The field of machine learning, which aims to develop computer algorithms that improve with
experience, holds promise to enable computers to assist humans in the analysis of large …

Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

This study uses three machine learning algorithms including, support vector machine (SVM),
random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices …

Non-invasive assays for early cancer screening are hampered by challenges in the isolation
and profiling of circulating biomarkers. Here, we show that surface proteins from serum …

Cancer tumor classification based on morphological characteristics alone has been shown
to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most …

DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and
its classification. It provides better insights of many genetic mutations occurring within a cell …

Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and
local responses to tumor growth, thereby altering their RNA profile. We determined the …

Tumor cells are often exposed to intrinsic and external factors that alter protein homeostasis,
thus producing endoplasmic reticulum (ER) stress. To cope with this, cells evoke an …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Methodological breakthroughs over the past four decades have repeatedly revolutionized
transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to …