調査、論文、教育、資格学習（4,182 件）
概要と使いどころ
Research any domain with WHOIS data, DNS records, SSL certificate details, domain age, reputation scoring, and security header analysis. Covers ownership research, infrastructure mapping, security assessment, and competitive domain analysis.
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Comprehensive domain analysis combining WHOIS ownership, DNS infrastructure, SSL security, SEO performance, tech stack, site structure, and backlink profile. One-stop domain intelligence for acquisition research, security assessment, and competitive analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Look up Common Vulnerabilities and Exposures (CVEs) with severity scores, affected software, exploitability status, and remediation guidance. Essential for security research, vulnerability management, and patch prioritization.
日本語の概要は準備中です。原文の説明を表示しています。
Rapidly assess crisis situations by monitoring Twitter, Reddit, news, and Chinese social platforms simultaneously. Detects escalation velocity, identifies key narratives, tracks official responses, and measures public sentiment shifts during critical events.
日本語の概要は準備中です。原文の説明を表示しています。
Translate text content between languages with high quality and natural phrasing. Covers document translation, content localization, and multi-language research support for teams working across language barriers.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze content performance across YouTube, TikTok, Instagram, and Twitter to identify what works. Compares engagement rates, format effectiveness, posting cadence impact, and audience response patterns across platforms for content strategy optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Generate content ideas backed by real audience demand data from Reddit questions, YouTube search gaps, Twitter discussions, and web search trends. Identifies proven topics, underserved questions, and content formats that outperform in your niche.
日本語の概要は準備中です。原文の説明を表示しています。
Monitor competitor websites, content changes, social activity, and market positioning through SandBase. Use when asked for competitive intelligence, competitor tracking, market positioning research, or competitive content monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Research competitor pricing strategies by extracting pricing pages, comparing product listings on Google Shopping and Amazon, and analyzing market positioning. Produces pricing benchmarks, tier comparisons, and feature-per-price analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Research online communities across Reddit, Telegram, and social platforms. Analyzes community size, engagement patterns, discussion themes, moderation culture, and member demographics for community strategy, partnership, and go-to-market planning.
日本語の概要は準備中です。原文の説明を表示しています。
Research any topic across China's major social platforms — Weibo, Douyin, Xiaohongshu, Bilibili, and Zhihu — simultaneously. Aggregates discussion volume, sentiment, key opinion leaders, and platform-specific trends for comprehensive China market intelligence.
日本語の概要は準備中です。原文の説明を表示しています。
Research target audiences across Reddit, Twitter, Instagram, and Xiaohongshu to understand demographics, interests, pain points, language patterns, and content preferences. Produces audience personas backed by real community data.
日本語の概要は準備中です。原文の説明を表示しています。
Search companies, enrich profiles with firmographic data, find job postings, and track company news through Apollo. Covers company search, detailed enrichment (industry, size, funding, tech stack), hiring activity, and news monitoring for B2B sales intelligence.
日本語の概要は準備中です。原文の説明を表示しています。
Research companies with employee reviews, product reviews, industry classification, and competitive landscape through Akta. Covers company enrichment, employee satisfaction signals, product feedback, industry mapping, and news monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Search academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence gathering.
日本語の概要は準備中です。原文の説明を表示しています。
Track flight status, delays, gate changes, and estimated arrival times for any commercial flight. Covers real-time flight tracking, schedule lookup, and delay monitoring for travel coordination and logistics planning.
日本語の概要は準備中です。原文の説明を表示しています。
Parse and extract structured content from PDFs, Word documents, and other file formats. Converts documents into clean, machine-readable text for analysis, summarization, data extraction, and content processing workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Convert between currencies with real-time exchange rates. Supports 150+ currencies including crypto, with historical rate lookup. Useful for international pricing, financial reporting, and cross-border commerce research.
日本語の概要は準備中です。原文の説明を表示しています。
Research emerging academic trends by combining scholarly databases, web sources, and news to identify rising research areas, breakthrough papers, and shifting scientific consensus. Ideal for R&D teams tracking frontier science.
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Enrich any company with comprehensive firmographic data including industry, size, revenue, funding, tech stack, beneficial ownership, and regulatory status. Combines multiple data sources for complete B2B company intelligence and due diligence.
日本語の概要は準備中です。原文の説明を表示しています。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
日本語の概要は準備中です。原文の説明を表示しています。
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
日本語の概要は準備中です。原文の説明を表示しています。
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
日本語の概要は準備中です。原文の説明を表示しています。
Clinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.
日本語の概要は準備中です。原文の説明を表示しています。