Signals via API

Public sample from today's flagship detail endpoint. Public payloads can be limited or redacted. The backend exposes read endpoints for signals, storylines, and briefings.

GET /v1/narratives/f851ed9c-b7fe-497d-a896-14bfcb4c21f4?tenant=biotech&run_id=f978da0e-08e5-4f34-86e6-4ec72bcde9a6

{
  "id": "f851ed9c-b7fe-497d-a896-14bfcb4c21f4",
  "title": "ProtBLIP2-SST: Protein Function Prediction via BLIP2 with Sequence, Structure, and Text",
  "summary": "Protein function prediction traditionally relies on structured gene ontology (GO) labels or multi-label classifiers. However, these labels or classifiers cannot flexibly describe molecular function, biological process, cellular component, and free-text functional narratives in a single output. In comparison, generation-based approaches offer an intuitive paradigm for flexible free-text protein ann",
  "first_seen_at": "2026-07-12T15:48:36.505334Z",
  "last_seen_at": "2026-07-13T02:48:33.482550Z",
  "created_at": "2026-07-13T05:06:02.886458Z",
  "updated_at": "2026-07-13T05:06:09.778347Z",
  "timeframe": "24h",
  "run_id": "f978da0e-08e5-4f34-86e6-4ec72bcde9a6",
  "metrics": {
    "divergence": 17.3205,
    "post_count": 3,
    "momentum_1h": 0,
    "momentum_7d": 3,
    "score_total": 0.96368,
    "shill_score": 10,
    "momentum_24h": 3,
    "market_movers": [],
    "evidence_score": 0.25,
    "unique_authors": 3,
    "coherence_score": 0.7763,
    "diversity_bonus": 0.0875,
    "duplicate_ratio": 0,
    "platforms_count": 1,
    "social_momentum": 3,
    "divergence_label": "chatter_without_pricing",
    "market_conviction": 0,
    "origin_share_top1": 1,
    "unique_publishers": 1,
    "origin_layer_posts": 3,
    "source_types_count": 1,
    "unique_authors_24h": 3,
    "unique_origin_urls": 3,
    "amplifier_share_top1": null,
    "author_concentration": 0.3333,
    "publisher_share_top1": 1,
    "amplifier_layer_posts": 0,
    "concentration_penalty": 0.2,
    "unique_origin_domains": 1,
    "source_diversity_bonus": 0,
    "unique_publisher_types": 1,
    "unique_origin_publishers": 1
  },
  "platforms": [
    "rss"
  ],
  "top_tickers": [],
  "why_now": [],
  "display_title": "Advances in protein function prediction integrating sequence, structure, and language models",
  "display_summary": "Recent research introduces innovative computational frameworks that enhance protein function prediction by integrating protein sequence, structural data, and natural language processing.",
  "narrative_frame_display": null,
  "entities": null,
  "recurring_claims": [
    {
      "claim": "Integrating protein sequence and structure data with language models improves protein function prediction accuracy and flexibility.",
      "evidence_urls": [
        "https://biorxiv.org/content/10.64898/2026.07.10.737551v1?rss=1",
        "https://biorxiv.org/content/10.64898/2026.07.11.737882v1?rss=1"
      ]
    },
    {
      "claim": "Protein language model-guided data augmentation enhances prediction tasks by preserving biological signals and optimizing variation.",
      "evidence_urls": [
        "https://biorxiv.org/content/10.64898/2026.07.10.737545v1?rss=1"
      ]
    }
  ],
  "stance_map": [
    {
      "stance": "promotes",
      "who": "Chen, Z., Luo, Q.",
      "evidence_urls": [
        "https://biorxiv.org/content/10.64898/2026.07.10.737551v1?rss=1"
      ]
    },
    {
      "stance": "promotes",
      "who": "Mathai, D., Schulze, S.",
      "evidence_urls": [
        "https://biorxiv.org/content/10.64898/2026.07.11.737882v1?rss=1"
      ]
    },
    {
      "stance": "promotes",
      "who": "Chen, Z., Wang, R., Luo, Q.",
      "evidence_urls": [
        "https://biorxiv.org/content/10.64898/2026.07.10.737545v1?rss=1"
      ]
    }
  ],
  "quality_flags": {
    "mixed_topic_risk": "low",
    "promo_risk": "low",
    "source_quality": "medium"
  },
  "editor_note": "This cluster highlights cutting-edge integrative methods combining sequence, structure, and language models to improve protein function prediction, a key area in biotech R&D and genomics.",
  "display_tags": [
    "r_and_d",
    "genomics"
  ],
  "tags": [
    "r_and_d",
    "genomics"
  ],
  "cscope_tags": [
    "r_and_d",
    "genomics"
  ],
  "why_now_display": [
    "Recent advances in protein language models and AlphaFold structure predictions enable novel integrative approaches.",
    "Open-source tools like ProtPen facilitate accessible proteome-wide functional annotation.",
    "Systematic evaluation of data augmentation strategies addresses limitations of labeled protein data."
  ],
  "why_it_matters_display": [
    "Improved protein function prediction aids understanding of biological processes and disease mechanisms.",
    "Integrating sequence, structure, and language models enhances annotation flexibility and accuracy.",
    "These methods enable large-scale proteome analysis, accelerating biotech research and drug discovery."
  ],
  "show_why": true,
  "sources_display": [
    {
      "label": "ProtBLIP2-SST: Protein function prediction via BLIP2 with sequence, structure, and text"
    },
    {
      "label": "biorxiv.org: ProtPen combines sequence- and structure-based approaches to facilitate protein..."
    },
    {
      "label": "ProtAug: An empirical investigation of pLM-guided data augmentation for protein..."
    }
  ],
  "trend_status": "flat",
  "trend_sparkline": "▁",
  "trend_points_n": 0,
  "trend_window": {
    "lookback_days": 14,
    "max_points": 36,
    "value_key": null
  },
  "llm_status": "accepted",
  "llm_meta": {
    "model": "gpt-4.1-mini-2025-04-14",
    "prompt_version": "enrich_v3",
    "input_hash": "0bed5336d07d5706c96cc71e9c2dc701c85051956fd8bfa438e68bfa1238ef24",
    "updated_at": "2026-07-14T17:10:29.169470+00:00",
    "llm_metadata": {
      "env": "prod",
      "host": "3becb33dec2d",
      "stage": "signals.enrich",
      "run_id": "48d209ec-9a21-4f52-915d-8729d8354235",
      "tenant": "biotech",
      "service": "api",
      "pipeline": "pipeline_run",
      "correlation_id": "f851ed9c-b7fe-497d-a896-14bfcb4c21f4"
    }
  },
  "story_id": "1887320d-5340-4728-bff5-61470333d258",
  "public_surface": {
    "eligible_for_flagship_sample": false,
    "eligible_for_top_public_brief": false,
    "eligible_for_public_index": false,
    "eligible_only_for_lower_or_internal_surfaces": true,
    "public_rank_score": 4,
    "structural_priority": true,
    "mostly_social": false,
    "community_chatter": false,
    "reader_label": "Limited evidence",
    "excluded_reasons": [
      "low_evidence"
    ],
    "source_signals": {
      "post_count": 3,
      "unique_origins": 1,
      "source_types_count": 1,
      "independent_non_social_count": 1,
      "social_source_count": 0,
      "non_social_source_count": 0,
      "why_now_count": 3,
      "why_it_matters_count": 3
    }
  },
  "current_cycle_open": false
}

• Signals and storylines feed endpoints with filtering and rate limits
• Briefing delivery endpoints for integrations
• Evidence link payloads for auditability

• Route top stories into Slack or Teams for morning and evening desk updates.
• Sync storyline evidence into Notion, Airtable, or internal research trackers.
• Feed metrics into BI dashboards for momentum, concentration, and source mix monitoring.

Quick start endpoints: /v1/feed/stories, /v1/signals, /v1/storylines/search, /v1/briefings/latest.

For product access, see Pricing.