseline performed best. Dopamine-norepinephrine was the strongest pair, whereas dopamine-norepinephrine-epinephrine best estimated multianalyte failure burden. These results support a general conclusion: feature-graph guidance can improve small-sample multivariate quality assessment when the supplied structure is outcome-relevant, but graph relevance must be tested rather than assumed. Catecholamines serve here as a worked example rather than a restriction of the framework.

Machine learning workflows frequently incorporate data preprocessing to enhance predictive performance. However, the need for Super Learner ensembles made up only of preprocessing-invariant tree-based algorithms remains unexplored. Using three benchmark clinical classification datasets, this study examined how preprocessing affected the Super Learner's prediction performance, learner weight distribution, and oracle behavior. The Heart Disease (207 observations), Indian Liver Patient Dataset (583 observations), and Pima Indians Diabetes (768 observations) datasets were used to create a Super Learner ensemble model that included Classification and Regression Trees (CART), Random Forest, Ranger, and Extreme Gradient Boosting (XGBoost). Models were evaluated under raw and preprocessed data conditions using repeated cross-validation. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), and Brier score. Learner weight allocation and Oracle Gap were compared using paired Wilcoxon signed-rank tests with Benjamini-Hochberg adjustment. Preprocessing produced negligible changes in predictive performance for the Heart Disease and Pima datasets. For the ILPD dataset, preprocessing significantly improved AUC (0.746 to 0.752; adjusted p = 0.0017) and reduced the Brier score (0.177 to 0.175; adjusted p < 0.001). Learner weights remained largely stable, although Random Forest replaced Ranger as the dominant learner for the Heart Disease dataset. Oracle Gaps remained extremely small (<0.002) across all datasets and did not differ significantly between preprocessing conditions. Preprocessing provides limited benefit for Super Learner ensembles composed of preprocessing-invariant learners and does not materially alter their oracle behavior. Preprocessing decisions should therefore be guided by dataset characteristics rather than adopted as a universal modelling practice.

Performance estimates in protein function prediction depend not only on model choice but also on upstream decisions that define the learning problem. Using antioxidant protein classification as a controlled case study, we evaluated how dataset harmonisation, protein representation, redundancy control, and partitioning strategy affect protein machine learning pipelines. We integrated 18,804 records from 12 publicly available dataset entries into a curated consensus dataset of 4,193 protein sequences. One-hot encoding and six pretrained protein language model representations were evaluated as model inputs and as similarity spaces for redundancy reduction and distance-aware splitting. Representation choice substantially altered dataset geometry, retained dataset size, class balance, and downstream evaluation. At representation-specific p90 thresholds, one-hot encoding retained the complete dataset, whereas pretrained embeddings retained between 5% and 25% of sequences. Distance-aware partitioning reduced apparent performance relative to random splitting by up to 0.15 MCC before redundancy control, while this difference narrowed after similarity filtering. Selected configurations nevertheless maintained high performance under stricter evaluation, reaching an MCC of 0.84. These findings show that performance estimates should be interpreted as outcomes of complete data-centric workflows rather than isolated properties of predictive models.

Background: Machine Learning (ML) has shown promise in histopathological diagnosis, but rare tumours, like duodenal epithelial neoplasms, are challenging because large, labelled datasets are difficult to assemble. We propose a data-efficient ML pipeline for detecting duodenal epithelial neoplasia in whole-slide images. Methods: We assembled a five-hospital dataset of haematoxylin and eosin-stained duodenal biopsy whole slide images (WSIs) at 10x magnification, comprising 8,027 normal and 401 neoplastic biopsies, including adenomas, carcinomas and neuroendocrine tumours. WSIs were divided into patches and encoded using the Hibou-B foundation model. We compared global standardisation with unsupervised site-specific standardisation of patch embeddings. Our pipeline combined principal component analysis, a Gaussian mixture model and a Random Forest classifier. We assessed generalisability using leave-one-hospital-out cross-validation across four hospitals and on an independent test set from a fifth hospital. Results: In leave-one-hospital-out cross-validation, the pipeline achieved a mean accuracy of 90%, sensitivity of 96%, specificity of 83% and an AUC of 0.96. On the external test hospital, it achieved 94% accuracy, 93% sensitivity, 95% specificity, and AUC of 0.97. Site-specific standardisation improved mean cross-validation accuracy by 20 percentage points compared with global standardisation, while the Gaussian mixture pipeline improved accuracy by 12 percentage points compared with mean pooling of patch embeddings. Conclusions: We present a data-efficient ML approach for detecting duodenal epithelial neoplasia from WSIs. Strong performance was maintained across hospitals and on an independent external test cohort despite the limited number of neoplastic training cases, supporting the feasibility of foundation-model-based approaches for rare histopathological diagnoses.

SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.

Rapid and accurate identification of viral sequences underpins clinical diagnostics, epidemiological surveillance, and the safety testing of biological products, yet the established alignment-based methods such as BLAST are inherently closed-set, with accuracy tied to how well a query is already represented in the reference database. Recent advances in genomic foundation models (GFMs) have enabled alignment-free approaches to sequence classification. However, their performance relative to traditional methods remains incompletely characterised. We evaluated GFMs, specifically the Nucleotide Transformer (NT) and DNABERT families, for viral sequence classification using a dataset derived from the Reference Viral Database (RVDB). Sequence embeddings were generated with mean, max, and CLS pooling, classified by nearest-neighbour search in an embedding index and compared with BLAST as a reference. Matching BLAST provided a particularly stringent benchmark here, because as a closed-set method it compares each query directly against the exact reference sequences it stores, whereas a foundation model must rely on representations shaped by broad, general-purpose pre-training. On sequences of standard length, the best model, NTv3-650M, reached an accuracy of 0.93, approaching the 0.97 achieved by BLAST. On long genomic sequences, evaluated on a much smaller test set, the difference narrowed further, with NTv3-650M reaching 0.95 against 0.98 for BLAST. Robustness analyses showed that all GFMs were markedly more sensitive to base masking than BLAST. Both methods indexed the same labelled reference sequences, and BLAST compared queries with them directly at the nucleotide level, so approaching it from frozen, general-purpose representations without any parameter update is a meaningful result. NTv3-650M emerged as the most promising candidate for further optimisation and deployment in bioinformatics applications.

Purpose: Autosomal Dominant Polycystic Kidney Disease is the most common monogenic kidney disease and largely due to variants in PKD1. We aimed to assess pathogenicity evidence for PKD1 missense variants in disease databases and evaluate in silico pathogenicity prediction tool performance. Methods: PKD1 missense variants reported as pathogenic, likely pathogenic or likely benign were extracted from ClinVar and PKDB. Variants were re-classified using ACMG/AMP criteria to identify "truth sets" of pathogenic and benign variants. In silico scores were obtained from five tools (SIFT, PolyPhen-2, CADD, REVEL, AlphaMissense) and evaluated using established thresholds. A Receiver Operating Characteristic curve analysis was performed using the PKD1 variant truth sets. Results: 346/389 (89%) reported disease-causing missense variants in PKD1 were downgraded to Variants of Unknown Significance (VUS) using current classification criteria. Based on current thresholds, REVEL achieved the highest sensitivity of 62%, with specificity of 79%. AlphaMissense was the only tool not to misclassify any truth set variants, but many of the variant scores were between the pathogenic and benign thresholds. Conclusion: A large majority of PKD1 missense variants are classified as VUS with current pathogenicity criteria. Commonly used in silico tools, applied with established genome-wide thresholds, do not reliably distinguish pathogenic and benign missense variants in PKD1.

Hurthle cell carcinoma (HCC) is an aggressive form of thyroid cancer. While mitochondrial DNA mutations and chromosomal losses have been identified in HCC, isoform switching, and its functional consequences remain uncharacterized. This study reanalyzed NCBI GEO dataset GSE228870 (n = 32), using Salmon and IsoformSwitchAnalyzeR() to identify isoform switching. The analysis resulted in 371 switches across 335 genes showing functional consequences including loss of protein domains, shorter open reading frames (ORFs), loss of signal peptides and novel sub-cellular localizations. Most significant isoform switches (q-value < 0.05, |dIF| > 0.1) were observed in LAMA2, LSP1, MAD2L2, FBLN2 and CXCL12, implicating extracellular matrix dysregulation, DNA damage response, immune signaling and cytoskeleton regulation. These genes are expressed in normal thyroid (median TPM 20.69, 11.66, 14.79, 134.1 & 80.76). However, specific isoforms of LAMA2 and MAD2L2 are not expressed in normal thyroid, explaining tumor-specific expression in HCC. Alternative transcription termination site (ATTS) gain was significant, suggesting altered 3 end in HCC transcripts. TCGA SpliceSeq showed LSP1, FBLN2 and CXCL12 undergo alternative promoter (LSP1 exon1 PSI=94.5%, FBLN2 exon2 PSI=99.0%) and alternative termination (CXCL12 exon3.3 PSI=53.9%) in thyroid cancer, suggesting ATTS and alternative transcription start site (ATSS) as shared splicing dysregulation mechanisms. This is the first systematic characterization of isoform-level dysregulation in HCC.

Motivation: Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a comprehensive and systematic assessment of model architectures, data modalities, graph structures, and their performance for graph signal classification in the biomedical domain is yet to be performed. In order to close this gap, we conducted a benchmarking study on multiple GNNs on a Protein-Protein Interaction (PPI) network for Kidney Renal Clear Cell Carcinoma and Breast cancer subtype prediction, performing an in-depth investigation of architectures, incorporating skip connections and various data modalities. Results: While none of the GNNs outperforms the structure-agnostic Multi-Layer Perceptron baseline, all of them can handle bimodal data (gene methylation and expression) and offer the ability to gain explainability based on PPIs. We offer practical guidelines for applying GNNs to graph signal processing tasks specifically for cancer classification. Depending on the underlying dataset and PPI structure employed, models on different data modalities outperform others. Overall, we suggest using ChebNet, which tends to outperform the Graph Convolutional Network and the Graph Attention Network in cancer subtype prediction. We recommend using GNN architectures that employ a simple flattening readout layer, as they provide better classification performance and faster training time than those with global average pooling. Additionally, we tested residual connections, but they had only an insignificant impact on classification performance.

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

Accurate classification of lung adenocarcinoma growth patterns from hematoxylin and eosin (H\&E)-stained whole-slide images is essential for treatment planning and prognosis. We develop and evaluate an exponentially weighted ensemble deep learning approach that combines five architectures (EfficientNet-B3, DeiT3, Swin Transformer, Vision Transformer, ConvNeXt) with weights determined by a softmax transformation of per-model Quadratic Weighted Kappa (QWK) scores. Using 25,545 image patches at 40x magnification from the Dartmouth-Hitchcock dataset, we evaluated performance with stratified 5-fold cross-validation. Individual model performance ranged from Cohen's kappa kappa = 0.5563 (ConvNeXt) to kappa = 0.9561 (EfficientNet-B3). The ensemble achieved 96.52% accuracy and kappa = 0.9648, exceeding the best individual model by delta kappa = +0.0087 with statistical significance (McNemar's test, chi^2 = 50.84, p < 10^-12; bootstrap 95% CI on delta kappa [+0.0056, +0.0117]. Pairwise disagreement rates of 16.6--43.2% between architectures indicate that the models capture different aspects of tissue morphology, and the framework automatically down-weights unreliable components, as illustrated by ConvNeXt receiving only 5.7% ensemble weight despite a single-fold training failure. The approach offers a clinically grounded method for ensemble aggregation in digital pathology.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced renal cell carcinoma (RCC), yet only a subset of patients derives durable clinical benefit and no robust predictive biomarkers have been implemented in routine clinical practice. As T-cell receptor (TCR)-mediated antigen recognition underlies effective antitumor immunity, characterization of the intratumoral TCR repertoire may provide clinically relevant information for patient stratification. Here, we evaluated the predictive value of the baseline intratumoral TCR{beta} and TCR{gamma} repertoires in a real-world cohort of patients with advanced RCC treated predominantly with first-line immune checkpoint inhibitor-based regimens. Responders exhibited a significantly more even TCR{beta} repertoire together with increased TCR{gamma} diversity and clonal richness, whereas non-responders showed greater TCR{gamma} clonal dominance. In addition, the preferential usage of specific TRBV genes, including TRBV5.7, TRBV7.1, and TRBV18, was associated with clinical response and longer progression-free survival. In this exploratory cohort, integration of the most informative TCR{beta} and TCR{gamma} variables into a combined random forest model yielded an area under the receiver operating characteristic curve of 0.90 in leave-one-out cross-validation, with 100% sensitivity and 81.8% specificity at the selected threshold. To our knowledge, this is the first study to simultaneously characterize baseline intratumoral TCR{beta} and TCR{gamma} repertoires in patients with advanced RCC predominantly receiving first-line immune checkpoint blockade. These findings support baseline intratumoral TCR repertoire features as candidate tissue biomarkers for pretreatment patient stratification and provide a rationale for the prospective validation of TCR repertoire profiling as a predictor of immunotherapy benefit in RCC.

Summary: Prot2Surf is a software tool designed for the characterization and prediction of protein association to surfaces. In this application note, Prot2Surf was tested using catalytic domains of the lytic polysaccharide monooxygenases (LPMOs), interacting with native surfaces. The results show that the software can efficiently analyze key binding features, including protein-surface distances, distances between catalytically reactive atoms, and the orientation angle between surface chains and the protein. These features are essential for distinguishing productive binding poses in these protein-surface systems and for understanding interaction patterns that provide guidance on protein engineering. Prot2Surf performs these analyses within seconds to a few minutes, providing a fast and accessible framework to post-process and characterize protein-surface encounter complexes. Availability and implementation: Prot2Surf, which is written in Fortran90, is documented and freely available as open source on GitHub: https://github.com/TUNNELING-GROUP/Prot2Surf. In order to run Prot2Surf, users should also install the SDA software package which is freely available at https://www.h-its.org/downloads/sda7/.

Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset, particularly at higher initial seeding densities. We further extended the RF framework to predict growth curves across seeding densities. Collectively, these findings highlight the potential of data-driven modeling to complement conventional mathematical approaches and support more informed cell-culture design.

Discrete Ricci curvature is an appealing descriptor for single-cell trajectory graphs, but its practical value depends on task validity, graph-topology controls, and whether the biological target is a local transition region or a broader fate decision. We present a controlled empirical study of when curvature features add information to single-cell lineage graphs after repairing unsupported task definitions and preserving strong graph baselines. Paul15 is used as a benchmark-repair and boundary-setting dataset: on the repaired annotation-informed branch-region proxy, graph-plus-Forman and graph-plus-Ollivier improve modestly over graph topology alone (exact AUPRC 0.652 and 0.635 versus 0.607). Pancreas provides the clearest positive transition-region ranking result. On a Fev+ endocrine transition-region benchmark, graph-plus-Ollivier reaches exact AUPRC 0.761 versus 0.669 for the graph-feature stack (five-split canonical evaluation, paired mean +0.092, 95% CI [0.076, 0.114]), and a restricted preterminal endocrine fate task improves under both curvature hybrids. The primary evaluation is transductive node ranking; graph-attachment analyses provide supporting out-of-sample robustness checks. Zebrafish provides a realism check: branch-region ranking again benefits from a hybrid model, with graph-plus-Forman strongest (0.704 versus 0.593 for graph topology), whereas a valid early Notochord versus Prechordal Plate task is graph-topology dominated on the canonical graph. Supplementary checks, including a stricter zebrafish sample-token holdout, biologically grounded bottleneck proxy, negative controls, and pairwise transfer, sharpen the same conclusion without expanding the claim set. Curvature can add useful, dataset-dependent hybrid signal for lineage transition-region tasks, but curvature-only models are weak and graph topology remains essential and sometimes sufficient.

Identification of differentially expressed genes is a crucial step for downstream tasks on gene data such as biomarker discovery, drug target identification. Traditional Methods assume negative binomial distribution on RNA-sequence data and models the DEGs using either generalized linear models or by estimating dispersion and assumption of mean-variance rate. The proposed method uses axiomatic approach by using quantum mechanics principles to project transcript data onto a Hilbert space using a composite kernel. Using the curvature generated by the transcripts on the latent manifold within the Hilbert space, a gravitational search inspired mechanism is used to identify the optimal number of differentially expressed genes by minimizing a representational loss function, and a reduced gene feature space is constructed as the potential differentially expressed genes. The proposed method has been compared with existing empirical methods for validation using proper statistical and biological benchmark analysis.

Background: Immunochemotherapy (ICT) is considered standard in regards to care for small-cell lung cancer (SCLC) in extensive stages, yet reliable biomarkers for treatment response remain elusive. While previous univariate analyses suggest specific peripheral lymphocyte subsets correlate with survival, the systemic immune response involves complex, multivariate interactions that require advanced analytical approaches. Methods: This paper analysed high-dimensional flow cytometry data from 32 patients with stage IV SCLC treated with carboplatin, etoposide, and atezolizumab. Peripheral blood was analysed at baseline (V0) and longitudinally during treatment. To identify potential early predictive biomarkers and mitigate sample attrition in later cycles, we focused on baseline and measurements after two cycles of ICT (V1). We employed a rigorous machine learning framework utilising nested cross-validation, bootstrapping, and permutation-based statistical testing to evaluate eleven different regression and survival models. Results: Under model-appropriate metrics, regressors did not generalise (R2 <0); conversely, censoring-aware Random Survival Forests (RSF) successfully extracted robust prognostic signatures. Baseline immune profiles (V0) achieved a concordance index (C-index) of 0.66 (p= 0.015), while dynamic changes from V0 to V1 ({triangleup}V) achieved a C-index of 0.65 (p= 0.022). Crucially, absolute values measured after two cycles of ICT (V1) yielded no significant signal (p= 0.445). Feature importance analysis confirmed the prognostic value of Th17 normalisation and identified Naive Regulatory T cells and Memory B cells as candidate components. Conclusion: Machine learning validation confirms a predictive signal in the peripheral immune profile of SCLC patients. Early dynamic shifts in the balance between regulatory and effector immune arms are associated with prognosis, contrasting with the lack of signal in absolute counts after two cycles of ICT. These findings establish a proof of concept for multivariate liquid biopsy immune profiling, warranting confirmation in larger cohorts and highlighting the necessity of integrating systemic and tumour-intrinsic data.

MotivationInfluenza is an infectious disease associated with excess human deaths. For influenza A virus (IAV), the surface antigens hemagglutinin (HA) and neuraminidase (NA) define the specific subtype. The high mutation rate of IAV can make clinical testing and assigning subtype after sequencing challenging. Accurate subtype classification is important for tracking circulating IAV strains that may impact human health. ResultsWe analyzed a large IAV protein sequence dataset with known HA and NA subtypes. Using logistic regression and random forest approaches, we identified a small set of subtype-associated amino acids, which were then used to develop a subtype characterization method with near-optimal accuracy. Further analysis indicated that signal sequence peptide variation and indels in HA and NA explained the unique combination of subtype-associated amino acids.