A therapy that would once have been considered a feat of science fiction has reversed aggressive and incurable blood cancers in some patients, doctors report. The treatment involves precisely editing the DNA in white blood cells to transform them into a cancer-fighting living drug. The first girl to be treated, whose story we reported in 2022, is still free of the disease and now plans to become a cancer scientist. Now eight more children and two adults with T-cell acute lymphoblastic leukaemia have been treated, with almost two thirds (64%) of patients in remission. T-cells are supposed to be the body's guardians - seeking out and destroying threats - but in this form of leukaemia, they grow out of control.

Leukemia diagnosis and monitoring rely increasingly on high-throughput image data, yet conventional clustering methods lack the flexibility to accommodate evolving cellular patterns and quantify uncertainty in real time. We introduce Adaptive and Dynamic Neuro-Fuzzy Clustering, a novel streaming-capable framework that combines Convolutional Neural Network-based feature extraction with an online fuzzy clustering engine. ADNF initializes soft partitions via Fuzzy C-Means, then continuously updates micro-cluster centers, densities, and fuzziness parameters using a Fuzzy Temporal Index (FTI) that measures entropy evolution. A topology refinement stage performs density-weighted merging and entropy-guided splitting to guard against over- and under-segmentation. On the C-NMC leukemia microscopy dataset, our tool achieves a silhouette score of 0.51, demonstrating superior cohesion and separation over static baselines. The method's adaptive uncertainty modeling and label-free operation hold immediate potential for integration within the INFANT pediatric oncology network, enabling scalable, up-to-date support for personalized leukemia management.

Kennedy granddaughter Tatiana Schlossberg's terminal cancer diagnosis sheds light on acute myeloid leukemia warning signs and symptoms to watch for, with hope on the horizon for treatment.

To further clarify the model and workflow of our proposed BayReL, we provide a schematic illustration of BayReL in Figure S1, where we only include two views for clarity. Figure S2 shows the inferred bipartite network with the top 200 interactions by BayReL. Schematic illustration of BayReL. 2 Figure S2: The bipartite sub-network with the top 200 interactions inferred by BayReL in AML data, Genes and drugs are shown as blue and red nodes, respectively. D. Details on the experimental setups, hyper-parameter selection, and run time We learn the model for 1000 training epochs and use the validation set for early stopping. Each training epoch for CF, BRCA, and AML took 0.01, 0.42, In all experiments, we used CCAGFA R package as the official implementation of BCCA.

All linear models were implemented using PyTorch. We used an Nvidia GTX 1080 Ti GPU for training. IG computes feature attributions by comparing a model's prediction with the prediction We also found that EG led to the best performance for models trained using the DAPr framework. RNA-seq data as follows 1. N is the total number of counts. We also scaled Dasatinib IC50 values to have zero mean and unit variance.

Recent advances in machine learning have come in the form of complex models that humans struggle to interpret.

Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in extracting meaningful insights from complex data. We introduce Non-negative Stiefel Approximating Flow (NSA-Flow), a general-purpose matrix estimation framework that unifies ideas from sparse matrix factorization, orthogonalization, and constrained manifold learning. NSA-Flow enforces structured sparsity through a continuous balance between reconstruction fidelity and column-wise decorrelation, parameterized by a single tunable weight. The method operates as a smooth flow near the Stiefel manifold with proximal updates for non-negativity and adaptive gradient control, yielding representations that are simultaneously sparse, stable, and interpretable. Unlike classical regularization schemes, NSA-Flow provides an intuitive geometric mechanism for manipulating sparsity at the level of global structure while simplifying latent features. We demonstrate that the NSA-Flow objective can be optimized smoothly and integrates seamlessly with existing pipelines for dimensionality reduction while improving interpretability and generalization in both simulated and real biomedical data. Empirical validation on the Golub leukemia dataset and in Alzheimer's disease demonstrate that the NSA-Flow constraints can maintain or improve performance over related methods with little additional methodological effort. NSA-Flow offers a scalable, general-purpose tool for interpretable ML, applicable across data science domains.

We propose a doubly robust estimator for the average treatment effect in high dimensional low sample size observational studies, where contamination and model misspecification pose serious inferential challenges. The estimator combines bounded influence estimating equations for outcome modeling with covariate balancing propensity scores for treatment assignment, embedded within a penalized empirical likelihood framework using nonconvex regularization. It satisfies the oracle property by jointly achieving consistency under partial model correct ness, selection consistency, robustness to contamination, and asymptotic normality. For uncertainty quantification, we derive a finite sample confidence interval using cumulant generating functions and influence function corrections, avoiding reliance on asymptotic approximations. Simulation studies and applications to gene expression datasets (Golub and Khan) demonstrate superior performance in bias, error metrics, and interval calibration, highlighting the method robustness and inferential validity in HDLSS regimes. One notable aspect is that even in the absence of contamination, the proposed estimator and its confidence interval remain efficient compared to those of competing models.

Artificial intelligence (AI) agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act, and learn through complicated research workflows. Building on large language models (LLMs) coupled with perception, computation, action, and memory tools, these agentic AI systems could integrate diverse biomedical data, execute tasks, carry out experiments via robotic platforms, and iteratively refine hypotheses in closed loops. We provide a conceptual and technical overview of agentic AI architectures, ranging from ReAct and Reflection to Supervisor and Swarm systems, and illustrate their applications across key stages of drug discovery, including literature synthesis, toxicity prediction, automated protocol generation, small-molecule synthesis, drug repurposing, and end-to-end decision-making. To our knowledge, this represents the first comprehensive work to present real-world implementations and quantifiable impacts of agentic AI systems deployed in operational drug discovery settings. Early implementations demonstrate substantial gains in speed, reproducibility, and scalability, compressing workflows that once took months into hours while maintaining scientific traceability. We discuss the current challenges related to data heterogeneity, system reliability, privacy, and benchmarking, and outline future directions towards technology in support of science and translation.