Things to Do in L.A. Tap to enable a layout that focuses on the article. 'I just wanted to help.' Father turns to 9-year-old son for lifesaving stem cell donation Stephen Mondek became what Cedars-Sinai Medical Center believes is its youngest known stem cell donor. His father was dying of acute myeloid leukemia, a cancer that affects blood-forming cells in the bone marrow, and needed a donation to rebuild his immune system. This is read by an automated voice. Please report any issues or inconsistencies here .

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.

As machine learning models are increasingly deployed in sensitive application areas, the demand for interpretable and trustworthy decision-making has increased. Random Forests (RF), despite their widespread use and strong performance on tabular data, remain difficult to interpret due to their ensemble nature. We present Forest-Guided Clustering (FGC), a model-specific explainability method that reveals both local and global structure in RFs by grouping instances according to shared decision paths. FGC produces human-interpretable clusters aligned with the model's internal logic and computes cluster-specific and global feature importance scores to derive decision rules underlying RF predictions. FGC accurately recovered latent subclass structure on a benchmark dataset and outperformed classical clustering and post-hoc explanation methods. Applied to an AML transcriptomic dataset, FGC uncovered biologically coherent subpopulations, disentangled disease-relevant signals from confounders, and recovered known and novel gene expression patterns. FGC bridges the gap between performance and interpretability by providing structure-aware insights that go beyond feature-level attribution.

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clinical labs. While numerous ML models for flow cytometry data have been proposed, few studies have described the clinical deployment of such models. Realizing the potential gains of ML models in clinical labs requires not only an accurate model, but infrastructure for automated inference, error detection, analytics and monitoring, and structured data extraction. Here, we describe an ML model for detection of Acute Myeloid Leukemia (AML), along with the infrastructure supporting clinical implementation. Our infrastructure leverages the resilience and scalability of the cloud for model inference, a Kubernetes-based workflow system that provides model reproducibility and resource management, and a system for extracting structured diagnoses from full-text reports. We also describe our model monitoring and visualization platform, an essential element for ensuring continued model accuracy. Finally, we present a post-deployment analysis of impacts on turn-around time and compare production accuracy to the original validation statistics.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis related genes (DEFRGs) between AML and normal marrow tissues. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we selected 10 DEFRGs that were associated with OS to build a prognostic signature.

Artificial intelligence is a much-discussed topic in medicine, especially in the field of diagnostics. "We aimed to investigate the potential on the basis of a specific example," explains Prof. Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "Because this requires large amounts of data, we evaluated data on the gene activity of blood cells. Numerous studies have been carried out on this topic and the results are available through databases. Thus, there is an enormous data pool. We have collected virtually everything that is currently available."

In the largest metastudy to date on acute myeloid leukemia, German researchers contend that they have demonstrated that artificial intelligence can detect this common and deadly form of blood cancer. Results of their proof-of-concept study, published in the journal iScience, are based on the analysis of the gene activity of cells found in blood using 12,029 samples from 105 different studies. "Our results support the notion that transcriptomics combined with machine learning could be used as part of an integrated -omics approach where risk prediction, differential diagnosis and subclassification of AML is achieved by genomics while diagnosis could be assisted by transcriptomic-based machine learning," state the study's authors. "The transcriptome holds important information about the condition of cells," says Joachim Schultze, a research group leader at the DZNE and head of the Department for Genomics and Immunoregulation at the LIMES Institute of the University of Bonn. "However, classical diagnostics is based on different data. We therefore wanted to find out what an analysis of the transcriptome can achieve using artificial intelligence--that is to say trainable algorithms."

Acute Myeloid Leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different studies, we present a large-scale study of machine learning-based prediction of AML in which we address key questions relating to the combination of machine learning and transcriptomics and their practical use. We find data-driven, high-dimensional approaches – in which multivariate signatures are learned directly from genome-wide data with no prior knowledge – to be accurate and robust. Importantly, these approaches are highly scalable with low marginal cost, essentially matching human expert annotation in a near-automated workflow.

Artificial intelligence can detect one of the most common forms of blood cancer - acute myeloid leukemia (AML) - with high reliability. Researchers at the German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn have now shown this in a proof-of-concept study. Their approach is based on the analysis of the gene activity of cells found in the blood. Used in practice, this approach could support conventional diagnostics and possibly accelerate the beginning of therapy. The research results have been published in the journal "iScience".

We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD). In our DL network, autoencoders are stacked to form a hierarchical DL model from which raw data are compressed and organized and high-level features are extracted. The network is written in R language and is designed to predict prognosis of AML for a given case (DTD of more than or less than 730 days). The DL network achieves an excellent accuracy of 83% in predicting prognosis. As a proof-of-concept study, our preliminary results demonstrate a practical application of DL in future practice of prognostic prediction using next-gen sequencing (NGS) data.