Multi-omics data analysis is an important aspect of cancer molecular biology studies and
has led to ground-breaking discoveries. Many efforts have been made to develop machine …

Patient similarity networks (PSNs), where patients are represented as nodes and their
similarities as weighted edges, are being increasingly used in clinical research. These …

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

With the emergence of the big data age, the issue of how to obtain valuable knowledge from
a dataset efficiently and accurately has attracted increasingly attention from both academia …

Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …

Spectral clustering makes use of spectral-graph structure of an affinity matrix to partition data
into disjoint meaningful groups. Because of its elegance, efficiency and good performance …

Motivation: Despite ongoing cancer research, available therapies are still limited in quantity
and effectiveness, and making treatment decisions for individual patients remains a hard …

In complex pattern recognition tasks, objects are typically characterized by means of
multimodality attributes, including categorical, numerical, text, image, audio, and even …

Dimensionality reduction aims to map the high-dimensional inputs onto a low-dimensional
subspace, in which the similar points are close to each other and vice versa. In this paper …

Heterogeneous feature representations are widely used in machine learning and pattern
recognition, especially for multimedia analysis. The multi-modal, often also high …