Jump to Navigation

Deep Learning for Survival Analysis in Breast Cancer with Whole Slide Image Data

Bioinformatics Oxford Journals - Wed, 08/06/2022 - 5:30am
AbstractMotivationWhole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer mechanisms. Such analyses are challenging to execute because of the sizes and complexity of whole slide image data and relatively limited volume of training data for machine learning methods.ResultsWe propose and experimentally evaluate a multi-resolution deep learning method for breast cancer survival analysis. The proposed method integrates image data at multiple resolutions and tumor, lymphocyte and nuclear segmentation results from deep learning models. Our results show that this approach can significantly improve the deep learning model performance compared to using only the original image data. The proposed approach achieves a c-index value of 0.706 compared to a c-index value of 0.551 from an approach that uses only color image data at the highest image resolution. Furthermore, when clinical features (sex, age and cancer stage) are combined with image data, the proposed approach achieves a c-index of 0.773.Availabilityhttps://github.com/SBU-BMI/deep_survival_analysis
Categories: Bioinformatics Trends

Integrative Data Semantics through a Model-enabled Data Stewardship

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractMotivationThe importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease aetiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets.ResultsHere, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests, and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved.AvailabilityThe DST source code and Docker images are respectively available at https://github.com/SCAI-BIO/data-steward and https://hub.docker.com/r/phwegner/data-steward. Furthermore, the DST is hosted at https://data-steward.bio.sca.fraunhofer.de/data-steward.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractSummaryQuantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.AvailabilityscGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Effective drug-target interaction prediction with mutual interaction neural network

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractMotivationAccurately predicting drug-target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in building deep learning models for DTI prediction is how to appropriately represent drugs and targets. Target distance map and molecular graph are low dimensional and informative representations, which however have not been jointly used in DTI prediction. Another challenge is how to effectively model the mutual impact between drugs and targets. Though attention mechanism has been employed to capture the one-way impact of targets on drugs or vice versa, the mutual impact between drugs and targets has not yet been explored, which is very important in predicting their interactions.ResultsTherefore, in this paper we propose MINN-DTI, a new model for DTI prediction. MINN-DTI combines an interacting-transformer module (called Interformer) with an improved Communicative Message Passing Neural Network (CMPNN) (called Inter-CMPNN) to better capture the two-way impact between drugs and targets, which are represented by molecular graph and distance map respectively. The proposed method obtains better performance than the state-of-the-art methods on three benchmark datasets: DUD-E, human and BindingDB. MINN-DTI also provides good interpretability by assigning larger weights to the amino acids and atoms that contribute more to the interactions between drugs and targets.AvailabilityThe data and code of this study are available at https://github.com/admislf/MINN-DTI.
Categories: Bioinformatics Trends

Spectral clustering of single-cell multi-omics data on multilayer graphs

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractMotivationSingle-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem.ResultsWe introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs (SCML) and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis.AvailabilityThe code used in this study can be found at https://github.com/jssong-lab/sc-spectrumSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Prior Knowledge Facilitates Low Homologous Protein Secondary Structure Prediction with DSM Distillation

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractMotivationProtein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignment (MSA). Unfortunately, many proteins lack homologous sequences, which results in the low quality of MSA and poor performance. In this paper, we propose the novel DSM-Distil to tackle this issue, which takes advantage of the pretrained BERT and exploits the knowledge distillation on the newly designed dynamic scoring matrix (DSM) features. Specifically, we propose the dynamic scoring matrix (DSM) to replace the widely used profile and PSSM features. DSM could automatically dig for the suitable feature for each residue, based on the original profile. Namely, DSM-Distil not only could adapt to the low homologous proteins but also is compatible with high homologous ones. Thanks to the dynamic property, DSM could adapt to the input data much better and achieve higher performance. Moreover, to compensate for low-quality MSA, we propose to generate the pseudo-DSM from a pretrained BERT model and aggregate it with the original DSM by adaptive residue-wise fusion, which helps to build richer and more complete input features. In addition, we propose to supervise the learning of low-quality DSM features by using high-quality ones. To achieve this, a novel teacher-student model is designed to distill the knowledge from proteins with high homologous sequences to that of low ones. Combining all the proposed methods, our model achieves the new state-of-the-art performance for low homologous proteins.ResultsCompared with the previous state-of-the-art method “Bagging”, DSM-Distil achieves an improvement about 5% and 7.3% improvement for proteins with MSA count ≤ 30 and extremely low homologous cases respectively. We also compare DSM-Distil with Alphafold2 which is a state-of-the-art framework for protein structure prediction. DSM-Distil outperforms Alphafold2 by 4.1% on extremely low-quality MSA on 8-state secondary structure prediction. Moreover, we release a large-scale up-to-date test dataset BC40 for low-quality MSA structure prediction evaluation.Availability and implementationBC40 dataset: https://drive.google.com/drive/folders/15vwRoOjAkhhwfjDk6-YoKGf4JzZXIMCHardCase dataset: https://drive.google.com/drive/folders/1BvduOr2b7cObUHy6GuEWk-aUkKJgzTUvCode: https://github.com/qinwang-ai/DSM-Distil
Categories: Bioinformatics Trends

CLIPreg: Constructing translational regulatory networks from CLIP-, Ribo- and RNA-seq

Bioinformatics Oxford Journals - Thu, 02/06/2022 - 5:30am
AbstractMotivationThe creation and analysis of gene regulatory networks have been the focus of bioinformatics research and underpins much of what is known about gene regulation. However, as a result of a bias in the availability of data-types that are collected, the vast majority of gene regulatory network resources and tools have focused on either transcriptional regulation or protein-protein interactions. This has left other areas of regulation, for instance, translational regulation, vastly underrepresented despite them having been shown to play a critical role in both health and disease.ResultsIn order to address this we have developed CLIPreg, a package that integrates RNA, Ribo and CLIP- sequencing data in order to construct translational regulatory networks coordinated by RNA-binding proteins and micro-RNAs. This is the first tool of its type to be created, allowing for detailed investigation into a previously unseen layer of regulation.Availability and implementationCLIPreg is available at https://github.com/SGDDNB/CLIPreg.
Categories: Bioinformatics Trends

gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs

Bioinformatics Oxford Journals - Wed, 01/06/2022 - 5:30am
AbstractMotivationA widely applicable strategy to create cell factories is to knock out (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesise at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow.ResultsTo address this multi-objective optimization problem, we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise cell growth, product synthesis, and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run - granting users flexibility in selecting designs to take to the lab.AvailabilitygcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755 (Legon et al., 2022).Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Palo: Spatially-aware color palette optimization for single-cell and spatial data

Bioinformatics Oxford Journals - Wed, 01/06/2022 - 5:30am
AbstractSummaryIn the exploratory data analysis of single-cell or spatial genomic data, single cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assign visually similar colors to spatially neighboring clusters, making it hard to identify the distinction between clusters. To address this issue, we developed Palo that optimizes the color palette assignment for single-cell and spatial data in a spatially-aware manner. Palo identifies pairs of clusters that are spatially neighboring to each other and assigns visually distinct colors to those neighboring pairs. We demonstrate that Palo leads to improved visualization in real single-cell and spatial genomic datasets.AvailabilityPalo R package is freely available at Github (https://github.com/Winnie09/Palo) and Zenodo (https://doi.org/10.5281/zenodo.6562505).
Categories: Bioinformatics Trends

Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors

Bioinformatics Oxford Journals - Wed, 01/06/2022 - 5:30am
AbstractMotivationMulti-region sequencing of solid tumors can improve our understanding of intratumor subclonal diversity and the evolutionary history of mutational events. Due to uncertainty in clonal composition and the multitude of possible ancestral relationships between clones, elucidating the most probable relationships from bulk tumor sequencing poses statistical and computational challenges.ResultsWe developed a Bayesian hierarchical model called PICTograph to model uncertainty in assigning mutations to subclones, to enable posterior distributions of cancer cell fractions, and to visualize the most probable ancestral relationships between subclones. Compared to available methods, PICTograph provided more consistent and accurate estimates of cancer cell fractions and improved tree inference over a range of simulated clonal diversity. Application of PICTograph to multi-region whole exome sequencing of tumors from individuals with pancreatic cancer precursor lesions confirmed known early-occurring mutations and indicated substantial molecular diversity, including 6-12 distinct subclones and intra-sample mixing of subclones. Using ensemble-based visualizations, we highlight highly probable evolutionary relationships recovered in multiple models. PICTograph provides a useful approximation to evolutionary inference from cross-sectional multi-region sequencing, particularly for complex cases.Availabilityhttps://github.com/KarchinLab/pictograph
Categories: Bioinformatics Trends

MIO: MicroRNA target analysis system for Immuno-Oncology

Bioinformatics Oxford Journals - Wed, 01/06/2022 - 5:30am
AbstractSummaryMicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology, however, dedicated analysis tools are missing. Here we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of forty microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enable the selection of prognostic and predictive microRNAs and gene interaction network biomarkers.Availability and Implementationhttps://mio.icbi.at, https://github.com/icbi-lab/mio, https://github.com/icbi-lab/miopySupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

VOC-alarm: Mutation-based prediction of SARS-CoV-2 variants of concern

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
Abstract Mutation is the key for a variant of concern (VOC) to overcome selective pressures, but this process is still unclear. Understanding the association of the mutational process with VOCs is an unmet need. Here we developed VOC-alarm, a method to predict VOCs and their caused COVID surges, using mutations of about 5.7 million SARS-CoV-2 complete sequences. We found that VOCs rely on lineage-level entropy value of mutation numbers to compete with other variants, suggestive of the importance of population-level mutations in the virus’ evolution. Thus, we hypothesized that VOCs are a result of a mutational process across the globe. Analyzing the mutations from January 2020—December 2021, we simulated the mutational process by estimating the pace of evolution, and thus divided the time period, January 2020—March 2022, into eight stages. We predicted Alpha, Delta, Delta Plus (AY.4.2) and Omicron (B.1.1.529) by their mutational entropy values in the stages I, III, V, and VII with accelerated paces, respectively. In late November 2021, VOC-alarm alerted that Omicron strongly competed with Delta and Delta plus to become a highly transmissible variant. Using simulated data, VOC-alarm also predicted that Omicron could lead to another COVID surge from January 2022—March 2022.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractMotivationThe importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neither of these approaches provides an adequate understanding of gene regulation in human tissues. To address this issue, we developed a deep learning-based chromatin loop prediction model called DeepLUCIA (Deep Learning-based Universal Chromatin Interaction Annotator).ResultsAlthough DeepLUCIA does not use TF binding profile data which previous TF binding-dependent methods critically rely on, its prediction accuracies are comparable to those of the previous TF binding-dependent methods. More importantly, DeepLUCIA enables the tissue-specific chromatin loop predictions from tissue-specific epigenomes that cannot be handled by genomic variation-based approach. We demonstrated the utility of the DeepLUCIA by predicting several novel target genes of SNPs identified in genome-wide association studies targeting Brugada syndrome, COVID-19 severity, and age-related macular degeneration.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

CNpare: matching DNA copy number profiles

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractSummarySelecting the optimal cancer cell line for an experiment can be challenging given the diversity of lines available. Here, we present CNpare, which identifies similar cell line models based on genome-wide DNA copy number.AvailabilityCNpare is available as an R package at https://github.com/macintyrelab/CNpare. All analysis performed in the manuscript can be reproduced via the code found at https://github.com/macintyrelab/CNpare_analysesSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractMotivationPhytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins is crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset.ResultsIn our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set.Availability and implementationEffector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http://lab.malab.cn/~wys/webserver/Effector-GAN). The Python script can be downloaded from http://lab.malab.cn/~wys/gitlab/effector-gan.
Categories: Bioinformatics Trends

Trying Out a Million Genes to Find the Perfect Pair with RTIST

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractMotivationConsensus methods can be used for reconstructing a species tree from several gene trees which exhibit incompatible topologies due to incomplete lineage sorting. Motivated by the fact that there are no anomalous rooted gene trees with three taxa and no anomalous unrooted gene trees with four taxa in the multispecies coalescent model, several contemporary methods form the gene tree consensus by finding the median tree with respect to the triplet or quartet distance—i.e., estimate the species tree as the tree which minimizes the sum of triplet or quartet distances to the input gene trees. These methods reformulate the solution to the consensus problem as the solution to a recursively-solved dynamic programming problem. We present an iterative, easily-parallelizable approach to finding the exact median triplet tree, and implement it as an open source software package which can also find suboptimal consensus trees within a specified triplet distance to the gene trees. The most time-consuming step for methods of this type is the creation of a weights array for all possible subtree bipartitions. By grouping the relevant calculations and array update operations of different bipartitions of the same subtree together, this implementation finds the exact median tree of many gene trees faster than comparable methods, has better scaling properties with respect to the number of gene trees, and has a smaller memory footprint.ResultsRTIST (Rooted Triple Inference of Species Trees) finds the exact median triplet tree of a set of gene trees. Its runtime and memory footprints scale better than existing algorithms. RTIST can resolve all the non-unique median trees, as well as sub-optimal consensus trees within a user-specified triplet distance to the median. Although it is limited in the number of taxa (≤ 20), its runtime changes little when the number of gene trees is changed by several orders of magnitude.AvailabilityRTIST is written in C and Python. It is freely available at https://github.com/glebzhelezov/rtist
Categories: Bioinformatics Trends

transferGWAS: GWAS of images using deep transfer learning

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractMotivationMedical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations.ResultsWe validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.Supplementary informationOur method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/
Categories: Bioinformatics Trends

An adaptive direction-assisted test for microbiome compositional data

Bioinformatics Oxford Journals - Tue, 31/05/2022 - 5:30am
AbstractMotivationMicrobial communities have been shown to be associated with many complex diseases such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros, and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings.ResultsWe develop a novel microbiome-based direction-assisted test (MiDAT) for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and ii) contains a burden type test which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies.AvailabilityAn R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data

Bioinformatics Oxford Journals - Fri, 27/05/2022 - 5:30am
AbstractMotivationTesting for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (1) matching uncertainty between data features and metabolites; (2) lacking of method to analyze positive mode and negative mode LC/MS data simultaneously on the same set of subjects; (3) the incompleteness of pathways in individual software packages.ResultsWe developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration – (1) a weighted GSEA-type test, and (2) a permutation-based weighted hypergeometric test. The package is capable of combining positive and negative ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: KEGG, Mummichog, and SMPDB.AvailabilityThe package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html.Supplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
Categories: Bioinformatics Trends

Pages

Subscribe to Centre for Bioinformatics aggregator - Bioinformatics Trends

Calendar

Mon
Tue
Wed
Thu
Fri
Sat
Sun
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
 
August 2022