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DeepMatrixLens (DML) converts scattered publications into structured Evidence Objects you can filter, compare, and verify across life science, chemistry, materials, energy, battery research, engineering, and applied physics.
Why this exists
Scientific output has outgrown human bandwidth. Even the best teams are forced to make decisions using incomplete slices of the literature, because manually reading and normalizing the full evidence base is impossible at scale.
PubMed alone contains over 30 million citations, and that’s only one part of the global research landscape.
Europe PMC describes itself as providing access to all PubMed abstracts and reports a corpus scale of 46M+ abstracts and 10.7M+ full‑text articles.
The result: time loss, duplicated experiments, missed negative evidence, and slow go/no‑go decisions.
What DeepMatrixLens does
DeepMatrixLens is an evidence intelligence layer for research heavy domains.
Instead of forcing humans to read thousands of papers end-to-end, DML converts research into structured, comparable evidence so teams can explore more of the literature with less manual triage.
Core outcomes
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Structured Evidence Objects (not just summaries)
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Filters that match how experts actually think (methods, conditions, endpoints)
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Evidence comparability across studies
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Traceable outputs (source-linked verification)
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Multi-field capability (one approach, multiple domains)
How it works (simple + credible)
1) Ingest
Open-access sources + your permitted datasets.
2) Extract
Turn unstructured text into Evidence Objects with context and provenance.
3) Explore
Ask questions, apply filters, compare results, and jump to sources for verification.
Use cases by field
Life science — drug discovery + disease connection
Example question:
“Which interventions show the strongest evidence for modulating a target in disease‑relevant models, under specific experimental conditions?”
Why this matters:
Broad biomedical queries can return an unreviewable volume of papers. Researchers typically reduce the pile using crude proxies (date range, journal prestige, keywords), which discards evidence blindly.
What DML enables:
DML helps users explore the full evidence space by making results comparable and filterable (model context, conditions, endpoints), then linking back to sources for verification.
Typical life-science workflows DML supports:
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Disease → pathway → intervention mapping
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Target and biomarker evidence exploration
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Comparing interventions across models and conditions
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Rapid shortlisting + source-linked validation
Chemistry — synthesis + reaction intelligence
Example question:
“What reaction conditions consistently improve yield/selectivity for a transformation across substrate scope—and what fails?”
Why this matters:
Chemistry evidence is scattered across papers, SI, inconsistent reporting styles, and conflicting experimental conditions.
What DML enables:
A structured way to compare conditions and outcomes across studies—helping chemists identify robust patterns, not just isolated wins.
Materials science — structure → processing → property
Example question:
“Which compositions and processing parameters correlate with a target property (strength, conductivity, stability), and under what measurement setup?”
What DML enables:
Evidence-centric exploration across composition, process, and test conditions—so teams iterate faster and waste less time on dead ends.
Energy
Solar energy:
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Example: “Which materials show stable performance under humidity/thermal cycling, not just ideal lab conditions?”
Hydrogen:
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Example: “Which catalysts perform best at realistic current densities and remain stable over time?”
Heat / thermal energy:
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Example: “Which phase-change or thermal-interface materials survive repeated cycling without degradation?”
Grid / systems:
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Example: “What evidence exists for reliability across different environments and duty cycles?”
Battery research
Example question:
“Which electrolyte additives reduce degradation for a target chemistry, and which failure modes remain?”
What DML enables:
Connecting results across different protocols (temperature, C‑rate, cycling regime) so conclusions aren’t based on apples-to-oranges comparisons.
Engineering & applied physics
Example question:
“Which design/process changes improve reliability and performance, and what are the trade-offs?”
What DML enables:
Comparable evidence across papers that report results in different formats—turning scattered claims into searchable, comparable evidence.
Our Vision
Our goal is to support R&D teams and universities in their quest for knowledge.
DeepMatrix Lens is dedicated to providing structured evidence from scientific literature, enabling researchers to efficiently analyze and compare diverse research fields.
Intelligent Solutions
Our platform converts extensive publications into organized evidence, facilitating in-depth searches and comparisons across fields like life sciences, chemistry, and engineering.
If DML saves just 5 hours/week per researcher:
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5 hours/week × 52 weeks/year = 260 hours/year saved
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At a fully-loaded cost of $70–$120/hour, that’s $18,200–$31,200 per researcher per year
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For a 10-person team: $182,000–$312,000/year in preventable literature overhead
The bigger savings often come from:
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Avoiding duplicated experiments
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Surfacing overlooked negative or contradictory evidence
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Faster go/no-go decisions before expensive work begins