Gene-expression signature-based disease classification and clinical outcome prediction has
not been widely introduced in clinical medicine as initially expected, mainly due to the lack …

The patterns identified from the systematically collected molecular profiles of patient tumor
samples, along with clinical metadata, can assist personalized treatments for effective …

A large number of biomarkers have been discovered over the past few years using
proteomics techniques. Unfortunately, most of them are neither specific nor sensitive enough …

High-content, high-density long or short oligonucleotide microarrays for simultaneous
measurement of redundancy of RNA species are nowadays widely used for hypothesis …

Microarray data analysis has been shown to provide an effective tool for studying cancer
and genetic diseases. Although classical machine learning techniques have successfully …

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and
proteomic data. Cross-validation methods are often used to obtain estimates of classification …

Gastric cancer (GC) is one of the most common malignant tumors worldwide. No
fundamental improvements in the five‑year survival rates of patients with GC have been …

Background Machine learning is a powerful approach for describing and predicting classes
in microarray data. Although several comparative studies have investigated the relative …

Motivation Artificial intelligence, trained via machine learning (eg neural nets, random
forests) or computational statistical algorithms (eg support vector machines, ridge …

The rapid expansion of high dimensional “omic” technologies and their rapidly falling costs
have ushered in the era of precision medicine. However, large data sets are required for the …