Draft — ISCB Awards and Competitions

MOTIVATION LETTER

A pre-registered, Bayesian-calibrated ODE model of reward-memory encoding in addiction confirmed all five of its hypotheses with posterior super-additivity of 13 to 22 percentage points across model versions. That model, the Conjunctive Consolidation Threshold framework, is one of four active research lines I have conducted as an independent computational researcher in Nigeria. I am applying to the ISCB Wikipedia/Wikidata Competition because my work across addiction neuroscience, protein machine learning, and dynamical-systems methods sits precisely at the intersection of pharmacology, neuroscience, and computational biology that the competition aims to make publicly accessible.

My independent research record demonstrates a commitment to open science and reproducible workflows. Every preprint is deposited on OSF and Zenodo with full code, data, and analysis pipelines. My hERG cardiotoxicity topology study was a pre-registered, powered replication that settled a comparison the published literature had never actually run: bipartite persistent homology does not beat a plain descriptor baseline (AUROC 0.8426 versus 0.8782). My TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings with Morgan drug fingerprints to predict drug-resistance mutations from sequence alone, achieving AUROC 0.804 on the Platinum benchmark while covering 100 percent of mutations versus approximately 18 percent for structure-limited tools. These results are concrete, reproducible, and directly relevant to computational biology content on Wikipedia and Wikidata.

The competition's goal of improving public access to high-quality computational biology knowledge aligns with my practice of publishing negative results directly rather than reframing them. My ergofluids project failed its first real-data gate; I reported that outcome in the pre-registered format. My interface-topology-for-resistance study discovered that topological constructs carry almost no signal for drug-resistance prediction (AUROC 0.425 and 0.485 on the Platinum benchmark), ruling out interface geometry as the driver. These are the kinds of results that Wikipedia and Wikidata entries rarely capture, yet they are essential for accurate scientific understanding.

I am currently enrolled in the M.Sc. Digital Health programme at Hasso Plattner Institute and the University of Potsdam, starting winter semester 2026/27. My eligibility as a student trainee fits the competition's open category. I have endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. My ORCID is 0009-0001-9272-6735, and my GitHub repository at github.com/AmunRaPtah contains the full codebases for all four active research lines.

I will contribute entries on the Conjunctive Consolidation Threshold model, the TOPOLOGIX method for sequence-based drug-resistance prediction, and the negative results from the hERG topology replication and the interface-topology-for-resistance study. Each entry will include citations to the preprints, links to the OSF and Zenodo repositories, and explanations of the methods in language accessible to a general computational biology audience.

SHORT ESSAY ON OPEN SCIENCE AND PUBLIC COMMUNICATION

Three sole-authored preprints from my CCT model project are each in review at a peer-reviewed journal: International Addiction Review and Treatment, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. Every preprint was deposited on OSF and Zenodo before submission, with the full Bayesian MCMC calibration code (PyMC DEMetropolisZ, 14 free parameters, literature-elicited priors from a 1,847-record screen) and the ODE solver (RK45) available on GitHub. This workflow ensures that any reader can reproduce the posterior super-additivity of 13 to 22 percentage points across model versions.

My hERG cardiotoxicity topology study was pre-registered on OSF before any analysis began. The pre-registration specified the opposition-distance metric, the Ripser and GUDHI software, and the primary comparison against a plain descriptor baseline. When the topological features did not beat that baseline (AUROC 0.8426 versus 0.8782), I reported the result directly rather than searching for a different framing. The pre-registration, data, and analysis code remain publicly accessible.

For the Wikipedia/Wikidata Competition, I will apply this same open-science discipline to content creation. Every entry will cite preprints, link to repositories, and include the negative results that are systematically missing from public knowledge bases. The competition's evaluation criteria emphasize quality and quantity of contributions, demonstrated commitment to open science, and public communication. My track record meets all three.

SHORT ESSAY ON RELEVANT EXPERIENCE AND SKILLS

My technical skills span the full stack of computational biology research. I write production-grade Python using scipy, numpy, pandas, PyMC for Bayesian MCMC calibration, and ODE solvers. I use Ripser and GUDHI for topological data analysis, RDKit for drug fingerprint generation, and ESM-2 for protein-language-model embeddings. I have built four independent DuckDB-based ingest-to-analyze corpus and RAG pipelines across life sciences, technology and AI security, and social science domains. I self-host local LLM serving with llama.cpp and manage production systems operations including Linux VPS, systemd, Caddy TLS, CI/CD, and automated backup and disaster recovery.

My employment history includes a current role as National Product Manager at Synthcare, a previous clinical pharmacist position at Ramset Pharmacy, and research assistant positions in NMDA and insulin docking at CDDDP and in antimicrobial resistance genomics and surveillance pipeline development at GHRU-GSAR. These roles give me direct experience with the computational tools and biological domains that the competition targets.

For the Wikipedia/Wikidata Competition, I will contribute entries on the CCT model, the TOPOLOGIX method, and the negative results from the hERG topology replication and the interface-topology-for-resistance study. Each entry will include the relevant preprints, repository links, and explanations of the methods. I will also add or update Wikidata entries for the software tools I use regularly: PyMC, Ripser, GUDHI, RDKit, and ESM-2.

CHECKLIST

- [ ] Confirm ISCB membership status; if not a member, apply for membership before submitting
- [ ] Verify eligibility for the Wikipedia/Wikidata Competition as a student trainee enrolled in M.Sc. Digital Health at HPI/Potsdam
- [ ] Prepare a list of planned Wikipedia and Wikidata contributions with specific article titles and Wikidata item IDs
- [ ] Gather links to all preprints on OSF and Zenodo for the CCT model, hERG topology study, interface-topology-for-resistance study, and TOPOLOGIX project
- [ ] Prepare a brief biography and research summary for the nomination or application form
- [ ] Confirm the submission deadline for the Wikipedia/Wikidata Competition; the ISCB website states rolling but may have specific cycles
- [ ] Verify that the competition accepts self-nominations; if not, identify a nominator from the list of endorsers (Berridge, Gershman, Daw, Mattar)
- [ ] Prepare a sample Wikipedia entry or Wikidata contribution to demonstrate quality and format

EDITOR NOTES

- Eligibility risk: The ISCB Wikipedia/Wikidata Competition is open to students and trainees at any level, which fits the applicant's current M.Sc. enrollment. However, the applicant is also employed as National Product Manager at Synthcare, which may affect trainee status. Verify the competition's definition of trainee.
- Fact verification needed: Confirm that the ISCB Wikipedia/Wikidata Competition accepts self-nominations. The deep research indicates self-nominations are accepted for awards, but the competition may have different rules.
- Gap: The applicant's profile does not include any prior Wikipedia or Wikidata editing experience. The application should acknowledge this and describe a plan for learning the platform's conventions, possibly by completing a Wikipedia training module before submitting.
- Gap: The applicant's profile does not include any prior ISCB membership or conference attendance. If membership is required or beneficial, the applicant should join before submitting.
- Gap: The applicant's profile does not specify which specific Wikipedia articles or Wikidata items they plan to edit. The motivation letter and essays should include concrete examples, not just general categories.

MOTIVATION LETTER

A pre-registered, Bayesian-calibrated ODE model of reward-memory encoding in addiction confirmed all five of its hypotheses with posterior super-additivity of 13 to 22 percentage points across model versions. That model, the Conjunctive Consolidation Threshold framework, is one of four active research lines I have conducted as an independent computational researcher in Nigeria. I am applying to the ISCB Wikipedia/Wikidata Competition because my work across addiction neuroscience, protein machine learning, and dynamical-systems methods sits precisely at the intersection of pharmacology, neuroscience, and computational biology that the competition aims to make publicly accessible.

My independent research record demonstrates a commitment to open science and reproducible workflows. Every preprint is deposited on OSF and Zenodo with full code, data, and analysis pipelines. My hERG cardiotoxicity topology study was a pre-registered, powered replication that settled a comparison the published literature had never actually run: bipartite persistent homology does not beat a plain descriptor baseline (AUROC 0.8426 versus 0.8782). My TOPOLOGIX project uses ESM-2 protein-language-model delta-embeddings with Morgan drug fingerprints to predict drug-resistance mutations from sequence alone, achieving AUROC 0.804 on the Platinum benchmark while covering 100 percent of mutations versus approximately 18 percent for structure-limited tools. These results are concrete, reproducible, and directly relevant to computational biology content on Wikipedia and Wikidata.

The competition's goal of improving public access to high-quality computational biology knowledge aligns with my practice of publishing negative results directly rather than reframing them. My ergofluids project failed its first real-data gate; I reported that outcome in the pre-registered format. My interface-topology-for-resistance study discovered that topological constructs carry almost no signal for drug-resistance prediction (AUROC 0.425 and 0.485 on the Platinum benchmark), ruling out interface geometry as the driver. These are the kinds of results that Wikipedia and Wikidata entries rarely capture, yet they are essential for accurate scientific understanding.

I am currently enrolled in the M.Sc. Digital Health programme at Hasso Plattner Institute and the University of Potsdam, starting winter semester 2026/27. My eligibility as a student trainee fits the competition's open category. I have endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. My ORCID is 0009-0001-9272-6735, and my GitHub repository at github.com/AmunRaPtah contains the full codebases for all four active research lines.

I will contribute entries on the Conjunctive Consolidation Threshold model, the TOPOLOGIX method for sequence-based drug-resistance prediction, and the negative results from the hERG topology replication and the interface-topology-for-resistance study. Each entry will include citations to the preprints, links to the OSF and Zenodo repositories, and explanations of the methods in language accessible to a general computational biology audience.

SHORT ESSAY ON OPEN SCIENCE AND PUBLIC COMMUNICATION

Three sole-authored preprints from my CCT model project are each in review at a peer-reviewed journal: International Addiction Review and Treatment, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. Every preprint was deposited on OSF and Zenodo before submission, with the full Bayesian MCMC calibration code (PyMC DEMetropolisZ, 14 free parameters, literature-elicited priors from a 1,847-record screen) and the ODE solver (RK45) available on GitHub. This workflow ensures that any reader can reproduce the posterior super-additivity of 13 to 22 percentage points across model versions.

My hERG cardiotoxicity topology study was pre-registered on OSF before any analysis began. The pre-registration specified the opposition-distance metric, the Ripser and GUDHI software, and the primary comparison against a plain descriptor baseline. When the topological features did not beat that baseline (AUROC 0.8426 versus 0.8782), I reported the result directly rather than searching for a different framing. The pre-registration, data, and analysis code remain publicly accessible.

For the Wikipedia/Wikidata Competition, I will apply this same open-science discipline to content creation. Every entry will cite preprints, link to repositories, and include the negative results that are systematically missing from public knowledge bases. The competition's evaluation criteria emphasize quality and quantity of contributions, demonstrated commitment to open science, and public communication. My track record meets all three.

SHORT ESSAY ON RELEVANT EXPERIENCE AND SKILLS

My technical skills span the full stack of computational biology research. I write production-grade Python using scipy, numpy, pandas, PyMC for Bayesian MCMC calibration, and ODE solvers. I use Ripser and GUDHI for topological data analysis, RDKit for drug fingerprint generation, and ESM-2 for protein-language-model embeddings. I have built four independent DuckDB-based ingest-to-analyze corpus and RAG pipelines across life sciences, technology and AI security, and social science domains. I self-host local LLM serving with llama.cpp and manage production systems operations including Linux VPS, systemd, Caddy TLS, CI/CD, and automated backup and disaster recovery.

My employment history includes a current role as National Product Manager at Synthcare, a previous clinical pharmacist position at Ramset Pharmacy, and research assistant positions in NMDA and insulin docking at CDDDP and in antimicrobial resistance genomics and surveillance pipeline development at GHRU-GSAR. These roles give me direct experience with the computational tools and biological domains that the competition targets.

For the Wikipedia/Wikidata Competition, I will contribute entries on the CCT model, the TOPOLOGIX method, and the negative results from the hERG topology replication and the interface-topology-for-resistance study. Each entry will include the relevant preprints, repository links, and explanations of the methods. I will also add or update Wikidata entries for the software tools I use regularly: PyMC, Ripser, GUDHI, RDKit, and ESM-2.

CHECKLIST

- [ ] Confirm ISCB membership status; if not a member, apply for membership before submitting
- [ ] Verify eligibility for the Wikipedia/Wikidata Competition as a student trainee enrolled in M.Sc. Digital Health at HPI/Potsdam
- [ ] Prepare a list of planned Wikipedia and Wikidata contributions with specific article titles and Wikidata item IDs
- [ ] Gather links to all preprints on OSF and Zenodo for the CCT model, hERG topology study, interface-topology-for-resistance study, and TOPOLOGIX project
- [ ] Prepare a brief biography and research summary for the nomination or application form
- [ ] Confirm the submission deadline for the Wikipedia/Wikidata Competition; the ISCB website states rolling but may have specific cycles
- [ ] Verify that the competition accepts self-nominations; if not, identify a nominator from the list of endorsers (Berridge, Gershman, Daw, Mattar)
- [ ] Prepare a sample Wikipedia entry or Wikidata contribution to demonstrate quality and format

EDITOR NOTES

- Eligibility risk: The ISCB Wikipedia/Wikidata Competition is open to students and trainees at any level, which fits the applicant's current M.Sc. enrollment. However, the applicant is also employed as National Product Manager at Synthcare, which may affect trainee status. Verify the competition's definition of trainee.
- Fact verification needed: Confirm that the ISCB Wikipedia/Wikidata Competition accepts self-nominations. The deep research indicates self-nominations are accepted for awards, but the competition may have different rules.
- Gap: The applicant's profile does not include any prior Wikipedia or Wikidata editing experience. The application should acknowledge this and describe a plan for learning the platform's conventions, possibly by completing a Wikipedia training module before submitting.
- Gap: The applicant's profile does not include any prior ISCB membership or conference attendance. If membership is required or beneficial, the applicant should join before submitting.
- Gap: The applicant's profile does not specify which specific Wikipedia articles or Wikidata items they plan to edit. The motivation letter and essays should include concrete examples, not just general categories.

AI Draft — ISCB Awards and Competitions