Normal view
-
cs.AI, q-bio.NC updates on arXiv.org
-
From Data to Behavior: Predicting Unintended Model Behaviors Before Training
arXiv:2602.04735v1 Announce Type: cross Abstract: Large Language Models (LLMs) can acquire unintended biases from seemingly benign training data even without explicit cues or malicious content. Existing methods struggle to detect such risks before fine-tuning, making post hoc evaluation costly and inefficient. To address this challenge, we introduce Data2Behavior, a new task for predicting unintended model behaviors prior to training. We also propose Manipulating Data Features (MDF), a lightwei
-
cs.AI, q-bio.NC updates on arXiv.org
-
Contrastive Continual Learning for Model Adaptability in Internet of Things
arXiv:2602.04881v1 Announce Type: cross Abstract: Internet of Things (IoT) deployments operate in nonstationary, dynamic environments where factors such as sensor drift, evolving user behavior, and heterogeneous user privacy requirements can affect application utility. Continual learning (CL) addresses this by adapting models over time without catastrophic forgetting. Meanwhile, contrastive learning has emerged as a powerful representation-learning paradigm that improves robustness and sample e
Contrastive Continual Learning for Model Adaptability in Internet of Things
-
cs.AI, q-bio.NC updates on arXiv.org
-
DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data
arXiv:2503.01733v3 Announce Type: replace-cross Abstract: Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific n
DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data
-
Nature - Issue - nature.com science feeds
-
Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-xA multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.
Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes
Nature, Published online: 04 February 2026; doi:10.1038/s41586-025-10087-x
A multiancestry phenome-wide analysis of copy number variants in the UK Biobank genomes increases power to detect genetic associations with complex traits across human populations.-
npj Digital Medicine
-
Digital intervention <i>mylovia</i> improves sexual functioning in women with sexual dysfunction in randomized controlled trial
npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-zDigital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial
Digital intervention <i>mylovia</i> improves sexual functioning in women with sexual dysfunction in randomized controlled trial
npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-z
Digital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial-
Omics in Gastric
-
Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer
Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.ABSTRACTBACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/b
Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer
Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.
ABSTRACT
BACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.
OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling.
DESIGN: We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort.
RESULTS: A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage.
CONCLUSION: This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.
PMID:41617485 | DOI:10.1136/gutjnl-2025-337247
-
Pulmonary nodule
-
Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach
Discov Oncol. 2026 Jan 29;17(1):178. doi: 10.1007/s12672-025-03646-1.ABSTRACTPulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implications. The early detection of malignant nodules is critically important for improving the prognosis of lung cancer, which remains the leading cause of cancer-related mortality worldwide. Traditional imaging techniques have Limitations in accurately classifying pulmonary nodules. Liqu
Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach
Discov Oncol. 2026 Jan 29;17(1):178. doi: 10.1007/s12672-025-03646-1.
ABSTRACT
Pulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implications. The early detection of malignant nodules is critically important for improving the prognosis of lung cancer, which remains the leading cause of cancer-related mortality worldwide. Traditional imaging techniques have Limitations in accurately classifying pulmonary nodules. Liquid biopsy, a minimally invasive method that evaluates circulating components in the Blood, presents promising diagnostic potential in this context. This study aims to evaluate the diagnostic capacity of multiple liquid biopsy biomarkers for early and accurate differentiation between benign and Malignant pulmonary nodules. Accordingly, we conducted a comprehensive study involving a meta-analysis, selecting 16 eligible studies that utilised liquid biopsy to assess various circulating biomarkers in the diagnostic yield. The most significant results were linked to circulating free DNA (cfDNA). However, other components, including circulating tumour cells (CTCs), microRNAs/pfeRNAs, extracellular vesicles (EVs), serological markers, and imaging techniques, also provided valuable information. Similarly, integrating multi-omics data with machine learning models has been shown to enhance the ability to differentiate between benign and malignant pulmonary nodules, thereby supporting early diagnosis and improved management for patients with lung cancer.
PMID:41612093 | PMC:PMC12855667 | DOI:10.1007/s12672-025-03646-1
-
Journal of Medical Internet Research
-
The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study
Background: Online health communities have evolved into digital marketplaces where physicians have to compete for patients. Existing research examines physician-patient dynamics through a patient-centric lens, treating physicians as passive recipients of ratings and reviews, while the strategic role of physician self-disclosure remains unexamined. This gap constrains a comprehensive understanding of how physicians can actively shape patient decisions, making the investigation of strategic self-d
The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study
-
cs.AI, q-bio.NC updates on arXiv.org
-
Agentic Digital Twins: A Taxonomy of Capabilities for Understanding Possible Futures
arXiv:2601.18799v1 Announce Type: cross Abstract: As digital twins (DTs) evolve to become more agentic through the integration of artificial intelligence (AI), they acquire capabilities that extend beyond dynamic representation of their target systems. This paper presents a taxonomy of agentic DTs organised around three fundamental dimensions: the locus of agency (external, internal, distributed), the tightness of coupling (loose, tight, constitutive), and model evolution (static, adaptive, rec
Agentic Digital Twins: A Taxonomy of Capabilities for Understanding Possible Futures
-
cs.AI, q-bio.NC updates on arXiv.org
-
Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?
arXiv:2508.10530v2 Announce Type: replace Abstract: The alignment of language models~(LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize the expected reward that reflects human preferences. Recently, Direct Preference Optimization~(DPO) was proposed as a LM alignment method that directly optimize the policy from static preference data, and further improved by incorporating on-policy sampling~(i.e., pre
Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?
-
cs.AI, q-bio.NC updates on arXiv.org
-
Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery
arXiv:2601.12542v2 Announce Type: replace Abstract: Artificial intelligence systems for scientific discovery have demonstrated remarkable potential, yet existing approaches remain largely proprietary and operate in batch-processing modes requiring hours per research cycle, precluding real-time researcher guidance. This paper introduces Deep Research, a multi-agent system enabling interactive scientific investigation with turnaround times measured in minutes. The architecture comprises specializ
Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery
-
cs.AI, q-bio.NC updates on arXiv.org
-
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
arXiv:2406.16821v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce \textbf{\textnormal{\textbf{BADGER}}}, a general \textbf{binding-affinity guidance framework for d
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
-
cs.AI, q-bio.NC updates on arXiv.org
-
AI-generated data contamination erodes pathological variability and diagnostic reliability
arXiv:2601.12946v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathologic
AI-generated data contamination erodes pathological variability and diagnostic reliability
-
Journal of Medical Internet Research
-
Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review
Background: Ambulatory oxygen therapy is prescribed for patients with chronic lung diseases who experience exertional hypoxemia. However, available devices may not adequately meet user requirements, and their performance characteristics are heterogeneous. Objective: This study aims to identify devices available for delivery of ambulatory oxygen therapy, the technologies that they use to generate oxygen, the performance characteristics of each device, and the development status. Methods: We used
Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review
-
npj Digital Medicine
-
Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials
npj Digital Medicine, Published online: 27 January 2026; doi:10.1038/s41746-026-02396-wPublisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials
Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials
npj Digital Medicine, Published online: 27 January 2026; doi:10.1038/s41746-026-02396-w
Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials-
cs.AI, q-bio.NC updates on arXiv.org
-
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
arXiv:2601.17310v1 Announce Type: new Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
-
cs.AI, q-bio.NC updates on arXiv.org
-
Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity
arXiv:2601.18641v1 Announce Type: cross Abstract: Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society. While fluency is often equated with credibility and competence, individuals with atypical speech patterns are routinely marginalized. Given the current state of the debate, this article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence. Automated speech r
Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity
-
cs.AI, q-bio.NC updates on arXiv.org
-
MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
arXiv:2409.07314v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows. To bridge the gap between theoretical capability and verified utility, we introduce MEDIC, a comprehensive evaluation framework establishing leading indicators across various clinical dimensions. Beyond
MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
-
cs.AI, q-bio.NC updates on arXiv.org
-
Pretrain Value, Not Reward: Decoupled Value Policy Optimization
arXiv:2502.16944v2 Announce Type: replace-cross Abstract: In this paper, we explore how directly pretraining a value model simplifies and stabilizes reinforcement learning from human feedback (RLHF). In reinforcement learning, value estimation is the key to policy optimization, distinct from reward supervision. The value function predicts the \emph{return-to-go} of a partial answer, that is, how promising the partial answer is if it were continued to completion. In RLHF, however, the standard p
Pretrain Value, Not Reward: Decoupled Value Policy Optimization
-
cs.AI, q-bio.NC updates on arXiv.org
-
uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
arXiv:2506.11238v2 Announce Type: replace-cross Abstract: Introduction: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains challenging due to variability in electrocardiogram (ECG) waveforms caused by differences in lead placement, recording conditions, and population demographics. Methods: We developed uPVC-Net, a universal deep learning model to detect PVCs from any single-lead ECG recordings. The model is devel