Driven by high-throughput sequencing techniques, modern genomic and clinical studies are
in a strong need of integrative machine learning models for better use of vast volumes of …

Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …

The rapidly increasing availability of electronic health records (EHRs) from multiple
heterogeneous sources has spearheaded the adoption of data-driven approaches for …

Precision medicine initiatives come amid the rapid growth in quantity and variety of
biomedical data, which exceeds the capacity of matrix-oriented data representations and …

Background High-throughput genomic and proteomic data have important applications in
medicine including prevention, diagnosis, treatment, and prognosis of diseases, and …

Excessive smartphone use causes personal and social problems. To address this issue, we
sought to derive usage patterns that were directly correlated with smartphone dependence …

A non-negative least squares classifier is proposed in this paper for classifying under-
complete data. The idea is that unknown samples can be approximated by sparse non …

The 3D microarrays, generally known as gene-sample-time microarrays, couple the
information on different time points collected by 2D microarrays that measure gene …

Modern data generated in many fields are in a strong need of integrative machine learning
models in order to better make use of heterogeneous information in decision making and …

Microarray data can be used to detect diseases and predict responses to therapies through
classification models. However, the high dimensionality and low sample size of such data …