Sixing Huang – Medium

Sixing Huang

Tailor a Multi-Model Chatbot for a Multi-Model DuckDB

A Human-in-the-loop chatbot capable of SQL, graph, vector, and full-text queries

Oct 30

Tailor a Multi-Model Chatbot for a Multi-Model DuckDB

Oct 30

DuckDB as a DrugDB: a Free and Simple Multi-Model Drug and Trial Database

A fourth case study of the clinical trials

Oct 15

DuckDB as a DrugDB: a Free and Simple Multi-Model Drug and Trial Database

Oct 15

Build a Drug Trial Database with the Multi-Model SurrealDB

A third case study of the clinical trials

Oct 1

Build a Drug Trial Database with the Multi-Model SurrealDB

Oct 1

PostgreSQL Goes Multi-Model: Graph, Vector, and SQL

A second case study of the clinical trials

Sep 18

PostgreSQL Goes Multi-Model: Graph, Vector, and SQL

Sep 18

Clinical Trial Search with Google Spanner: Graph, SQL, Vector, and LLM All in One Query

Build a multi-model database that connects drugs, disorders, and clinical trials

Sep 2

Clinical Trial Search with Google Spanner: Graph, SQL, Vector, and LLM All in One Query

Sep 2

Estimate House Price in Tokyo: the AutoML and MLOps Approach

Managing the H2O AutoML project with Weights & Biases

Aug 4

Estimate House Price in Tokyo: the AutoML and MLOps Approach

Aug 4

Published in
CodeX

Old Drugs, New Uses: the Graph Neural Network Approach

Using PyG’s link prediction to find new drug indications

May 8

Old Drugs, New Uses: the Graph Neural Network Approach

May 8

Published in
CodeX

Know Your Drugs: A Knowledge Graph for Drug-Drug-Disease Interactions

Build a shared database for drugmakers, FDA, doctors, and patients

Mar 1

Know Your Drugs: A Knowledge Graph for Drug-Drug-Disease Interactions

Mar 1

Published in
CodeX

Getting Insights from 3,000+ Clinical Trials in a Knowledge Graph

Build an analytic platform with Google Healthcare Natural Language API, the Unified Medical Language System, and Neo4j

Feb 5

Getting Insights from 3,000+ Clinical Trials in a Knowledge Graph

Feb 5

Published in
CodeX

Graph or Grove? Ecosystem Services Valuation with Graph Machine Learning and Random Forest

Evaluating Neo4j’s Graph Data Science, Sklearn and Google AutoML Tabular with the Ecosystem Services Valuation Database

Jan 1

Graph or Grove? Ecosystem Services Valuation with Graph Machine Learning and Random Forest

Jan 1

Sixing Huang

Sixing Huang

A Neo4j Ninja, German bioinformatician in Gemini Data. I like to try things: Cloud, ML, satellite imagery, Japanese, plants, and travel the world.

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