AIMC Topic: Leukemia, Myeloid, Acute
Clear Filters Showing 51 to 60 of 88 articles
MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.
Scientific reports
Jul 23, 2020
In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this informat...
Acute myeloid leukemia and artificial intelligence, algorithms and new scores.
Best practice & research. Clinical haematology
Jun 7, 2020
Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of ou...
A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.
Journal of materials chemistry. B
Feb 18, 2020
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...
A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer.
PloS one
Dec 11, 2019
Disease causing gene identification is considered as an important step towards drug design and drug discovery. In disease gene identification and classification, the main aim is to identify disease genes while identifying non-disease genes are of les...
A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.
Blood cancer journal
Oct 1, 2019
Machine learning applications in the diagnosis of leukemia: Current trends and future directions.
International journal of laboratory hematology
Sep 9, 2019
Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing tr...
Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach.
Cancer medicine
Jul 15, 2019
Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative therapy for high-risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with...
Disease Management
Hematopoietic Stem Cell Transplantation
Transplantation, Homologous
Patient Participation
Treatment Outcome
Decision Trees
Female
Survival Analysis
Young Adult
Adult
Leukemia, Myeloid, Acute
Male
Middle Aged
Models, Theoretical
Machine Learning
Algorithms
Prognosis
Clinical Decision-Making
Humans
Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.
Cancer gene therapy
May 29, 2019
Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patient...
Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome.
EBioMedicine
Oct 22, 2018
BACKGROUND: Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from dr...
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Nature communications
Jan 3, 2018
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatm...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31964
- Neural Networks, Computer 30201
- Algorithms 27375
- Deep Learning 26880
- Artificial Intelligence 24818
- Robotics 13382
- Image Processing, Computer-Assisted 9730
- Retrospective Studies 9328
- Magnetic Resonance Imaging 6341
- Reproducibility of Results 5670
- Tomography, X-Ray Computed 4863
- Support Vector Machine 4762
- Computational Biology 4328
- Brain 4125
- Computer Simulation 3932
Recent Journals
- Scientific reports 6689
- Sensors (Basel, Switzerland) 4794
- PloS one 4186
- Neural networks : the official journal of the International Neural Network Society 3026
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1915
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1378
- IEEE journal of biomedical and health informatics 1167
- Journal of chemical information and modeling 1059
- Nature communications 1029
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 872
- Journal of medical Internet research 853
- Briefings in bioinformatics 853