Leukemia

Computational methods for characterizing and learning from heterogeneous cell signaling data.

Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating dis...

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment ...

Impact of polyphenol-rich extracts of fruits and seeds on viability of human intestinal and liver cells .

(Kakadu plum) is a native Australian fruit consumed by Indigenous Australians for centuries. Commercial interest in has increased in recent years du...

PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer.

The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression an...

Oncologic Equipoise Between Robotic and Open Radical Cystectomy.

Our objective was to establish the incidence of positive surgical margins, recurrence patterns, and recurrence-free (RFS) and overall survival (OS) i...

CBCR: A Curriculum Based Strategy For Chromosome Reconstruction.

In this paper, we introduce a novel algorithm that aims to estimate chromosomes' structure from their Hi-C contact data, called Curriculum Based Chrom...

Magnetic tri-bead microrobot assisted near-infrared triggered combined photothermal and chemotherapy of cancer cells.

Magnetic micro/nanorobots attracted much attention in biomedical fields because of their precise movement, manipulation, and targeting abilities. Howe...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires l...

An anomaly detection approach to identify chronic brain infarcts on MRI.

The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical i...

A Novel Drug Repositioning Approach Based on Collaborative Metric Learning.

Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of ...

Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviou...

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to tre...

Establishing a Robot-Assisted Kidney Transplant Program: Independent Evaluation of the Learning Curve and Surgical Nuances.

Robot-assisted kidney transplantation (RAKT) is a technically challenging procedure and there has been limited reporting of its learning curve (LC), ...

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. There...

Is Anti-Müllerian Hormone a Marker of Ovarian Reserve in Young Breast Cancer Patients Receiving a GnRH Analog during Chemotherapy?

INTRODUCTION: Anti-Müllerian hormone (AMH) is the most reliable biomarker of ovarian reserve; however, its role in predicting ovarian recovery after c...

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.

PURPOSE: The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer patholog...

CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre-B-cell acute lymphoblastic leukaemia.

Disease relapse is the greatest cause of treatment failure in paediatric B-cell acute lymphoblastic leukaemia (B-ALL). Current risk stratifications fa...

Biomimetic FPGA-based spatial navigation model with grid cells and place cells.

The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons respo...

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and p...

BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells.

BACKGROUND AND OBJECTIVE: The classification of human peripheral blood cells yields significance in the detection of inflammation, infections and bloo...