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

{"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}

{"title":"Characterization of Two Novel EF-Hand Proteins Identifies a Clade of Putative Ca²⁺-Binding Protein Specific to the Ambulacraria.","authors":"Arisnel Soto-Acabá, Pablo A Ortiz-Pineda, Joshua G Medina-Feliciano, Joseph Salem-Hernández, José E García-Arrarás","doi":"10.26502/jbsb.5107030","DOIUrl":"10.26502/jbsb.5107030","url":null,"abstract":"<p><p>In recent years, transcriptomic databases have become one of the main sources for protein discovery. In our studies of nervous system and digestive tract regeneration in echinoderms, we have identified several transcripts that have attracted our attention. One of these molecules corresponds to a previously unidentified transcript (<i>Orpin</i>) from the sea cucumber <i>Holothuria glaberrima</i> that appeared to be upregulated during intestinal regeneration. We have now identified a second highly similar sequence and analyzed the predicted proteins using bioinformatics tools. Both sequences have EF-hand motifs characteristic of calcium-binding proteins (CaBPs) and N-terminal signal peptides. Sequence comparison analyses such as multiple sequence alignments and phylogenetic analyses only showed significant similarity to sequences from other echinoderms or from hemichordates. Semi-quantitative RT-PCR analyses revealed that transcripts from these sequences are expressed in various tissues including muscle, haemal system, gonads, and mesentery. However, contrary to previous reports, there was no significant differential expression in regenerating tissues. Nonetheless, the identification of unique features in the predicted proteins and their presence in the holothurian draft genome suggest that these might comprise a novel subfamily of EF-hand containing proteins specific to the Ambulacraria clade.</p>","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"5 1","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648499/pdf/nihms-1843289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10312693","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":"Integrative In-Silico Evaluation of Features on BRCA1 Cis Regulatory Element","authors":"Apeksha Arun Bhandarkar, Smeeta Shrestha","doi":"10.26502/jbsb.5107037","DOIUrl":"https://doi.org/10.26502/jbsb.5107037","url":null,"abstract":"Genomic cis regulatory elements support the gene transcriptional landscape which fine tune spatiotemporal gene expression via interaction with different transcription factors and co modulators during development. These regulatory elements are poorly conserved, highly heterogenous with limited understanding of their role in gene expression. Here we use a well-known human tumor suppressor gene, Breast Cancer Type 1 ( BRCA1 ) and UCSC human genome browser database to report the in-silico putative cis regulatory enhancer element and its features. We report a 2kb double elite enhancer, GH17J043079 located within intron 12 of the BRCA1 gene. The enhancer interacts with NBR1 , NBR2 , TMEM106A and RPL27 and VAT1 gene promoters. GH17J043079 showed histone activity in human embryonic stem cells, cancerous cells, housed transcription factors specific to liver cells and was enriched with Alu elements, indicative of ability for potential gene rearrangements. Additionally, it contained eQTLs, rs4793197, rs8176190, rs8176192, rs8176193 and rs8176194 with disparity in allele frequency across populations. Our in-silico review on the features present within GH17J043079 element in BRCA1 helps to postulate an intricate transcription regulation. Such candidate based analysis of features within cis regulatory element on a gene can help elucidate intricate genomic architecture, gene regulation and its impact on complex disorders.","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":"69367859","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":"Prenatal findings of 2q13 Duplication and Deletion: Further Evidence for Lack of Phenotypic-Genotype Correlation","authors":"L. Li, X. Huang, M. Ye, J. Chen, Z. Zeng, H. Guo, Q. Liao, W. Hu, D. Tang, Y. Dai","doi":"10.22541/au.163308148.84107640/v1","DOIUrl":"https://doi.org/10.22541/au.163308148.84107640/v1","url":null,"abstract":"2q13 CNV was associated with various diseases, with a lack of consensus.\u0000By CMA analysis, we found that four fetuses had deletion in the proximal\u0000region of 2q13, one had duplication, and one had duplication in the\u0000distal region of 2q13; however, they had variable outcomes.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47622345","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":"Abstract 184: The utility of deep metric learning for breast cancer identification on mammographic images","authors":"Justin Du, S. Umrao, E. Chang, M. Joel, Aidan Gilson, G. Janda, R. Choi, Yongfeng Hui, S. Aneja","doi":"10.1158/1538-7445.AM2021-184","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-184","url":null,"abstract":"Purpose: Although deep learning (DL) models have shown increasing ability to accurately classify diagnostic images in oncology, significantly large amounts of well-curated data are often needed to match human level performance. Given the relative paucity of imaging datasets for less prevalent cancer types, there is an increasing need for methods which can improve the performance of deep learning models trained using limited diagnostic images. Deep metric learning (DML) is a potential method which can improve accuracy in deep learning models trained on limited datasets. Leveraging a triplet-loss function, DML exponentially increases training data compared to a traditional DL model. In this study, we investigated the utility of DML to improve the accuracy of DL models trained to classify cancerous lesions found on screening mammograms. Methods: Using a dataset of 2620 lesions found on routine screening mammogram, we trained both a traditional DL and DML models to classify suspicious lesions as cancerous or benign. The VGG16 architecture was used as the basis for the DL and DML models. Model performance was compared by calculating model accuracy, sensitivity, and specificity on a blinded test set of 378 lesions. In addition to individual model performance, we also measured agreement accuracy when both the DL and DML models were combined. Sub-analyses were conducted to identify phenotypes which were best suited for each model type. Both models underwent hyperparameters optimization to identify ideal batch size, learning rate, and regularization to prevent overfitting. Results: We found that the combination of the traditional DL model with DML model resulted in the highest overall accuracy (78.7%) representing a 7.1% improvement compared to the traditional DL model (p Conclusion: Our study suggests that addition of DML models with traditional DL models can improve diagnostic image classification performance in cancer. Our results suggest DML models may provide increased specificity and help with classification of unique populations often misclassified by traditional DL models. Further studied investigating the utility of DML on other cancer imaging tasks are necessary to successfully build more robust DL models in cancer imaging. Citation Format: Justin Du, Sachin Umrao, Enoch Chang, Marina Joel, Aidan Gilson, Guneet Janda, Rachel Choi, Yongfeng Hui, Sanjay Aneja. The utility of deep metric learning for breast cancer identification on mammographic images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 184.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73633562","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":"Abstract SY01-03: The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI","authors":"J. Elmore","doi":"10.1158/1538-7445.AM2021-SY01-03","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-SY01-03","url":null,"abstract":"The current gold standard for cancer diagnoses is based on pathologists9 visual inspection of tissue sections. However, our research has found concerning levels of inter-observer and intra-observer variability among pathologists. Our prior work in melanoma shows that current diagnoses within the disease spectrum from benign nevi to melanoma in situ to invasive melanoma are neither reproducible nor accurate, yielding estimates that ~17% of all diagnoses for melanocytic lesions in the US are incorrect (Elmore et al. BMJ 2017). A study conducted by our team in breast pathology quantified the magnitude of diagnostic agreement among pathologists compared with a gold standard consensus reference: among DCIS cases, 16% of interpretations were discordant, while among atypia cases 52% of interpretations were discordant (Elmore et al. JAMA 2015). While computer systems, such as computer aided detection (CAD) tools, have been widely integrated into clinical practice to aid the interpretative and diagnostic process, our work has also found that the use of CAD can be associated with increases in potential harms, including higher recall and biopsy rates for screening mammography (Fenton et al. NEJM 2007). Given the critical need to improve the quality of our current diagnostic and prognostic capabilities, our multidisciplinary research team is conducting several studies that involve the development and integration of AI/machine learning and eye-tracking across clinical contexts. The challenges and implications associated with “gold standard” definitions for diagnoses, with data sharing infrastructure and with the eventual impact of AI on the human interface will be discussed. Citation Format: Joann Elmore. The gold standard cancer diagnosis: Studies of physician variability, interpretive behavior, and the impact of AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY01-03.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74479392","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":"Abstract 237: Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data using graph embedding","authors":"Liang Ding, Hao Shi, Koon-Kiu Yan, Yogesh Dhungana, Sivaraman Natarajan, J. Easton, J. Chiang, C. Tinkle, Xiaoyan Zhu, Liming Cai, S. Baker, H. Chi, Jiyang Yu","doi":"10.1158/1538-7445.AM2021-237","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-237","url":null,"abstract":"Spatial heterogeneity of diverse cellular components in the tumor microenvironment (TME) plays a critical role in the reprogramming of tumor initiation, growth, invasion, metastasis, and response to therapies. Systematic knowledge of TME spatial organization with regards to immune infiltration and tumor resource distribution is of high clinical significance. High throughput single-cell RNA sequencing (scRNA-seq) has become a revolutionary approach for studying cell composition and the development of TME. However, the spatial information of cells is lost as the tissue must be dissociated before the sequencing is performed. While various spatial techniques are emerging, their applicability is still rather limited. To address this challenge computationally, we develop a novel de novo framework to reconstruct TME spatial organization from scRNA-seq data. We hypothesized that cell spatial organization in a microenvironment is mainly determined by cell identity and interactions between different cells. In particular, the spatial organization of structural cells and immune cells follow different mechanisms. Neighboring structural cells, which share similar whole transcriptome profiles, form a scaffold of the TME; immune cells, whose activities are influenced by the structural cells, migrate in the scaffold to interact with structural cells and exert their functions. The algorithm models the scaffold of structural cells using adaptive nearest neighbor graph by taking the cell density estimation into the consideration, where the nearest neighbor graph was further augmented by inserting immune cells into the appropriate locations of the scaffold according to the LR similarities. To reconstruct 3D spatial organization while preserving the cell topology represented by the graph, we employed a graph embedding strategy to minimize the discrepancy between the graph topology and the embedded 3D space. We evaluated the framework on two diffuse intrinsic pontine gliomas (DIPG) samples from a mouse model with coupled scRNA-seq and spatial transcriptome (ST, 10x Visium platform) data. The predicted spatial organization successfully separates the major cell types. The T-cell infiltrated tumor, verified by the T-cell spatial spots of the ST image, is well recapitulated. We deconvoluted the ST data by integrating the scRNA-seq data using SPOTlight. The neighborhood enrichment distributions of predicted spatial organization and the spot deconvoluted ST data show high consistency as measured by Kullback-Leibler divergence. We found heterogeneous neighborhood composition of CD8+ T-cells, indicating diverse clonality and functions with respect to their locations in the TME. Citation Format: Liang Ding, Hao Shi, Koon-Kiu Yan, Yogesh Dhungana, Sivaraman Natarajan, John Easton, Jason Chiang, Christopher L. Tinkle, Xiaoyan Zhu, Liming Cai, Suzanne J. Baker, Hongbo Chi, Jiyang Yu. Inferring spatial organization of tumor microenvironment from single-cell RNA sequencing data usin","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78201923","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":"Abstract 187: Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures","authors":"Derek Van Booven, Victor Sandoval, O. Kryvenko, M. Parmar, A. Briseño, H. Arora","doi":"10.1158/1538-7445.AM2021-187","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-187","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75085466","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}