Narrow Results

Progen and Avexa Agree to Merge for Research Synergies

Merial to Develop Vaccine Candidates Using Imugene's Vector Technology

Mesoblast Named as Emerging Company to Look Out For at 2009 Frost & Sullivan Award

Joint Laboratory by ProGenTech and China CDC

BIG Taps Life Technologies' SOLiD System to Advance Research in China

Thermo Fisher Scientific Launches Program to Meet Melamine Detection Challenges

The Sym006 Discovery Collaboration of Symphogen and Meiji Advanced to Preclinical Development

Astellas Enters License Agreement to Use Millipore's Technology

S*BIO and Tan Tock Seng Hospital to Assess New Therapeutic

Preclinical Translational Imaging Center Opened in Singapore

KKH Implements Rapid HIV Testing for Unbooked Pregnancies

Singapore's Parkway Chosen to Manage Abu Dhabi Hospital

With advanced imaging, sequencing, and profiling technologies, multiple omics data become increasingly available and hold promises for many healthcare applications such as cancer diagnosis and treatment. Multimodal learning for integrative multi-omics analysis can help researchers and practitioners gain deep insights into human diseases and improve clinical decisions. However, several challenges are hindering the development in this area, including the availability of easily accessible open-source tools. This survey aims to provide an up-to-date overview of the data challenges, fusion approaches, datasets, and software tools from several new perspectives. We identify and investigate various omics data challenges that can help us understand the field better. We categorize fusion approaches comprehensively to cover existing methods in this area. We collect existing open-source tools to facilitate their broader utilization and development. We explore a broad range of omics data modalities and a list of accessible datasets. Finally, we summarize future directions that can potentially address existing gaps and answer the pressing need to advance multimodal learning for multi-omics data analysis.

By using wireless mobile communications and mobile information terminals, Internet and the linking of computers and information technology to human bodies effectively, mobile computing can play an important role in modern technology that can be used by anybody anytime, anywhere, and can reconsider new technology with physiological measurements and reconstruct it creatively. In particular, mobile computing can intervene in the process of inducing biometric changes before diseased symptoms develop into diseases in an aging society. Nevertheless, there are difficulties, such as data, treatment by many parameters, ambiguity of data standardization and difficulty of the simultaneous collection of data etc. Therefore, in this study, a system was embodied by excluding time limiting factors using mobile computing and selecting the mobile neural dynamic coding method based on bioelectric signals. As a result of the experiment, it could be a model for biomedical Signal Mobile Analysis Devices and mobile biometric measuring devices, whose self-measurement can be made possible by an academic approach. Furthermore, it became the research foundation of the atypical characteristics of the formation of bioelectrical signals based on mobile applications and could be modeled in the structure circuit form of a biosignal.