Data analysis is a very challenging task in LC-MS metabolomic studies. The use of powerful
analytical techniques (eg, high-resolution mass spectrometry) provides high-dimensional …

Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …

Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has
been very successful in treatment of melanoma. However, only a subset of patients with …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

A number of supervised machine learning models have recently been introduced for the
prediction of drug–target interactions based on chemical structure and genomic sequence …

Background Selection of relevant genes for sample classification is a common task in most
gene expression studies, where researchers try to identify the smallest possible set of genes …

Background Cross-validation (CV) is an effective method for estimating the prediction error
of a classifier. Some recent articles have proposed methods for optimizing classifiers by …

Background Current clinical therapy of non-small cell lung cancer depends on histo-
pathological classification. This approach poorly predicts clinical outcome for individual …

Motivation: In genomic studies, thousands of features are collected on relatively few
samples. One of the goals of these studies is to build classifiers to predict the outcome of …

Feature selection can improve the accuracy of machine-learning models, but appropriate
steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common …