Research Analytics · AI-Powered Evidence Analysis

Deep Research for Life Science Evidence Analysis

Understand how scientific products are used, how applications are changing, and what emerging research means for your business, journal, or society.

PubGrade Deep Research brings AI-assisted evidence analysis into Research Analytics. Move beyond finding papers and counting mentions: investigate experimental details, classify applications, compare findings, and turn selected evidence into structured tables and practical briefs.

Find evidence & patterns. Investigate and compare them. Understand the findings.
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What Do You Want to Investigate?

Deep Research is designed for questions that go beyond your initial Research Analytics’ results. Use it to examine selected evidence in detail, compare records systematically, and understand the scientific meaning behind the patterns you already see in Research Analytics results.

 
Understand actual product use Understand actual product use

Understand actual product use

Go beyond product mentions. Investigate whether a product was actually used, how it was used, in which sample or workflow, what it measured or enabled, and whether the paper reports explicit benefits, limitations, or comparisons. Turn reviewed product-use patterns into reusable filters or tracked segments in Research Analytics.

Example question
In which assays, sample types, and workflows was our platform actually used, and which use cases should we track separately in Research Analytics?
Discover emerging modalities, targets, instruments, and workflows Discover emerging modalities, targets, instruments, and workflows

Discover emerging modalities, targets, instruments, and workflows

Explore a selected set of papers to surface entities and concepts you did not predefine. Identify emerging modalities, targets, biomarkers, instruments, reagents, workflow steps, or combinations of methods, then review and normalize them before turning them into reusable filters in Research Analytics.

Example question
Across these recent oncology papers, which targets, instruments, and new modalities recur — including ones we did not already know to search for?
Map what teams and competitors are actually working on Map what teams and competitors are actually working on

Map what teams and competitors are actually working on

Analyze the recent output of a lab, hospital, company, or competitor set to understand what they are really doing, not just which broad topics they publish in. Extract modalities, disease focus, targets, methods, and applications record by record to build a sharper view of scientific direction.

Example question
From the last 50 papers from this hospital and its competitors, which new modalities are being worked on, and which papers support each one?
Classify evidence into disease areas, research areas, and themes Classify evidence into disease areas, research areas, and themes

Classify evidence into disease areas, research areas, and themes

Organize publications into categories that fit your question rather than relying only on search terms or journal labels. Classify papers by disease area, research area, application, experimental purpose, model system, or common theme — then push those reviewed categories back into Research Analytics for counting, comparison, and monitoring.

Example question
Classify these papers by disease area and application. Which themes deserve their own saved filters for ongoing tracking?
Explain what is driving a trend, spike, or gap Explain what is driving a trend, spike, or gap

Explain what is driving a trend, spike, or gap

Start with a signal already visible in Research Analytics, then investigate what the underlying records actually have in common. Determine whether the change reflects a new modality, target class, workflow, sample type, terminology shift, or a small number of highly active groups.

Example question
Why did activity in this area rise so quickly — and is the increase driven by new modalities, new targets, or just a few very active organizations?
Explore journal, event, and society opportunities Explore journal, event, and society opportunities

Explore journal, event, and society opportunities

Look inside a growing topic or adjacent field to understand the scientific themes, methods, and communities that may fit your journal, annual meeting, webinar program, or society strategy. Identify underrepresented but relevant areas, extract what defines them, and turn promising themes into reusable Research Analytics segments for editorial, program, and audience planning.

Example question
Which emerging methods or themes fit our journal or annual meeting scope but are still underrepresented in our recent publications or program?

Built for Teams Across Research, Commercial, and Publishing Workflows

Deep Research supports different teams working from the same underlying evidence. It helps scientific, commercial, publishing, and intelligence teams investigate questions, compare findings, and turn research signals into useful outputs.

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Understand product use, emerging applications, unmet needs, and changes in scientific demand.

Prepare account briefs, battlecards, meeting talking points, and research-grounded outreach inputs.

Compare studies, investigate evidence, explore technologies, and prepare structured analyses.

Analyze research activity across authors, journals, conferences, topics, and communities.

Turn Any Research Analytics Segment Into a Deep Research Question

Deep Research does not begin with a blank prompt. It starts from the subsets you already defined in Research Analytics: a filtered result set, a tagged group of documents, or the records associated with a selected author, organization, journal, country, or time period—globally or within a specific topic or research area.

Investigate a specific question with a clearly defined evidence set.
 

Start with a Result Set in Research Analytics
Filter records mentioning a product, model, method, or competitor technology. Narrow by year, geography, topic, organization type, or tagged segment.

Use Deep Research to ask
Where is this product actually used, what does it measure or enable, and what role does it play in the workflow?

What this helps uncover

  • Actual use vs simple mention
  • Sample or material used
  • Application and disease context
  • Experimental purpose
  • Companion methods
  • Reported findings or limitations
  • Cases where use remains unresolved

Start with a Search in Research Analytics
Select a broad topic, technology, or product-related evidence set.

Use Deep Research to ask
Which specific applications are mentioned and appear to be growing, find and discover new use cases, new applications, or different experimental workflows. 

Loop new terms back into Research Analytics for analysis and trending.

What this helps uncover

  • Growth in established applications
  • Emerging or adjacent use cases
  • Shifts in sample type or workflow
  • New combinations of methods
  • Whether the apparent trend deserves deeper review

Start from in RA
Select one author, one organization, one company, or a defined subset of researchers or institutions within a topic.

Use Deep Research to ask
What methods, technologies, applications, and scientific priorities are represented in this selected body of work or organization-scoped evidence set?

What this helps uncover

  • Recurring methods or platforms
  • Scientific focus areas
  • Experimental priorities
  • Topic shifts over time (e.g. loop back to Research Analytics)
  • Useful context for account, editorial, or partnership planning

Start from in RA
Filter to subset of documents with potentially contradictory results in a certain research area. 

Use Deep Research to ask
What actually differs between these documents and what explains the difference?

What this helps uncover

  • Different applications or methods
  • Distinct organization or researcher behavior
  • Differences in study type or maturity
  • Whether the difference might be scientific, geographic, or workflow-related

Start from in RA
Select a co-mention, intersection, or grouped result involving products, methods, diseases, targets, or workflows.

Use Deep Research to ask
Do these terms describe the same experiment, the same workflow, a comparison, or only a shared document?

What this helps uncover

  • Real co-use vs loose co-occurrence
  • Product role in the experiment
  • Method combinations
  • Cases requiring manual review

Start from in RA
Select a country, region, organization type, time slice, or trend change within a defined evidence set.

Use Deep Research to ask
What developments are associated with this increase or change?

What this helps uncover

  • New applications entering the set
  • Different organization types driving growth
  • Emerging methods or technologies
  • Geographic or market-specific shifts
  • Whether the signal reflects something meaningful or just noise

Start from in RA
Select one journal, several journals, one conference, one conference track, or a publishing/community segment.

Use Deep Research to ask
Which scientific themes, methods, and study types define this selected journal, event, or community?

What this helps uncover

  • Fit with journal or society scope
  • Candidate themes for editorial or program review
  • Differences between your portfolio and the wider field
  • Methods or communities worth further attention

Start from in RA
Select a literature subset around a treatment, method, platform, workflow, or topic.

Use Deep Research to ask
How do these studies compare in methods, materials, endpoints, outcomes, limitations, and comparability?

What this helps uncover

  • Study design differences
  • Sample or population differences
  • Combination vs single-approach results
  • Reported trade-offs
  • Which findings are not directly comparable

The PubGrade advantage is not just AI. It is controlled evidence.

Research Analytics helps your team define the exact records it wants to investigate, inspect why they were included, and refine the scope before analysis. Deep Research then works on that selected evidence set to answer questions, compare findings, and generate structured outputs grounded in the records you chose.
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Your team can find publications mentioning an instrument. The harder question is what those mentions reveal about actual use.

Product-use Example with Table Output

What Are Researchers Doing With Our Instrument?

The question

Analyze publications that mention Instrument Z. Identify the sample, application, experimental role, and reported findings. Highlight applications worth investigating and flag records where actual use cannot be established.

Start with a defined evidence set

Use Research Analytics to identify relevant records through product names, model numbers, and naming variations. Refine the scope by date, topic, organization, or other relevant criteria.

Deep Research then investigates the details needed to answer the question.

Turn mentions into structured information

The analysis can be organized around fields such as:

  • Product or model
  • Sample or material
  • Application and experimental purpose
  • What was measured
  • Product’s role
  • Reported findings or limitations
  • Source record
  • Competitors mentioned
  • Article metadata such as journal, citation count, authors, etc.
  • ...and many user-defined fields

How is our spatial profiling platform actually being used in recent translational oncology research?

 

DOIUse confirmationActual application / use caseDisease / research settingMaterial / sample typeReadout capturedProduct role in workflowCompanion methodsAlternative / competitor mentionedReported signal
10.3187/tio.2026.0142ConfirmedImmune-niche profiling for PD-1 responseNSCLCFFPE pretreatment biopsiesImmune neighborhoods; PD-L1 spatial contextPrimary assayH&E, scRNA-seq, ctDNAMultiplex IF panelResponder/non-responder separation
10.3187/trm.2026.0274ConfirmedNeoadjuvant response stratificationTriple-negative breast cancerPaired pre/post-treatment tissueStromal remodeling; macrophage statesBiomarker layer in multimodal workflowBulk RNA-seq, digital pathology modelSpatial transcriptomicsResidual-disease signal
10.3187/mth.2026.0088ConfirmedCross-platform validation of cell-state annotationsColorectal cancerTissue microarraysProtein marker localizationBenchmark / orthogonal validationSpatial transcriptomics, image segmentationAlternative imaging platformReference-layer use
10.3187/pcl.2026.0195ConfirmedDrug-response phenotypingPancreatic cancerPatient-derived organoids + co-cultureApoptosis; proliferation; immune proximityScreening endpointCRISPR perturbation, live-cell imagingHigh-content imagingEmerging preclinical workflow
10.3187/onc.2026.0311UnresolvedProduct mention without extractable useMelanomaNot extractableNot extractableNot establishedOther methods describedNone extractableExclude from confirmed-use counts

Illustrative example; article details are fictional.

Use the findings to guide the next decision

A synthesis can help your team identify:

  • Recurring applications.
  • Less familiar applications worth investigating.
  • Differences between samples or workflows.
  • Explicitly reported benefits and limitations.
  • Evidence gaps requiring further review.

Review the classifications before using them to compare application activity or prioritize opportunities.

 

A mention is not proof of use. Use is not proof of effectiveness. A successful experiment is not automatically a product endorsement.

Investigate a Filtered Evidence Set in Chat Example

What delivery patterns and study-design signals define recent extrahepatic oligonucleotide research?

Selected evidence set: recent original research articles on siRNA, ASOs, antibody-oligonucleotide conjugates, LNP delivery, and other non-viral oligonucleotide platforms. Use chat to explore the selected set, then narrow by company, year, modality, journal, or target tissue. Here, records linked to Biogen, Ionis, Genentech, Pfizer, Novartis, and Eli Lilly help pharma, CRO, and CDMO teams compare delivery strategies and identify opportunities.
Publishing & Society Example

Where Should Our Journal or Society Focus Next?

A growing topic is a starting signal. Editorial planning requires a closer understanding of the science, the community, and the fit with your remit.

The question

Within our field, which emerging research approaches fit our scope but are less represented in our recent publications? Identify potential themes for editorial or scientific-program review.

1. Define the comparison in Research Analytics

Select the field, relevant journals or communities, and time window. Establish the comparison between your publications or program and the wider research activity.

2. Investigate what the records actually describe

Examine the research questions, methods, applications, and findings behind the topic labels. Review whether apparently similar publications address the same scientific development.

3. Extract a structured table

Investigate and share a table that classifies, segments documents into categories that fit your universe and question. 

4. Let the Research Analytics AI Filter Group Assistant build new filter representing segments

Count results in your own and competing journals. See where your gap lies and competition is accelerating. 

Build an editorial opportunity brief

Structure the findings around:

Candidate theme, scientific development and elevance to your scope. Lower representation is a reason to investigate - not proof of unmet demand. 

Which emerging oncology methods are driving highly cited articles in competitor journals, and how are they being used?

Which emerging oncology methods are driving highly cited articles in competitor journals, and how are they being used?

Bring Us Your Scientific Question

Tell us what you want to understand, which evidence matters, and whether you need chat-based investigation, a structured extraction, or a practical brief.

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Explore related Research Analytics solutions

See how Deep Research connects to the broader Research Analytics workflow—from finding the right evidence set to turning insights into commercial, editorial, and strategic action.

Platform Overview

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Publishing, Editorial & Audience Intelligence
Explore related Research Analytics solutions

FAQ

PubGrade Deep Research helps teams investigate selected scientific evidence and Research Analytics result sets in more detail. It uses your filtered results to generate evidence-backed answers, structured extraction tables, and practical briefs.

Yes. Use chat to investigate a selected evidence set with follow-up questions, or use structured extraction to pull defined fields across many records into a reviewable table.

That depends on your setup and available content. Deep Research can work with selected evidence from Research Analytics, including scientific literature and other supported source types. Contact us if you want to discuss a specific source or workflow.

Yes—Deep Research can investigate the selected evidence associated with an author, organization, company, journal, or other segment in Research Analytics. For example, it can summarize recurring methods, applications, targets, diseases, and scientific priorities in a selected body of work or organization-scoped evidence set.

Yes. Deep Research can help compare product use, applied methods, and more within selected studies and evidence sets.

Deep Research does not process content where the applicable license or rights do not permit that use. If you want to analyze gated or licensed publisher content, please confirm the relevant rights with the publisher or copyright holder and contact us to discuss permitted custom workflows for your evidence set.

Large-scale structured extraction across many records should generally be feasible, including workflows involving thousands of documents where appropriate. For especially large or complex projects, contact your Customer Success Manager or the PubGrade team so we can help scope the right setup.

Generic AI tools can help with individual questions, but research teams often need a defined evidence set, structured Research Analytics context, batch analysis, evidence review, and reusable workflows. PubGrade combines precise evidence selection in Research Analytics with Deep Research grounded in that selected corpus.

PubGrade does not use your inputs, documents, or results to train models. Where third-party AI models are used, PubGrade uses settings and provider terms intended to prevent customer data from being used for model training. We also handle customer data in line with our agreements and applicable privacy and confidentiality obligations. If you have specific security, privacy, or confidentiality requirements, contact us to discuss the appropriate setup.

Ask us about Research Analytics packages that include starter Deep Research credits, pilot options, or tailored bundles based on your use case and team size.