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ScienceEvidence-backed gain

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologica…

RNA Biology

Both readings

Machine learning integrated explainable artificial intelligence in predicting toxicities of enfortumab vedotin in urothelial carcinoma: an exploratory study

Both readings

Can Artificial Intelligence Deliver in Real-World Health Systems? Early Insights From Augmented Intelligence in Medicine and Healthcare Initiative's 5 Funded Projects

Evidence-backed gain

Carbon dioxide hydrogenation on copper and nickel catalysts <i>via</i> a conformal sampling approach

Evidence-backed problem

Silicon Valley shouldn’t self-regulate on AI safety, Cohere CEO says

TORONTO — Aidan Gomez, CEO of Canadian AI company Cohere, says a "small group of companies in Silicon Valley" shouldn’t be left in charge of ensuring AI is safe.

Evidence-backed gain

Cohere, Aleph Alpha combine to target enterprise AI market

The combined business will operate as Cohere, with dual headquarters in Toronto and Berlin, while Aleph Alpha's Heidelberg office will focus on research. Aleph Alpha co-chief Ilhan Scheer will become chief operating officer of Cohere.

Evidence-backed problem

The Spin | Robots bowling the perfect doosra are some way off but AI is already reshaping cricket

Daniel Kokotajlo, a former researcher at OpenAI, warned last week that it was possible we would end up “creating a new species that ends up ruling the world”. Once the initial shock wore off, and I’d considered what this could mean for my young children and the future of the planet, another thought popped into my head: will this new species be able to swing an old cricket ball or navigate a seaming green top? It sounds frivolous. It is frivolous. But the question is not quite as ridiculous as it first appears. F…

Evidence-backed problem

Mirror publisher to cut 220 editorial jobs as readers turn to AI summaries

The publisher of the Mirror and Express newspapers is to cut a further 220 editorial jobs as it adapts to a dramatic fall in online traffic while readers increasingly turn to summaries generated by artificial intelligence. Reach, which also owns scores of online brands and regional titles including the Manchester Evening News, the Birmingham Mail and the Liverpool Echo, said the latest cuts were necessary to cope with a “mammoth shift” in how audiences seek out content. The move represents the latest restructure…

Science

Can AI Predict Publication? Multimodal Large Language Models and the Structural Determinants of Surgical Scholarship

BackgroundWhether artificial intelligence can identify publishable scientific work is untested. We evaluated whether a multimodal large language model (MLLM) could predict, from poster content alone, which abstracts at the American Association for the Surgery of Trauma (AAST) Annual Meetings reached publication, and characterized the investigator, institutional, and domain level determinants situating model performance.MethodsWe retrospectively analyzed 260 abstracts from the 2021-2022 AAST Annual Meetings. Bibl…

Health

Quality of Online Information for Management of Leg Pain in Children and Adolescents With Hypermobility-Associated Conditions

Aim Online health information seeking continues to gain popularity. Quality assessment of online information is particularly important for patient populations with low satisfaction and disengagement from health professionals and services, such as hypermobility-associated conditions. We aimed to assess the quality of online health information, for management of lower limb pain in children with hypermobility-associated conditions, available through search engines and corresponding AI-generated overviews. Methods W…

Health

Decoding the Bone-Eye Axis: Machine Learning for Age-Related Macular Degeneration Risk Prediction

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss, yet systemic determinants of its risk remain incompletely understood. Bone mineral density (BMD), a marker of skeletal and biological aging, may reflect shared pathways linking systemic and retinal degeneration. We investigated the association between BMD and AMD using a multilayered framework integrating epidemiological analyses, Mendelian randomization (MR), proteomic and metabolomic profiling, machine learning, and an exper…

Health

A hybrid deep learning framework for early autism screening

Objectives Early diagnosis of autism spectrum disorder (ASD) remains a significant challenge due to the time-consuming and subjective nature of traditional diagnostic methods. This study proposes a reliability-oriented hybrid deep learning framework that provides a low-cost, scalable, non-invasive, and AI-assisted pre-screening tool for early ASD risk indication and referral support. Methods The proposed system integrates two independent deep learning architectures: (1) a ResNet18 model optimized with 10-fold cr…

Science

Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum

The semi-quantitative assessment of brain [18F]FDG PET provides a more objective interpretation and improved accuracy in differentiating across neurodegenerative diseases. However, the correct identification of anatomical regions of interest without a structural MRI is challenging. Thus, this study aims to develop a deep-learning-based (DL-based) model for the automatic segmentation of 52 anatomical regions in brain [18F]FDG PET images and validate it across different metabolic profiles. 1628 brain [18F]FDG PET…

Health

Determination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets

Objectives To develop a machine learning (ML) approach to explore self-reported factors predictive for recovery in a small set of spinal pain patients. Methods In this prospective cohort study, patients ( N = 96; mean age = 44.5 ± 16.5 years; 53 female) completed an extensive questionnaire at baseline and after 1 and 3 months. Prediction targets were defined as binary outcomes (recovery/non-recovery) based on improvement at 3 months for pain intensity, disability, quality of life, and Patient Global Impression o…

Health

Is It Feasible to Assess Clinical Remission in Asthma via a Customized Data Extraction Approach from a Real-World Clinical Electronic Medical Records Database in Japan? A Retrospective Real-World Study

Objective : Clinical remission (CR) is an ambitious and attainable treatment goal for patients with asthma. This retrospective, observational study determined the feasibility of assessing CR using an electronic medical record (EMR) database. Methods : Data from the JAMDAS database were analyzed for patients with asthma initiating fluticasone furoate/umeclidinium/vilanterol single-inhaler triple therapy between August 18, 2020 and December 31, 2024. Asthma Control Test (ACT) scores were extracted using structured…