Journal of bioinformatics and systems biology : Open access最新文献

{"title":"NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.","authors":"Ted Liefeld, Edwin Huang, Alexander T Wenzel, Kenneth Yoshimoto, Ashwyn K Sharma, Jason K Sicklick, Jill P Mesirov, Michael Reich","doi":"10.26502/jbsb.5107072","DOIUrl":"10.26502/jbsb.5107072","url":null,"abstract":"<p><p>Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible <i>in silico</i> research for non-programmers.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"6 4","pages":"379-383"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conservation Genomics and Species Distribution Models Motivate Proactive and Collaborative Conservation in an Era of Rapid Change","authors":"Maria Sagatelova, Rosa A Rodriguez- Pena, Thomas J Rodhouse, Jeffrey Lonneker, Kirk R Sherrill, Andrea D Wolfe","doi":"10.26502/jbsb.5107063","DOIUrl":"https://doi.org/10.26502/jbsb.5107063","url":null,"abstract":"Small, fragmented plant populations with low genetic diversity are susceptible to deterministic and stochastic events that can affect long-term persistence of species. Penstemon lemhiensis Keck (Plantaginaceae) is a rare endemic with small, scattered populations across Idaho and Montana threatened by cumulative impacts of biological invasion, drought, and altered fire regimes. When contextualized by an understanding of rangewide distributions under different environmental change scenarios, conservation genetics can be leveraged to motivate proactive conservation action among collaborating stakeholder groups. We applied a genotypingby- sequencing (GBS) approach across eight populations and 93 individuals of P. lemhiensis. Genetic differentiation among populations followed an isolation-by-distance pattern and ranged from low to moderate (FST = 0.095-0.280). Values of inbreeding were low, and often negative (FIS = -0.039-0.032), indicating outbreeding within populations. Population structure analyses identified six ancestral populations and admixture across all individuals. We contextualized these findings by fitting bioclimatic niche models to past, present, and future climate regime scenarios. Habitat connectivity peaked mid-Holocene and nearly disappeared in the future scenario. Genetic analyses and species distribution models indicated that the species may experience drastic range contraction and accelerated isolation and inbreeding in future. We identified a core area in the Upper Big Hole Valley, Montana most likely to persist as suitable habitat. The National Park Service, Bureau of Land Management, and US Forest Service were identified as key stakeholders in that valley. We outline a proactive collaborative conservation strategy that aim to maintain wild P. lemhiensis populations.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135952823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acyclovir Improves the Efficacy of Chemoradiation in Nasopharyngeal Cancer Containing the Epstein Barr Virus Genome","authors":"Aditya Thandoni, Andrew Zloza, Devora Schiff, Malay Rao, Kwok-wai Lo, Bruce G Haffty, Sung Kim, Sachin R Jhawar","doi":"10.26502/jbsb.5107064","DOIUrl":"https://doi.org/10.26502/jbsb.5107064","url":null,"abstract":"Nasopharyngeal carcinoma (NPC) is a malignancy endemic to East Asia and is caused by Epstein-Barr Virus (EBV)-mediated cancerous transformation of epithelial cells. The standard of care treatment for NPC involves radiation and chemotherapy. While treatment outcomes continue to improve, up to 50% of patients can be expected to recur by five years, and additional innovative treatment options are needed. We posit that a potential way to do this is by targeting the underlying cause of malignant transformation, namely EBV. One method by which EBV escapes immune surveillance is by undergoing latent phase replication, during which EBV expression of immunogenic proteins is reduced. However, chemoradiation is known to drive conversion of EBV from a latent to a lytic phase. This creates an opportunity for the targeting of EBV-infected cells utilizing antiviral drugs. Indeed, we found that combining acyclovir with cisplatin and radiation significantly decreases the viability of the EBV-infected C666- 1 cell line. Western blot quantification revealed a resultant increase of thymidine kinase (TK) and apoptosis-inducing mediators, cleaved PARP (cPARP) and phosphorylated ERK (pERK). These studies suggest that the addition of anti-viral drugs to frontline chemoradiation may improve outcomes in patients treated for EBV-related NPC and future in vivo and clinical studies are needed.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135954929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning to identify microRNA biomarkers for predisposition to Huntington’s Disease","authors":"P. K, Sheridan C, Chandrasegaran S, Shanley Dp","doi":"10.26502/jbsb.5107046","DOIUrl":"https://doi.org/10.26502/jbsb.5107046","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDBPR: Latest Database of Protein Research","authors":"Shahid Ullah, Tianshun Gao, W. Rahman, F. Ullah, R. Jahan, Anees Ullah, Gulzar Ahmad, Muhammad Ijaz, Yihang Pan","doi":"10.26502/jbsb.5107032","DOIUrl":"https://doi.org/10.26502/jbsb.5107032","url":null,"abstract":"With the vast and rapid growth of protein research data, a large number of databases are produced to annotate proteins. How to use these databases is becoming a crucial part of modern biology. Database research is usually the first step in the analysis of a new protein. The combined utilization of multiple databases could help researchers to understand the evolution, structure, and function of proteins. Therefore, a well comprehensive and large-scale resource integrated with most of databases is urgently desirable for systematic and precise studies of proteins. Here we designed a platform LDBPR with a collection of 564 latest scientific protein databases. It fully covered physical, chemical, and biological information of Protein sequence, structure, and model, domain, function, and protein‐ protein interactions. Furthermore, The LDBPR can be explored by three ways: (i) single database can be browsed by typing the name in the given search bar; (ii) all protein categories can be browsed by clicking on the name of the category; (iii) the image icon, could give all categorized protein databases on single click. Moreover, the programming languages including PHP, HTML, CSS, and MySQL were used to construct LDBPR for the protein scientific community that can be freely searched by clicking http://www.habdsk.org/ldbpr.php and will be updated timely.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptome Dedifferentiation Observed in Animal Primary Cultures is Essential to Plant Reprogramming.","authors":"Norichika Ogata","doi":"10.26502/jbsb.5107039","DOIUrl":"https://doi.org/10.26502/jbsb.5107039","url":null,"abstract":"<p><p>Tissue culture environment liberate cells from ordinary laws of multi-cellular organisms. This liberation enables cells several behaviors, such as growth, dedifferentiation, acquisition of pluripotency, immortalization and reprogramming. Each phenomenon is relating to each other and hardly to determine. Recently, dedifferentiation of animal cell was quantified as increasing liberality which is information entropy of transcriptome. The increasing liberality induced by tissue culture may reappear in plant cells too. Here we corroborated it. Measuring liberality during reprogramming of plant cells suggested that reprogramming is a combined phenomenon of dedifferentiation and re-differentiation.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":" ","pages":"116-118"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40483821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-Aided Molecular Design of CCR2 - CCR5 Dual Antagonists for the Treatment of NASH","authors":"S. Kumari, Elizabeth Sobhia M","doi":"10.26502/jbsb.5107035","DOIUrl":"https://doi.org/10.26502/jbsb.5107035","url":null,"abstract":"Non-alcoholic fatty liver disease (NAFLD), one of the most common liver diseases, is caused by the disruption of hepatic lipid homeostasis by various metabolic disorders. The progression of NAFLD into Non-alcoholic steatohepatitis (NASH) is mediated by inflammatory chemokines, cytokines, mitochondrial dysfunction, and oxidative stress resulting in hepatocyte inflammation, ballooning, apoptosis, and activation of hepatic stellate cells (HSC). NASH can further lead to cirrhosis, hepatic carcinoma, and also it is predicted to be a major cause of liver transplantation over the next 10 years. Chemokine receptors are majorly involved in recruiting the monocytes in the liver where they are converted into pro-inflammatory macrophages, which further activate the hepatic stellate cell (HSCs) to promote their survival while activating collagen production and fibrogenesis. Thus, chemokines and their receptor play a vital role in the pathogenesis of NASH and can be a potential target for the treatment of NASH. Herein, in this study, we have carried out a structure-based design of CCR2 and CCR5 dual antagonists. We performed pharmacophore mapping studies followed by virtual screening of commercial database to obtain novel molecules which can potentially act as CCR2 and CCR5 dual antagonists. We also performed molecular docking studies of newly obtained hits molecules to see their interactions with both CCR2 and CCR5 receptors. Non-alcoholic By evaluating the chemical structures of the top five molecules, it was observed that all five molecules possess C2 symmetry. The docking results of the top five molecules showed that Thr284, Trp86, Tyr89, and Glu283 (in CCR5) and Asp283, Val37, Asn286, His202, and Gln288 (in CCR2) residues were involved in hydrogen bond interactions. The molecules also showed π-π stacking interaction with key residues Phe112, Tyr108, Phe109, Trp86, and Tyr89 (in CCR5) and HIE121, Trp98, Tyr120 (in CCR2). Additionally, in some molecule’s halogen bond was also observed. The residues which formed the halogen bond include Phe182, Thr195 (in CCR5), and Lys38 (in CCR2). The screened molecules showed the interactions with some key residues i.e., Phe112, Tyr108, Phe109 (in CCR5) and Trp98, Tyr120 (in CCR2) as same that of CVC interactions with CCR5 and CCR2 receptor.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics Analysis Identifies NDRG1 Gene Variants that may be Clinically Relevant","authors":"Borré Gb, Pimenta At, Chmieleski Gs, Moyses Gr, Souza Scb, Rabi Lt, Peres Kc, Teixeira Es, Bufalo Ne, Ward Ls","doi":"10.26502/jbsb.5107043","DOIUrl":"https://doi.org/10.26502/jbsb.5107043","url":null,"abstract":"Background: The search of single nucleotide variants that might have the capacity to alter genetic information and influence in regular cellular pathways, enhancing expansion, mitosis and evasion capacity to neoplasm cells, is central in understanding the molecular nature of distinct cellular growth abnormalities and is critical because it might expose new possibilities for therapeutic targets. The expression of NDRG1 protein, encoded by NDRG1 gene, has already been correlated with tumor progression and evasion, but information on different types of neoplasm is still contentious. Objective: To explore probable correlations of susceptibleness, progression and clinical characteristics between NDRG1 gene polymorphisms (SNPs) and patients that developed thyroid tumors. Methods: SNPs were obtained from the NCBI dbSNP. The encoded protein primary sequences were got from the UniProt database. We employed the three FASTA primary sequences to analyze the amino acid changes. The bioinformatics tools used were: PredictSNP1.0 (which encompasses: PANTHER, SNAP, PolyPhen-1, PhD-SNP, nsSNPAnalyze, SIFT, PredictSNP, PolyPhen-2, MAPP,); I-Mutant2.0; MUpro; PROVEAN; Haploview and SNPs3D). Results: The NCB database reports 319 missense SNPs in the NDRG1 gene. The SIFT tool predicted that 51 nsSNPs of 109 (which means 46.78%) were deleterious; the SNAP tool predicted nearly 30%; PolyPhen-2, 53 (48.62%); 52 (47.70%) derived from PhD-SNP; PolyPhen-1 indicated 38 nsSNPs (approximately 35%); and MAPP showed 47 (which is 43%). Finally, the PredictSNP toll contemplated 13 (approximately 12%) nsSNPs deleterious by all integrated tools, including rs201348291 and rs15132213, whose scores were the most significant, thus indicating a higher possibility that these SNPs are correlated and influence the pathophysiology of thyroid neoplasm. Conclusions: We demonstrated that NDRG1 rs201348291 and rs151322132 may be involved in thyroid cancer emergence and deserve further validation and evaluation of their clinical applicability in determining the risk of thyroid nodules malignancy and thyroid cancer prognostic.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Secondary Transcriptomic Analysis of Non-Hodgkin's B-Cell Lymphomas Predicts Reliance on Pathways Associated with the Extracellular Matrix and Robust Diagnostic Biomarkers.","authors":"Naomi Rapier-Sharman, Jeffrey Clancy, Brett E Pickett","doi":"10.26502/jbsb.5107040","DOIUrl":"10.26502/jbsb.5107040","url":null,"abstract":"<p><p>Approximately 450,000 cases of Non-Hodgkin's lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin's Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"5 4","pages":"119-135"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9410763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Silico Evaluation of Biopharmaceutical Properties of Chloramphenicol Derivatives and their Iron Complexes","authors":"Kananda Masonga Michel, Lumbwe Kitenge Edouard, Kayembe Kazadi Oscar, Mbayo Kitambala Marsi, Kalonda Mutombo Emery","doi":"10.26502/jbsb.5107033","DOIUrl":"https://doi.org/10.26502/jbsb.5107033","url":null,"abstract":"Evaluation of Biopharmaceutical Properties of Chloramphenicol Derivatives and their Complexes. Abstract Context and The use of chloramphenicol (CAM) has been reduced due to the side effects associated with its use (Bone marrow depression, neurotoxicity) and the increase in resistance to CAM that some microbes develop. To overcome these difficulties, two CAM derivatives, L1 and L2, and their respective iron complexes were synthesized to evaluate in silico their biopharmaceutical properties. The substrate (CAM), as well as the basic reagents (AAP and AASC) were purified from commercial pharmaceuticals. The CAM derivatives (L1 and L2) and also their iron complexes (C1, C2, and C3) were synthesized and showed maximum absorbance at 335 nm for CAM, 325 nm for L1, 395 nm for L2, at 330 nm for C1, at 325 nm for C2, and at 335 nm for C3. The in silico simulations performed with the above-mentioned tools showed that all the ligands (CAM, L1, and L2) present good similarities with the drugs, a good bioavailability because they were compliant with the Lipinski rule. The complexes, although bioavailable, did not conform to Lipinski's rule. CAM showed efficacy in enzymatic inhibition. However, L1 and L2 ligands perform better in ion channel modulation, kinase, and protease inhibition. This suggests that the ligands have better therapeutic performance and may well address several clinical needs. The C3 complex was the compound that showed better bioavailability and high bioactivity thus it was the most bioactive. L1, L2, and C3 could therefore be potential and promising candidates for CAM substitution. Permeability ; hERG : human Ether-a-go-go-Related Gene); GPCR : G protein-coupled receptor; NRL : Nuclear receptor ligand ; ICM : Ion channel modulation; KI : Kinase inhibition ; PI : Protease inhibition ; EI : Enzyme activity inhibition ; MLCT : Metal to Ligand Charge Transfer ; AAP : Acetaminophen ; AASC : Acetylsalicylic acid ; CAM : Chloramphenicol ; C1 : Ferric complex of CAM-O-AAP (L1) ; C2 : CAM-O-AASC iron complex (L2) ; C3 : CAM iron complex ; FeCAM : CAM iron complex; FeCAM-O-AAP : CAM-O-AAP iron complex (L1) ; FeCAM-O-AASC : CAM-O-AASC iron complex (L2) ; L1 : 2-(4-Acetylaminophenoxy)-2-chloro-N-[1,3-dihydroxy-1-(4-nitrophenyl) propan-2-yl] \"CAM-O-AAP; : 2-(2-Acetoxybenzoyloxy)-2-chloro-N-[1,3-dihydroxy-1-(4-nitrophenyl) propan-2-yl] the L2 ligand (from 335 nm for CAM to 395 nm for L2). These observations would thus be evidence for the formation of L1 and L2 compounds. In addition, the UV-Vis spectra of the C1, C2, and C3 complexes compared to the spectra of their respective ligands (L1, L2, and CAM) showed different types of effects, in particular the hyperchromatic effect in the case of the C1 and C3 complexes justified by the increase of the absorption maximum and the hypsochromatic effect in the case of C2 (from 395 nm for L2 to 315 nm for C2). These observations could well indicate the formation of C1, C2, and C3 complexes.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69367516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}