Computational biology in topical bioactive peptide discovery for cosmeceutical application:a concise review

Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair ingredients.With the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been improved.Some application cases have appeared in topical skin regeneration and repair scenarios.This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I.based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios.Typically,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.

Integer Programming Models for Computational Biology Problems

The recent years have seen an impressive increase in the use of IntegerProgramming models for the solution of optimization problems originating in Molecular Biology. Inthis survey, some of the most successful Integer Programming approaches are described, while a broadoverview of application areas being is given in modern Computational Molecular Biology.

Computational Biology Approaches to Plant Metabolism and Photosynthesis:Applications for Corals in Times of Climate Change and Environmental Stress

Knowledge of factors that are important in reef resilience helps us to understand how reef ecosystems react following major anthropogenic and environmental disturbances. The symbiotic relationship between the photosynthetic zooxanthellae algal cells and corals is that the zooxanthellae provide the coral with carbon, while the coral provides protection and access to enough light for the zooxanthellae to photosynthesise. This article reviews some recent advances in computational biology relevant to photosynthetic organisms, including Beyesian approaches to kinetics, computational methods for flux balances in metabolic processes, and determination of clades of zooxanthallae. Application of these systems will be important in the conservation of coral reefs in times of climate change and environmental stress.

Computational Biology Study of S100 Family with Suggestions for Crystallization

The S100 family is a class of calcium regulated proteins with EF hand. They are widely distributed and are implicated in diverse intracellular and extracellular physiological processes. A study of the S100 family using computational biology methods such as multiple sequence alignment, structural alignment and the construction of an evolutionary tree will promote understanding of S100 protein structures and their function, and could provide suggestions for crystallization.

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process.These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches.Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics.Protein modeling methods attempt to accurately predict three-dimensional(3D)structures of uncrystallized proteins for subsequent structure-based drug design applications.Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures.In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics.Selected drug discovery applications using these methods conclude the review.

Guest Editorial: Special Issue on Bioinformatics and Computational Biology

Bioinformatics and computational biology research is fundamental to our understanding of complex biological systems,impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences.It is one of the fastest developing research fields in the last two decades.High throughput biological data that are used to provide information at molecular and genetic level are rapidly generated.Almost all research problems in biological and medical sciences nowadays are computationally hard.Computational techniques

Call for Papers Special Issue on Bioinformatics and Computational Biology

Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by EI and other abstracting indexes. From 2012, the journal enters into IEEE Xplore Digital Library and all papers published there are freely downloadable.

Call for Papers Special Issue on Bioinformatics and Computational Biology

The publication of Tsinghua Science and Technology was started in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by EI and other abstracting indexes. From 2012, the journal enters into IEEE Xplore Digital Library and all papers published there are freely downloadable.

Call for Papers Special Issue of Tsinghua Science and Technology on Bioinformatics and Computational Biology

Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by Ei and other abstracting indexes. From 2012the journal enters into IEEE Xplore Digital Library.

Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

Decoding Retinoblastoma: Differential Gene Expression

Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.

Bioinformatics Identification of ZNFs/LINC00520/miR-181d/BCL2 Axis as a Novel Network in Cisplatin-Resistant Lung Adenocarcinoma Cells

Background: Resistance to cisplatin (DDP) leads to poor prognosis in patients with Lung Adenocarcinoma (LUAD) and limits its clinical application. It has been confirmed that autophagy promotes chemoresistance and, therefore, novel strategies to reverse chemoresistance by regulating autophagy are desperately needed. Methods: The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between A549 and A549/DDP cell lines were identified using the limma package in R, after gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. By combining Autophagy-Related Genes (ARGs) from Human Autophagy Database (HADb), the interactions lncRNA-miRNAs and the interactions miRNAs-mRNAs respectively predicted by miRcode and miRDB/Targetscan database, the autophagy-related ceRNA network was constructed. Then, extraction of ceRNA subnetwork and Cox regression analyses were performed. A prognosis-related ceRNA subnetwork was constructed, and the upstream Transcription Factors (TFs) regulating lncRNAs were predicted by the JASPAR database. Finally, the expression patterns of candidate genes were further verified by quantitative real-time polymerase chain reaction (qRT-PCR) experiments. Results: A total of 3179 DEmRNAs, 180 DEmiRNAs, and 160 DElncRNAs were identified, and 35 DEmRNAs were contained in the HADb. Based on the ceRNA hypothesis, we established a ceRNA network, including 10 autophagy-related DEmRNAs, 9 DEmiRNAs, and 14 DElncRNAs. Then, LINC00520, miR-181d, and BCL2 were identified to construct a risk score model, which was confirmed to be a well-predicting prognostic factor. Furthermore, 5 TF ZNF family members were predicted to regulate LINC00520, whereas the RT-PCR results showed that the 5 ZNFs were consistent with the bioinformatics analysis. Finally, a ZNF regulatory LINC00520/miR-181d/BCL2 ceRNA subnetwork was constructed. Conclusions: An ZNFs/LINC00520/miR-181d/BCL2 axis as a novel network in DDP-resistant LUAD has been constructed successfully, which may provide potential therapeutic targets for LUAD.

Establishment of a prognostic model related to tregs and natural killer cells infiltration in bladder cancer

BACKGROUND Regulatory T cells(Tregs)and natural killer(NK)cells play an essential role in the development of bladder urothelial carcinoma(BUC).AIM To construct a prognosis-related model to judge the prognosis of patients with bladder cancer,meanwhile,predict the sensitivity of patients to chemotherapy and immunotherapy.METHODS Bladder cancer information data was obtained from The Cancer Genome Atlas and GSE32894.The CIBERSORT was used to calculate the immune score of each sample.Weighted gene co-expression network analysis was used to find genes that will have the same or similar expression patterns.Subsequently,multivariate cox regression and lasso regression was used to further screen prognosis-related genes.The prrophetic package was used to predict phenotype from gene expression data,drug sensitivity of external cell line and predict clinical data.RESULTS The stage and risk scores are independent prognostic factors in patients with BUC.Mutations in FGFR3 lead to an increase in Tregs percolation and affect the prognosis of the tumor,and additionally,EMP1,TCHH and CNTNAP3B in the model are mainly positively correlated with the expression of immune checkpoints,while CMTM8,SORT1 and IQSEC1 are negatively correlated with immune checkpoints and the high-risk group had higher sensitivity to chemotherapy drugs.CONCLUSION Prognosis-related models of bladder tumor patients,based on Treg and NK cell percolation in tumor tissue.In addition to judging the prognosis of patients with bladder cancer,it can also predict the sensitivity of patients to chemotherapy and immunotherapy.At the same time,patients were divided into high and low risk groups based on this model,and differences in genetic mutations were found between the high and low risk groups.

Construction and imaging of a neurovascular unit model

In 2001,the concept of the neurovascular unit was introduced at the Stroke Progress Review Group meeting.The neurovascular unit is an important element of the health and disease status of blood vessels and nerves in the central nervous system.Since then,the neurovascular unit has attracted increasing interest from research teams,who have contributed greatly to the prevention,treatment,and prognosis of stroke and neurodegenerative diseases.However,additional research is needed to establish an efficient,low-cost,and low-energy in vitro model of the neurovascular unit,as well as enable noninvasive observation of neurovascular units in vivo and in vitro.In this review,we first summarize the composition of neurovascular units,then investigate the efficacy of different types of stem cells and cell culture methods in the construction of neurovascular unit models,and finally assess the progress of imaging methods used to observe neurovascular units in recent years and their positive role in the monitoring and investigation of the mechanisms of a variety of central nervous system diseases.

Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi-core sharing technology

Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of computations.Accordingly,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments.This research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing capabilities.The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment.The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit situations.The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting rate.The benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.

Semantic Similarity between Ontologies at Different Scales

In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims(plant,yeast, and candida, among which the latter two belong to the same kingdom — fungi) using four popular measures commonly applied to biomedical ontologies(Resnik, Lin, Jiang-Conrath,and Sim Rel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of JiangConrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar(as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales.This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.

RSMD-repeat searcher and motif detector

The functionality of a gene or a protein depends on codon repeats occurring in it.As a consequence of their vitality in protein function and apparent involvement in causing diseases,an interest in these repeats has developed in recent years.The analysis of genomic and proteomic sequences to identify such repeats requires some algorithmic support from informatics level.Here,we proposed an offline stand-alone toolkit Repeat Searcher and Motif Detector（RSMD）,which uncovers and employs few novel approaches in identification of sequence repeats and motifs to understand their functionality in sequence level and their disease causing tendency.The tool offers various features such as identifying motifs,repeats and identification of disease causing repeats.RSMD was designed to provide an easily understandable graphical user interface（GUI）,for the tool will be predominantly accessed by biologists and various researchers in all platforms of life science.GUI was developed using the scripting language Perl and its graphical module PerlTK.RSMD covers algorithmic foundations of computational biology by combining theory with practice.

Computational Molecular Biology(CMB)

Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes

Computational Molecular Biology(CMB)

Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes

Computational Molecular Biology(CMB)

Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,