'ioc' 검색 결과

Build incident response dashboards that bring affected systems, containment progress, indicators of compromise, and response timelines into one view. Help security teams coordinate active incidents and prepare operational reports and executive briefings.

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• Coordinating an active security incident
• Tracking affected systems and containment
• Reviewing response timelines after an incident

Investigate suspicious URLs with URLScan.io by reviewing screenshots, redirects, page content, and network requests. Extract domains, IP addresses, and file hashes as indicators of compromise, then compare findings with threat intelligence sources.

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• Investigating suspicious links in emails
• Checking pages for brand impersonation and credential forms
• Tracing redirects and extracting domains, IPs, and hashes

Search Google SecOps events for threats that have not triggered alerts. Test attack hypotheses, check historical records for compromise indicators, and examine unusual activity, then document findings and add them to relevant security cases.

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• Test a threat hypothesis based on an attack technique
• Check historical events for newly disclosed compromise indicators
• Investigate rare program executions and network destinations

Write and review YARA-X rules, which identify malware through file patterns. Refine detection strings and conditions, check rule quality with included scripts, test against clean files, improve performance, and migrate rules from legacy YARA.

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• Write rules targeting a specific malware family
• Review false positives and slow detection rules
• Migrate existing YARA rules to YARA-X

Investigate an alert or suspicious identifier using logs and source code. Assess attack success, measure affected data and users, test the suspected flaw locally, and produce a response plan for human review and execution.

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• Investigating a security alert
• Measuring affected records and customers
• Checking a suspected flaw with a local proof of concept

Search Rhea, a biochemical reaction database, using reaction IDs, compound names, enzyme classification numbers, or reaction terms. Get concise summaries of focused search results, with raw JSON available when you need machine-readable output.

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• Looking up a reaction by its Rhea ID
• Finding reactions associated with a compound
• Searching reactions by enzyme classification number

Syzkaller syzlang syntax basics for describing ioctl syscalls

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Use when targeting Soil Biology & Biochemistry or deciding whether a soil-science manuscript fits this venue. Encodes the journal's fit, the soil-process-mechanism bar, evidence and data-reporting expectations, house style, official-submission re-check, and desk-reject heuristics.

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SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.

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The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across

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Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordina

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Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

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The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.

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Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. Scales linearly with the number of spots and is designed to be used with 'SpatialExperiment' objects.

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SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin

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Annotate variants, compute amino acid coding changes, predict coding outcomes.

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Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.

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Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained f

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We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positi

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Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.

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Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and

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The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensi

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The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou

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Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <d

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