Lymphoma

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to gain insight into CPP activity. Experiments can provide detailed insight into the cell-penetration property of CPPs. However, the synthesis and identi...

Establishment of a SVM classifier to predict recurrence of ovarian cancer.

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from p...

Small airway segmentation in thoracic computed tomography scans: a machine learning approach.

Small airway obstruction is a main cause for chronic obstructive pulmonary disease (COPD). We propose a novel method based on machine learning to extr...

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India.

Rice is generally grown under completely flooded condition and providing food for more than half of the world's population. Any changes in weather par...

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-i...

Artificial neural networks (ANNs) and partial least squares (PLS) regression in the quantitative analysis of cocrystal formulations by Raman and ATR-FTIR spectroscopy.

The present work describes the development of an efficient, fast and accurate method for the quantification of polymer-based cocrystal formulations. S...

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system ...

Prediction of plant lncRNA by ensemble machine learning classifiers.

BACKGROUND: In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to adv...

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual v...

Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems.

The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing...

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contai...

Radiomics - the value of the numbers in present and future radiology.

Radiomics is a new concept that has been functioning in medicine for only a few years. This idea, created recently, relies on processing innumerable q...

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Ly...

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide...

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pa...

Robotic Patterning a Superhydrophobic Surface for Collective Cell Migration Screening.

Collective cell migration, in which cells migrate as a group, is fundamental in many biological and pathological processes. There is increasing intere...

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information.

Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological ap...

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase whe...

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.

Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical...