Biomarker Prediction
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Related Articles from SNS
MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models
Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced by any single feature-selection or classification method depend on algorithmic choices and rarely reproduce across pipelines. We present a disease-agnostic machine-learning framework that addresses this dependence by systematically benchmarking 25 (feature-selection x classifier) pipelines under five-fold stratified cross-validation,...
MTAP deficiency is a novel biomarker in neuroendocrine neoplasms of the lung
Abstract Introduction: MTAP emerges as potential predictive biomarker for MTA-cooperative PRMT5 inhibitors. Although MTAP attracts increasing attention in non-small cell lung cancer, its role in pulmonary neuroendocrine neoplasms (NENs) remains largely unexplored. Methods: Here, we assessed the prevalence of MTAP deficiency in 209 pulmonary NENs using immunohistochemistry (IHC).
A Foundation Model for the Cancer Genome
Cancer is a disease of the genome, in which somatic mutations and copy-number alterations determine tumour identity, clinical behaviour, and response to therapy. Consortium-scale sequencing has profiled hundreds of thousands of tumours, yet clinical interpretation still proceeds one alteration at a time against hand-curated knowledgebases, often ignoring co-occurring alterations and the genome-wide copy-number pattern. Self-supervised foundation models pretrained on unlabelled corpora have...
EndoTwin-W: glycodelin-A and CA-125 as non-invasive biomarkers of endometrial receptivity derived from a multiscale computational digital twin
Endometrial receptivity assessment currently requires invasive tissue biopsy, yet recent randomized trials have questioned the clinical utility of biopsy-based approaches. Here we present EndoTwin-W, a four-layer mechanistic computational model that simulates human endometrial remodeling from hormone inputs through receptor binding, pathway scoring, and continuous-time Markov chain cell-state transitions across 17 cell states. Transition rates were optimized against scRNA-seq and microarray...
Osteopontin Upregulation Defines a Pre-Rupture State in Thoracic Aortic Aneurysms in Mice and Humans
Background: Thoracic aortic aneurysm (TAA) is a life-threatening condition with an unpredictable lisk of rupture. Current clinical parameters have limited ability to accurately predict imminent rupture. Osteopontin (OPN) has been implicated in aortic aneurysm pathology, however, it role as a marker of imminent rupture remains.
Why you need to future proof your brain in middle age and how to start
To chart how our brains change over the course of our lives, neuroscientists have focused largely on beginnings and endings: the rapid development and pruning of neural connections in childhood and adolescence, and the degeneration associated with old age. “We kind of skipped over middle age,” says Sebastian Dohm-Hansen, a bioinformatician at University College Cork in Ireland. There are good reasons for that, not least that changes in brain structure and function are easier to spot with...
You Only Train Once: Differentiable Subset Selection for Omics Data
arXiv:2512.17678v2 Announce Type: replace Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that...
Routine laboratory trajectories encode the onset of organ-level complications in cancer
arXiv:2606.08538v1 Announce Type: new Abstract: Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,777,595 laboratory measurements from 3,905 patients with multiple myeloma or ovarian cancer predicted the two-year onset of 162 treatment-associated complications, including therapy-related myelodysplastic syndromes,...
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
arXiv:2412.06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues.
Policy on the AI Exponential
Policy on the AI Exponential In one of the side plots to The Lord of the Rings, two of the Hobbits attempt to rouse Treebeard—a wise but ponderous sentient tree—to defend his forest from an army that is cutting it down. The problem is that Treebeard operates at a very different speed than the Hobbits. It takes him a full day simply to say hello to another tree, so getting him and his peers to act fast enough is nearly impossible.