This perspective surveys three-factor learning, a key mechanism for brain-inspired spiking neural networks. Adding a third, global signal, analogous to neuromodulators, allows these AI systems to learn and adapt more effectively. This overview connects recent advances in neuroscience and machine learning, outlining a path toward more powerful AI.

We live in an era of escalating sustainability challenges, such as exceeding the global 1.5°C threshold in 2024, the collapse of talks to address the widespread plastic crisis, and increasing wildfire...

As artificial intelligence matures, the impact it might have on how society functions is being actively pondered. In this opinion, through uniform-binomial mixtures, the author sheds quantitative ligh...

Data constitute machine learning’s fuel. Unfortunately, real-world data are often imbalanced. This poses a huge challenge to machine learning, as rare cases are hard to learn. So far, no adequate measure has been available to assess the impact of imbalance on machine learning’s performance. As a remedy for this omission, the authors introduce the status of imbalance (SIMBA), a reliable, robust, and generic imbalance measure that outperforms existing ones.

Brain-computer interfaces (BCIs) offer a promising avenue to restore motor function by converting neural activity into control signals for external devices. While current BCI technologies have successfully demonstrated brain-directed control of robotic arms, they still face significant limitations in dexterous hand movement control. This study proposes a novel approach called SynergyNet to decode complex hand movements directly from brain signals, facilitating high-performance BCI control with fine movements.

In this review, the authors explore the transformative impact of large language models (LLMs) on drug discovery and development. They detail how LLMs can potentially accelerate our understanding of…

Quaternions, a type of hypercomplex number, are particularly useful in handling three-dimensional data, i.e., color images. Within the feature-rich hypercomplex setting, image-processing workflows can be realized for natural and biomedical images enabling alternative visual representations, offering effective solutions to current problems in computer vision and digital pathology, and generally expanding the scope and impact of hypercomplex image processing across a wide range of applications.

Transformer models are adaptable to off-the-beaten-path molecular design tasks, such as protein-sequence-based compound design. The authors use these predictions as a model system to unravel the learning characteristics of transformers. The analysis reveals that the transformer predictions are statistically driven and that the models do not learn protein-ligand interactions or other biologically relevant information. Instead, compound memorization plays an important role. These findings caution against over-interpretation of sequence-based generative compound design using transformer models.

This study explores AI-generated writing adoption across consumer complaints, corporate press releases, job postings, and international organization communications, revealing widespread use following ...

This study explores AI-generated writing adoption across consumer complaints, corporate press releases, job postings, and international organization communications, revealing widespread use following ChatGPT’s release. Findings highlight significant generative AI integration by small firms, corporations, and global institutions, emphasizing its transformative impact on communication practices, business strategies, and public policy.

Visual quality assessment is crucial for both human perception and artificial vision systems. This study reveals how the human brain naturally perceives image quality through distinct neural pathways. Functional magnetic resonance imaging results show that while low-level visual areas are highly sensitive to distortions, higher-level regions compensate to maintain recognition, much like how AI systems might “fill in” missing details. These findings not only deepen our understanding of human vision but also provide biological inspiration for developing more robust image-processing algorithms.

The vast microbial “dark proteome” remains largely uncharacterized. Nelson et al. present LA4SR, a transformer framework that classifies algal sequences 10,000× faster than traditional methods with near-perfect recall. Trained on 77 million sequences including synthetic chimeras, the system can robustly classify unknown sequences with natural or scrambled start and stop information. They also developed an accompanying suite of explainability software to reveal biologically meaningful patterns linking amino acid patterns to evolutionary affiliations.

This study introduces a language-model-driven agent system that captures and applies laboratory knowledge to autonomously plan, execute, and analyze experiments. When the agent system was demonstrated on quantum computing tasks, the agents achieved performance on par with expert researchers. By enabling intelligent coordination and decision-making in complex lab settings, this approach paves the way for more flexible, scalable automation in experimental science.

LigUnity is a protein-ligand affinity foundation model, enabling both rapid virtual screening and precise hit-to-lead optimization. LigUnity learns a shared “language” for protein pockets and ligands, outperforming docking in screening and approaching FEP+ accuracy at far lower cost. It generalizes to new targets and new chemical scaffolds and achieves 106 times the speedup compared to Glide-SP, offering a practical foundation for early-stage drug discovery.

Sleep staging is vital for understanding sleep physiology and diagnosing sleep disorders, yet manual scoring is time-consuming and unscalable. The authors introduce AISleep, an unsupervised sleep staging algorithm based on feature-weighted kernel density estimation, using only a single EEG channel. AISleep outperforms SOTA unsupervised methods, generalizes better than supervised models, and uncovers age-related EEG feature decline impacting staging accuracy. Its lightweight design enables integration into portable devices, providing a practical, scalable solution for accurate and accessible home-based sleep monitoring.

This opinion piece discusses the Bioconductor project for open-source bioinformatics and the engineering concepts underlying its effectiveness to date. Since the inception of Bioconductor in 2002 with...

The authors present an integrated radiomics-deep learning-LLM agent framework for hepatocellular carcinoma (HCC) management. A multimodal fusion model accurately predicts key pathological features from MRI, while LLM agents generate personalized treatment strategies. Expert evaluation confirms strong clinical alignment. This work demonstrates the promise of AI-powered decision systems in precision oncology.

In this review, the authors explore the transformative impact of large language models (LLMs) on drug discovery and development. They detail how LLMs can potentially accelerate our understanding of disease mechanisms, facilitate de novo drug discovery, and optimize clinical trial processes, including patient matching and outcome prediction. They also assess the current maturity of LLM applications and outline future directions for integrating these advanced computational models into the pharmaceutical pipeline.

This review explains how artificial intelligence is reshaping in vitro fertilization, from forecasting embryo health to tailoring hormone therapy. By combining images, health records, and molecular clues, these tools promise safer, more effective fertility treatments for patients worldwide.