Honghuang Lin

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Multi-functional enzymes are enzymes that perform multiple physiological functions. Characterization and identification of multi-functional enzymes are critical for communication and cooperation between different functions and pathways within a complex cellular system or between cells. In present study, we collected literature-reported 6,799(More)
BACKGROUND Although growing evidence from trials and population-based studies has supported a protective role for flavonoids in relation to risk of certain chronic diseases, the underlying mechanisms remain unclear. Several previous studies focused on individual inflammatory biomarkers, but because of the limited specificity of any individual marker, an(More)
UNLABELLED Bioinformatics involves the collection, organization and analysis of large amounts of biological data, using networks of computers and databases. Developing countries in the Asia-Pacific region are just moving into this new field of information-based biotechnology. However, the computational infrastructure and network bandwidths available in(More)
The emergence of digitally-intensive analog circuits introduces new challenges to formal verification due to increased digital design content, and non-ideal digital effects such as finite resolution, round-off error and overflow. We propose a machine learning approach to convert digital blocks to conservative analog approximations via the use of <i>kernel(More)
C-reactive protein (CRP) concentration is a heritable systemic marker of inflammation that is associated with cardiovascular disease risk. Genome-wide association studies have identified CRP-associated common variants associated in ∼25 genes. Our aims were to apply exome sequencing to (1) assess whether the candidate loci contain rare coding variants(More)
Atrial fibrillation (AF) is a morbid and heritable arrhythmia. Over 35 genes have been reported to underlie AF, most of which were described in small candidate gene association studies. Replication remains lacking for most, and therefore the contribution of coding variation to AF susceptibility remains poorly understood. We examined whole exome sequencing(More)
Leveraging machine learning has been proven as a promising avenue for addressing many practical circuit design and verification challenges. We demonstrate a novel active learning guided machine learning approach for characterizing circuit performance. When employed under the context of support vector machines, the proposed probabilistically weighted active(More)
As the cost of DNA sequencing decreases, association studies based on whole genome sequencing are now becoming feasible. It is still unclear, however, how much more we could gain from whole genome sequencing compared to exome sequencing, which has been widely used to study a variety of diseases. In this project, we performed a comparison between whole(More)