Early-stage medical AI researcher; first author of HPOQuest, an LLM agent for ontology-guided rare-disease diagnosis
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
2
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-13) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored: publication output requires a fuller indexed record of this researcher's papers (counts, venues, authorship roles). The available catalog currently covers a single reviewed arXiv preprint, so productivity cannot be measured yet.
Impact—
Not yet scored: impact depends on citation counts and downstream uptake, which are unavailable in the current snapshot. This axis would measure how widely the work influences rare-disease diagnosis and medical AI research once citation data accumulates.
Focus0/100
Scores a perfect 100 on field concentration: in the current snapshot, 100% of the cataloged output — one paper — sits in computational biology / medical AI, with no dispersion into other fields. The line of research is tightly targeted at ontology-guided rare-disease diagnosis with LLM agents, though a single paper is a thin base for judging long-term focus.
Collaboration41/100
Scores 28, reflecting a compact network: only 3 distinct coauthors appear in the record. The HPOQuest paper was written first-author alongside Nassir Navab, Azade Farshad, and Annalisa Marsico, suggesting a small, mentorship-style, cross-disciplinary team rather than a broad multi-group collaboration. With one paper observed, the sample is minimal and this score should be read as provisional.
Activity52/100
Not yet scored: activity measures publication cadence over time (papers per year and recent output). The snapshot holds a single 2026 arXiv preprint, so no yearly rhythm or research trajectory can be established from the provided data.
Reproducibility55/100
Mean reproducibility score of 55, computed from one reviewed paper (reproPapers = 1) and rated medium readiness. HPOQuest's training-free design eases replication, but Evolve's review reports no significance tests, and results are mixed outside the small MME cohort. Reproducibility therefore looks plausible rather than proven, pending more reviewed papers.
Research summary
Kamilia Zaripova is an early-stage researcher in computational biology and medical AI whose visible profile is anchored by HPOQuest (arXiv, 2026), a first-authored paper introducing a rare-disease diagnostic agent. The method is training-free and performs sequential, active acquisition of patient phenotypes guided by the Human Phenotype Ontology, positioning LLM-agent questioning as clinical decision support for rare diseases, where candidate diagnoses must be narrowed from sparse evidence. Evolve's review finds that sequential HPO questioning improves sparse rare-disease diagnosis, with strong results on the small MME cohort but mixed results elsewhere, and notes that no significance tests are reported — hence a medium reproducibility readiness and a mean reproducibility score of 55 computed from this single reviewed paper. The bibliometric radar reflects a focused but very thin record: a perfect focus score of 100, with all cataloged output sitting in computational biology / medical AI; a collaboration score of 28 built on only 3 distinct coauthors — Nassir Navab, Azade Farshad, and Annalisa Marsico; and no scores yet for output, impact, or activity because citation, venue, and multi-year publication data are absent. The profile is name-resolved from public bibliographic data under ORCID 0000-0002-8200-0818, with no institution or country available. It should be read as an early-stage profile: a focused first-author contribution with senior co-authors, awaiting a broader publication record before productivity and impact can be judged.
Collaboration pattern
The visible network is compact: only 3 distinct coauthors in the current record. Zaripova wrote HPOQuest as first author alongside Nassir Navab, Azade Farshad, and Annalisa Marsico. A first-authored paper with a small, stable set of senior collaborators suggests a mentorship-style, cross-disciplinary team rather than a wide multi-group network; more papers would be needed to confirm recurring partners.
Research keywords
Highlights
HPOQuest: training-free rare-disease diagnosis via active phenotype acquisition
Evolve verdict: strong on the small MME cohort, mixed elsewhere
Medium reproducibility readiness (score 55) on a single reviewed paper
Timeline
- 2026Publishes HPOQuest on arXiv as first author: a training-free rare-disease diagnostic agent using active phenotype acquisition against the Human Phenotype Ontology.
- 2026Evolve reviews the paper, finding strong diagnostic gains on the small MME cohort, mixed results elsewhere, and no significance tests reported.
- 2026Enters the Evolve catalog with medium reproducibility readiness (mean reproducibility score 55, computed from this single reviewed paper).
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric dimensions and coauthor counts come from a thin public bibliographic snapshot (one reviewed paper, 3 distinct coauthors); all radar scores are provisional.
- No institution, country, or career history is available in the input data; the profile is name-resolved from public bibliographic data via ORCID 0000-0002-8200-0818.
- Output, impact, and activity axes are unscored because citation counts, venue history, and multi-year publication data are not yet available.
- Reproducibility (mean 55) rests on a single reviewed paper whose review notes that no significance tests were reported.