Madan Babu
E687339
Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Madan Babu canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7663580 — 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: Madan Babu Context triple: [Francis Crick Medal and Lecture, notableRecipient, Madan Babu]
-
A.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
D.
Amar Nath Chatterjee
Amar Nath Chatterjee is an Indian politician who has served as the mayor of Asansol in West Bengal.
-
E.
Sanjeev Kumar
Sanjeev Kumar was a highly acclaimed Indian film actor known for his versatile performances in both mainstream and parallel cinema during the 1960s and 1970s.
- 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: Madan Babu Target entity description: Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
-
A.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
B.
Paresh Babu
Paresh Babu is a central fictional character in Rabindranath Tagore’s Bengali novel "Gora," representing complex social and philosophical themes in colonial India.
-
C.
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
D.
Amar Nath Chatterjee
Amar Nath Chatterjee is an Indian politician who has served as the mayor of Asansol in West Bengal.
-
E.
Sanjeev Kumar
Sanjeev Kumar was a highly acclaimed Indian film actor known for his versatile performances in both mainstream and parallel cinema during the 1960s and 1970s.
- F. None of above. chosen
Statements (37)
| Predicate | Object |
|---|---|
| instanceOf |
computational biologist
ⓘ
scientist ⓘ |
| awardReceived |
EMBO Gold Medal
NERFINISHED
ⓘ
EMBO Young Investigator Award NERFINISHED ⓘ Fellow of the Royal Society NERFINISHED ⓘ Royal Society Wolfson Research Merit Award NERFINISHED ⓘ |
| educatedAt |
MRC Laboratory of Molecular Biology
NERFINISHED
ⓘ
University of Madras NERFINISHED ⓘ |
| fieldOfWork |
computational biology
ⓘ
gene regulation ⓘ genomics ⓘ molecular biology ⓘ protein networks ⓘ regulatory genomics ⓘ structural biology ⓘ systems biology ⓘ |
| hasCitizenship | India NERFINISHED ⓘ |
| knownFor |
integrating computational and experimental approaches in biology
ⓘ
studies of intrinsically disordered proteins in regulation ⓘ systems-level analysis of cellular regulation ⓘ work on gene regulatory networks ⓘ work on protein interaction networks ⓘ |
| languageSpoken |
English
ⓘ
Tamil NERFINISHED ⓘ |
| memberOf |
European Molecular Biology Organization
NERFINISHED
ⓘ
Royal Society ⓘ |
| notableWork |
computational models of gene regulatory circuits
ⓘ
high-throughput analysis of transcription factor binding ⓘ studies on network motifs in biological systems ⓘ |
| occupation |
group leader
ⓘ
researcher ⓘ |
| researchInterest |
cellular information processing
ⓘ
evolution of regulatory networks ⓘ intrinsically disordered regions in proteins ⓘ signal transduction networks ⓘ transcriptional regulation ⓘ |
| workLocation | Cambridge, United Kingdom NERFINISHED ⓘ |
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: Madan Babu Description of subject: Madan Babu is a computational biologist known for his influential work on gene regulation, protein networks, and systems biology.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.