Leukemia

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds.

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predi...

Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives.

Artificial intelligence is playing an increasingly important role in many fields of medicine, assisting physicians in most steps of patient management. In nephrology, artificial intelligence can already be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. However, many nephrologists are still unfamiliar with the basic principles of medical artificia...

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle.

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We u...

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinati...

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (...

Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.

PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is...

Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applie...

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.

Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative therapy for high-risk acute leukemia (AL), some patients still r...

Characterization and potential antitumor effect of a heteropolysaccharide produced by the red alga .

Taking into account the rising trend of the incidence of cancers of various organs, effective therapies are urgently needed to control human malignanc...

Acute stroke rehabilitation for gait training with cyborg type robot Hybrid Assistive Limb: A pilot study.

Robot-assisted gait training following acute stroke could allow patients with severe disability to receive a high dosage and intensity of gait trainin...

Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning.

Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymph...

A practical guide to intelligent image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of singl...

Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors.

In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for ...

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.

This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymp...

GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation.

Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the...

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

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 r...

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversi...

Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning.

Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other dis...

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptak...

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study.

This paper aims to assist in the prevention of Chronic Kidney Disease (CKD) by utilizing machine learning techniques to diagnose CKD at an early stage...