Hai-Hui Huang

Shaoguan University | SGU · Computer Science

Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In this paper, we presented a new hybrid L1/2 +2 regularization (HLR) function, a linear combination of L1/2 and L2 p...

N-linked glycans on immunoglobulin G (IgG) have been associated with pathogenesis of diseases and the therapeutic functions of antibody-based drugs; however, low-abundance species are difficult to detect. Here we show a glycomic approach to detect these species on human IgGs using a specialized microfluidic chip. We discover 20 sulfated and 4 acety...

Background and objective: An important issue in genomic research is to identify the significant genes that related to survival from tens of thousands of genes. Although Cox proportional hazards model is a conventional survival analysis method, it does not induce the gene selection. Methods: In this paper, we extend the hybrid L1/2 + 2 regulariza...

Background Few proposed gene biomarkers have been satisfactory in clinical applications. That is mainly due to the small studies sample size. Because of the batch effect, different gene-expression studies cannot be merged directly. Many integrative methods have attempted to integrate various datasets to eliminate the batch effect while keeping biol...

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the cox model. This strategy i...

Multi-omics data integration is a promising field combining various types of omics data, such as genomics, transcriptomics, and proteomics, to comprehensively understand the molecular mechanisms underlying life and disease. However, the inherent noise, heterogeneity, and high dimensionality of multi-omics data present challenges for existing method...

Background Gene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets. Results Meta-analysis of multi-data sets is an efficient...

In epigenome-wide association studies (EWAS), the mixed methylation expression caused by the combination of different cell types may lead the researchers to find the false methylation site related to the phenotype of interest. To correct the EWAS false discovery, some non-reference models based on sparse principal component analysis (sparse PCA) ha...

Cache replacement policy (CRP) in content-centric network (CCN) can reduce cache redundancy, optimize cache utility, and improve network performance. When assessing the CRPs in CCN, it is often full of great uncertainty. Set pair analysis (SPA) is a pioneering uncertainty theory, which consists of three components of the connection number (CN), and...

One of the central tasks of genome research is to predict phenotypes and discover some important gene biomarkers. However, there are three main problems in analyzing genomics data to predict phenotypes and gene marker selection. Such as large p and small n, low reproducibility of the selected biomarkers, and high noise. To provide a unified solutio...

Background: Targeted therapy using anti-TNF is the first option for patients with RA. Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning. Objective: Although various effor...

Background: Chronic obstructive pulmonary disease (COPD) causes chronic obstructive conditions, chronic bronchitis, and emphysema, and is a major cause of death worldwide. Although several efforts for identifying biomarkers and pathways have been made, specific causal COPD mechanism remains unknown. Objective: This study combined biological inte...

In genome research, it is a fundamental issue to identify few but important survival‐related biomarkers. The Cox model is a widely used survival analysis technique, which is used to study the relationship between characteristics and survival response. However, limitations of the existing Cox methods for genomic data are as follows: (1) a typical ge...

Background: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable...

Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q‐rung orthopair fuzzy set (q‐ROFS), characterized by membership and nonmembership degrees with adjustable parameter q, is quite a high‐efficienc...

Background: In epigenome-wide association studies (EWAS), the mixed methylation expression caused by the combination of different cell types may lead the researchers to find the false methylation site related to the phenotype of interest. In order to fix this problem, researchers have proposed some non-reference methods based on sparse principle co...

The heterogeneity of cancer reflects the complexity of genetic mutations. Dissecting the heterogeneity plays an important role in the field of biomarker discovery, targeted therapy and drug designing. As it is time-consuming to identify new biomarkers in biological experiments, various machine learning methods have been developed. However, the curr...

The financial risk evaluation is critically vital for enterprises to identify the potential financial risks, provide decision basis for financial risk management, and prevent and reduce risk losses. In the case of considering financial risk assessment, the basic problems that arise are related to strong fuzziness, ambiguity and inaccuracy. q-rung o...

Blood-Brain-Barrier (BBB) is a strict permeability barrier for maintaining the Central Nervous System (CNS) homeostasis. One of the most important conditions to judge a CNS drug is to figure out whether it has BBB permeability or not. In the past 20 years, the existing prediction approaches are usually based on the data of the physical characterist...

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic re...

To identify the bio-mark genes related to disease with high dimension and low sample size gene expression data, various regression approaches with different regularization methods have been proposed to solve this problem. Nevertheless, high-noises in biological data significantly reduce the performances of methods. The accelerated failure time (AFT...

The most frequently selected 10 genes information. Top-10 ranked genes selected by all the methods for prostate and lymphoma datasets. (PDF)

The proof of theorem 1. (PDF)

Tuberculosis (TB), caused by infection with mycobacterium tuberculosis, is still a major threat to human health worldwide. Current diagnostic methods encounter some limitations, such as sample collection problem or unsatisfied sensitivity and specificity issue. Moreover, it is hard to identify TB from some of other lung diseases without invasive bi...

Identifying biomarker and signaling pathway is a critical step in genomic studies, in which the regularization method is a widely used feature extraction approach. However, most of the regularizers are based on L 1-norm and their results are not good enough for sparsity and interpretation and are asymptotically biased, especially in genomic researc...

“Sub-networks identified by the L_1 net and the Elastic net for lung cancer datasets (only those genes that are linked on the PPI network are plotted). Nodes colored based on higher (red) to lower (green) coefficients in the model.”