ISSN (Print): 1566-5240
ISSN (Online): 1875-5666
Volume 20, 10 Issues, 2020
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ISSN (Print): 1566-5240
ISSN (Online): 1875-5666
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Special Issue Submission
Therapeutic Advances in the Treatment of Liver Diseases
Guest Editor(s): Ravirajsinh Jadeja
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My experience working with Bentham Science Publishers was excellent and among the best I've had with publishing anywhere.
I worked with Ms. Samina Khan at Bentham. She was prompt to respond and her responses were always helpful. We have had a number of correspondences and she always worked things through to completion.
58 Articles Ahead of Print are available electronically
Computational medicine is an interdisciplinary field at the interface of computer science and medicine, where
computational approaches are developed to understand human diseases. Mathematical, informatics and
computational models are applied to provide insights into the mechanisms, diagnosis and treatment of diseases and
ultimately to improve patient care. Commonly applied areas include genomics, molecular biology, cellular biology
and epidemiology. In this issue, we present a number of computational medicine topics focused on moleculedisease
interactions, disease-drug interactions, cancer biology and population health by utilizing mathematical
modeling, big data analytics and deep learning.
The article titled “Hierarchical extension based on Boolean matrix for LncRNA-disease association prediction”
proposed a novel computational model that predicted new lncRNA-disease associations based on the property of
Boolean matrix from various relational sources.
Implementing computational models to explore lncRNA-disease associations is one of the hotspots nowadays.
Compared with the previous models, the newly proposed model showed better performance and identified potential
disease-associated LncRNA without known association data.
The article titled “A novel drug repositioning approach based on integrative multiple similarity measures”
proposed a drug repositioning approach, named DR_IMSM, to discover new usage indications for the existing
drugs. The approach consists of two parts: (1) a heterogeneous drug-disease network based on the drug-disease
interaction, shared diseases information for drug pairwise and shared drug information for diseases pairwise; and
(2) a deep learning method to capture the topology-based similarity for drug and disease. Compared to other
approaches, DR_IMSM outperformed and could potentially be used to improve the efficiency of drug research and
The article titled “Gene selection for the discrimination of colorectal cancer” proposed a feature selection
algorithm for discriminative genes from cancer and normal samples. This algorithm was designed particularly for
imbalanced class distribution between cancer and normal samples. The authors applied this algorithm on TCGA
and READ datasets. The results showed a list of genes that can be used to discriminate colon cancer. Finally, the
authors found several potential biomarkers for colorectal cancers and validated the selected feature gene sets using
independent datasets from the Coremine Medical online database.
The article titled “Association between single-nucleotide polymorphisms of RXRG and genetic susceptibility to
Type 2 diabetes in South China” used the genome-wide association study (GWAS) approach to analyze genetic
susceptibility for type 2 diabetes. Type 2 diabetes and its complications have been considered as a major public
health problem worldwide. The authors identified that RXRG rs3753898 in the adiponectin signaling pathway was
significantly associated with the genetic susceptibility of type 2 diabetes in the Guangdong Han population.
The article titled “Convolutional neural network visualization for identification of risk genes in bipolar disorder”
proposed a convolutional neural network to predict bipolar disorder (BD) based on GWAS data. After the
identification of BD, the authors further used gradient weighted class activation mapping (Grad-CAM) to interpret
the prediction model, indirectly to identify high-risk SNPs of BD. Finally, the authors found that 22 out of 137
predicted risk genes were previously reported to be associated with BD. This method could also be applied to
identify risk genes from other GWAS datasets.
In summary, these computational medicine topics show how the development of computational models is
necessary for providing insights into the inherent complexity of coupled nonlinear biological systems, and therefore,
how it achieves a quantitative understanding of the structure and function in health and disease.
Sumoylation, a protein posttranslation modification was discovered about two decades ago. This process is
catalyzed by 3 enzymes: the activating enzyme E1 (SAE1/UBA2), the unique conjugating enzyme E2 (UBC9), and
the ligating enzymes E3 (many types such as PIAS1 and RanBP2). The conjugated target proteins are
deconjugated by the SUMO isopeptidase protease (SENPs). It is now approved to be a very important regulatory
mechanism that controls many cellular processes including DNA replication and repair, chromatin structure and
dynamics, gene expression and regulation, cell proliferation and differentiation, cell transformation and neural
transmission, cell autophagy and senescence, apoptosis and necroptosis [1-16]. It is also implicated in various
human diseases such as cardiovascular diseases, neural degeneration, and cancer development [1-16]. In the
vision system, studies from several laboratories including ours reveal that sumoylation acts as a critical regulatory
mechanism controlling eye development [17-21]. In this special issue, we have characterized the expression and
localization patterns of both sumoylation ligases and de-conjugation enzymes in major ocular tissues and cell lines.
We have also begun to explore their potential roles in pathogenesis of major ocular diseases.
The first article by Qian Nie et al. analyzed the expression levels of 5 sumoylation enzymes: SAE1, UBA2,
UBC9, PIAS1 and RanGAP1, and established the differential expression patterns of these liagses in 5 major ocular
cell lines. Their results revealed while the mRNAs for the 5 sumoylation enzymes varied significantly from cell line to
cell line, the protein expression level remained relatively similar for SAE1, UBA2 and UBC9 in different ocular cell
lines. For ligase 3, different cell lines have cell-specific expression of each ligase 3.
The article by Xiaodong Gong et al. analyzed the localization of 5 sumoylation enzymes in the 5 major ocular cell
lines, and determined the localization patterns of each enzyme in these cell lines. Their results revealed that
sumoylation enzymes SAE1, UBC9 and PIAS1 were distributed in both nucleus and cytoplasm, with a much higher
level concentrated in the nucleus and the neighboring cellular organelle zone in all cell lines; the sumoylation
enzyme UBA2 was highly concentrated in the cytoplasm membrane, cytoskeleton and nucleus of all cell lines; and
the ligase E3, RanBP2 was exclusively localized in the nucleus with homogeneous distribution.
The article by Yunfei Liu et al. analyzed the localization of 7 SENPs, and established their differential localization
patterns in 5 major ocular cell lines. Their results revealed that SENP3 was almost exclusively localized in the nuclei
of all ocular cell lines except for rabbit lens epithelia cells. The remaining SENP1, 2, 5, 6 and 7 were localized in
both cytoplasm and nucleus with its distribution varying in different ocular cell lines. In addition, they found that
SENP8 was only expressed in human cell lines.
The article by Jiawen Xiang et al. analyzed the expression levels of 7 SENPs in 4 major ocular tissues, and
established the differential expression patterns of these SENPs in the above ocular tissues. Their results revealed
that all 7 SENPs were predominantly expressed at mRNA level in the retina, with much reduced mRNA expression
levels in cornea and lens tissues. The proteins for seven SENPs are almost absent in lens fiber cells of the mouse
eye. SENP1 to 3, SENP6 and SENP8 were clearly detectable in LEC. SENP1 to 3, and SENP6 were strongly
expressed in cornea, but substantially reduced in LEC and retina. The proteins for SENP5, SENP7 and SENP8
were highly expressed in the retina, but reduced in the cornea.
Article 5 by Qian Nie et al. examined the glucose oxidase (GO) and UVA-induced cataract animal models, and
found that the expression levels of 5 sumoylation enzymes were significantly altered. At the mRNA level, GO
treatment downregulated the mRNA levels of all 5 enzymes, but UVA irradiation lead to upregulation of 4 out 5
enzymes. At the protein level, both GO and UVA induced significant down-regulation of the 5 sumoylation enzymes.
Article 6 by Qian Nie et al. analyzed the expression changes of 5 ligases in sodium iodide induced animal model
of aging-related macular degeneration(AMD), and found significantly declined RNA levels of E1, E2 and E3 ligase
PIAS1 in NaIO3-injected mouse RPE one day-post treatment. Consistently, the protein level of PIAS1 was also
decreased at this time point. At the late stage of treatment (three days post-injection), significantly reduced
expression of E1 enzyme SAE1/UBA2 was detected in NaIO3-injected mouse retinas. In the contrary, dramatically
increased E3 ligase RanBP2 was found in the injected-retinas. Together, these results demonstrated for the first
time the dynamic expression of sumoylation pathway enzymes during the progression of retina degeneration
induced by oxidative stress.
The article by Xiangcheng Tang et al. examined the role of the sumoylation-regulated transcription factor, p53 in
eye lens differentiation. Their results revealed that p53 can regulate the cell cycle checkpoint genes p21 and
Gadd45α to mediate lens differentiation. Moreover, their work suggests that both protein kinases CHK1/2 and
ERK1/2 and protein phosphatase PP-1 may regulate the phosphorylation status of p53 during mouse eye
The last article by Fangyuan Liu et al. analyzed, for the first time, the non-sumoylation and sumoylation isoforms
of Pax-6 in 5 major ocular cell lines. Their results revealed that Pax-6 exists in 7 isoforms: p32, p43SP, p43SU, p46, resources, one from alternative splicing (p43SP), and the other sumoylation (p43SU). Thus, p32, p43SP, p46 and p48
are non-sumoylated isoforms, and p43SU, p57, and p68 are sumoylated isoforms.
Together, these articles established the differentiation expression and localization patterns of both sumoylation
ligases and de-conjugation enzymes in major ocular tissues and ocular cell lines. These results lay down an
important foundation for further exploration of sumoylation functions in ocular development. The articles in the
present volume also examined the altered expression patterns of both sumoylation ligases and de-conjugation
enzymes in stress-induced animal models of major ocular diseases, both cataract and AMD. The results from these
articles showed that sumoylation is linked to pathogenesis of major ocular diseases. These studies broaden our
understanding of aspects in the molecular medicine, especially ocular physiology and pathology.
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