“Molecular computation of solutions to combinatorial problems”
E199754
“Molecular computation of solutions to combinatorial problems” is Leonard Adleman’s pioneering 1994 paper that introduced DNA computing by demonstrating how molecular biology techniques can solve a combinatorial search problem.
All labels observed (1)
| Label | Occurrences |
|---|---|
| “Molecular computation of solutions to combinatorial problems” canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1778795 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “Molecular computation of solutions to combinatorial problems” Context triple: [Leonard Adleman, notableWork, “Molecular computation of solutions to combinatorial problems”]
-
A.
P, NP, and NP-Completeness: The Basics of Complexity Theory
"P, NP, and NP-Completeness: The Basics of Complexity Theory" is a foundational textbook by Oded Goldreich that introduces the core concepts, problems, and techniques of computational complexity theory, with a focus on the classes P, NP, and NP-complete problems.
-
B.
Computing with Register Machines
"Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
-
C.
“Inapproximability results for SAT and other problems”
“Inapproximability results for SAT and other problems” is a seminal theoretical computer science paper by Johan Håstad that establishes tight hardness-of-approximation bounds for satisfiability and related optimization problems using probabilistically checkable proofs.
-
D.
Introduction to the Theory of Computation
Introduction to the Theory of Computation is a widely used textbook in theoretical computer science that covers formal languages, automata, computability, and complexity theory.
-
E.
On Computable Numbers with an Application to the Entscheidungsproblem
"On Computable Numbers, with an Application to the Entscheidungsproblem" is Alan Turing’s landmark 1936 paper that introduced the Turing machine model and founded the formal study of computability and the limits of algorithmic decision procedures.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: “Molecular computation of solutions to combinatorial problems” Target entity description: “Molecular computation of solutions to combinatorial problems” is Leonard Adleman’s pioneering 1994 paper that introduced DNA computing by demonstrating how molecular biology techniques can solve a combinatorial search problem.
-
A.
P, NP, and NP-Completeness: The Basics of Complexity Theory
"P, NP, and NP-Completeness: The Basics of Complexity Theory" is a foundational textbook by Oded Goldreich that introduces the core concepts, problems, and techniques of computational complexity theory, with a focus on the classes P, NP, and NP-complete problems.
-
B.
Computing with Register Machines
"Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
-
C.
“Inapproximability results for SAT and other problems”
“Inapproximability results for SAT and other problems” is a seminal theoretical computer science paper by Johan Håstad that establishes tight hardness-of-approximation bounds for satisfiability and related optimization problems using probabilistically checkable proofs.
-
D.
Introduction to the Theory of Computation
Introduction to the Theory of Computation is a widely used textbook in theoretical computer science that covers formal languages, automata, computability, and complexity theory.
-
E.
On Computable Numbers with an Application to the Entscheidungsproblem
"On Computable Numbers, with an Application to the Entscheidungsproblem" is Alan Turing’s landmark 1936 paper that introduced the Turing machine model and founded the formal study of computability and the limits of algorithmic decision procedures.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
research article
ⓘ
scientific paper ⓘ |
| author |
Leonard Adleman
ⓘ
Leonard Adleman ⓘ
surface form:
Leonard M. Adleman
|
| citedAs | first experimental demonstration of DNA computing ⓘ |
| computationalModel | biochemical reaction system ⓘ |
| contribution |
founded field of DNA computing
ⓘ
showed that molecular biology operations can implement computation ⓘ |
| countryOfPublication |
United States of America
ⓘ
surface form:
United States
|
| demonstratedProperty |
feasibility of computation with DNA
ⓘ
massive parallelism of molecular reactions ⓘ |
| demonstratedSolutionTo | Hamiltonian path problem ⓘ |
| era | early DNA computing research ⓘ |
| field |
DNA computing
ⓘ
computational complexity theory ⓘ molecular biology ⓘ molecular computing ⓘ theoretical computer science ⓘ |
| hasAuthorAffiliation | University of Southern California ⓘ |
| hasMedium |
digital
ⓘ
print ⓘ |
| impact | highly cited in DNA computing literature ⓘ |
| inspiredField |
biomolecular computing
ⓘ
unconventional computing ⓘ |
| introducedConcept |
DNA computing
ⓘ
molecular computation ⓘ |
| language | English ⓘ |
| problemType | combinatorial search problem ⓘ |
| publicationDecade | 1990s ⓘ |
| publicationYear | 1994 ⓘ |
| publishedIn | Science ⓘ |
| publisher | American Association for the Advancement of Science ⓘ |
| relatedTo |
NP-completeness
ⓘ
algorithmic self-assembly ⓘ graph theory ⓘ |
| subjectOf |
historical analyses of DNA computing
ⓘ
surveys on molecular computation ⓘ |
| topic |
encoding combinatorial problems in molecular structures
ⓘ
use of DNA to solve NP-complete problems ⓘ |
| usedRepresentation | graph encoded in DNA strands ⓘ |
| usedSubstrate | DNA ⓘ |
| usedTechnique |
DNA ligation
ⓘ
gel electrophoresis ⓘ molecular biology techniques ⓘ polymerase chain reaction ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: “Molecular computation of solutions to combinatorial problems” Description of subject: “Molecular computation of solutions to combinatorial problems” is Leonard Adleman’s pioneering 1994 paper that introduced DNA computing by demonstrating how molecular biology techniques can solve a combinatorial search problem.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.