AIMC Topic: Leukemia
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A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.
International journal of molecular sciences
Feb 3, 2022
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Ab...
Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model.
Scientific reports
Jan 19, 2022
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system...
A deep learning method for counting white blood cells in bone marrow images.
BMC bioinformatics
Nov 8, 2021
BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counti...
Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison.
Medical engineering & physics
Oct 13, 2021
Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize and classify leukemia. But these methods are costly, slow and affected by the proficiency and exper...
Identification of biomarkers for acute leukemia via machine learning-based stemness index.
Gene
Aug 16, 2021
Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as r...
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.
Genome biology
Aug 16, 2021
Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chr...
Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images.
Clinical lymphoma, myeloma & leukemia
Jul 20, 2021
BACKGROUND: Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing...
Swarm Learning for decentralized and confidential clinical machine learning.
Nature
May 26, 2021
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an i...
Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.
Sensors (Basel, Switzerland)
Apr 24, 2021
Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infect...
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.
Nature communications
Mar 25, 2021
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which use...
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