Glossary

Session 1 : Introduction to Biological Networks and Knowledge Graphs

Global concepts across all parts

Term	Definition
Network / graph	A set of nodes (objects) and edges (pairs of objects that are related). Everything else is derived from those two sets.
Node / vertex	One object in the graph. Here: a gene, a disease, or an ICD-10 code.
Edge	A relationship between two nodes.
Degree	The number of edges attached to a node. A high-degree node is a hub. In a curated graph, degree measures research attention as much as biological importance.
Path	A sequence of edges leading from one node to another. Paths let a graph answer questions nobody stored the answer to.
Connected component	A group of nodes all reachable from one another.
Density	Fraction of possible edges that actually exist. Real biological graphs are sparse (ours: 0.0046).
Clustering coefficient	How often a node’s neighbours are themselves connected. Near zero here, because the graph is close to bipartite.
Directed / undirected	Whether edges have a direction. is_a is genuinely directed; we work undirected but keep the direction as an edge attribute.
Self-loop	An edge from a node to itself. None in this graph.
—	—
Knowledge graph	A graph whose nodes and edges are typed and carry provenance — we can tell what kind of entity each node is, what kind of relationship each edge is, and on what evidence.
Computed network	A network whose edges are calculated from measurements — correlate every pair, keep what clears a threshold. Contrast with curated.
Curator / curation	A biologist employed by a database to read published papers and record their findings as structured entries (gene X, disease Y, evidence PMID Z). Also covers automated contributors — text mining, GWAS pipelines. Every edge in a knowledge graph exists because a curator or pipeline put it there.
Curated network	A network whose edges are asserted facts read out of a database. Can only contain what somebody already recorded.
Node type	The kind of entity a node represents: gene, disease, or icd10.
Edge type	The kind of relationship: associated_with, is_a, or maps_to.
Provenance	The evidence attached to an edge — here an association score and an evidence count.
Bipartite graph	A graph whose nodes split into two groups with edges only between groups, never within. Genes and diseases here are almost perfectly bipartite.
Projection	Collapsing a bipartite graph onto one node type, joining two nodes when they share neighbours. Turns “which diseases resemble each other?” into a structural question.
Jaccard index	Size of the intersection divided by size of the union. Used to score shared genes so that well-studied diseases do not automatically rank highest.
Sparsification	Removing edges to make a graph readable or to keep only strong relationships — by weight threshold, top percentage, or degree.

Part 1: What is a Network, and What is a Knowledge Graph?

Data sources

Term	Definition
Open Targets Platform	Drug-target discovery platform aggregating gene–disease evidence from genetics, somatic mutations, drugs, pathways, expression and literature. Licensed CC0 1.0 (public domain). Source of our genes, diseases, hierarchy and association scores.
MONDO Disease Ontology	Unified disease ontology merging several older vocabularies, and the source of our MONDO → ICD-10 cross-references. Licensed CC BY 4.0.
SSSOM	Simple Standard for Sharing Ontological Mappings — the file format MONDO publishes its cross-references in (mondo.sssom.tsv).
DisGeNET	A widely cited gene–disease database. Commercially licensed since 2024, so it cannot be redistributed in a public teaching repository.
Ensembl gene ID	Stable gene identifier of the form ENSG00000012048. The identifier space shared by Open Targets and the Session 2 TCGA-BRCA matrix, which is what lets the two join.
TCGA-BRCA	The Cancer Genome Atlas breast cancer cohort. Source of the expression matrix in coexpr_expression.csv.gz (500 patients × 737 genes) and of the multi-omics data in Session 2.

Co-expression (Part 1 §7)

Term	Definition
Co-expression	Two genes whose expression rises and falls together across patients. Evidence that they are regulated together — which may mean they work together, or merely that both track a third thing like proliferation.
Correlation matrix	Every gene correlated against every other. 737 genes give 271,216 pairs. The diagonal (each gene with itself) is zeroed, otherwise it dominates any ranking.
Correlation threshold	The cut-off above which a correlation becomes an edge. There is no principled value. Sweeping 0.3 → 0.8 takes the same data from 39,593 edges to 200, and every one of those networks is defensible.
Proliferation confound	The dominant structure in any tumour co-expression network: cell-cycle genes correlate with each other and with anything expressed in dividing cells. Responsible for TOP2A, BUB1B and KNL1 appearing next to BRCA1. Not a discovery.
Corroboration	The same relationship supported by two sources whose errors are independent — e.g. BRCA2–BRIP1 both curated as causal and co-expressed. Strong. Contrast with the tubulins, where both sources fail for related reasons.
Candidate	A gene co-expressed with a disease’s causal genes that carries no curated edge to that disease. Where gene–disease discovery lives — and where the proliferation confound also lands, indistinguishably.
Association score	Open Targets’ 0–1 summary of gene–disease evidence. Not a probability — a weighted aggregation across evidence types, so a high score can reflect genetics or drug treatment.
Evidence count	How many individual pieces of evidence support an association.
Evidence type (datatype)	The kind of claim behind an association: genetic_association, somatic_mutation, known_drug, literature, rna_expression, animal_model, affected_pathway, genetic_literature. Kept in kg_evidence.csv.
Causal evidence	genetic_association and somatic_mutation — the types asserting the gene helps cause the disease, as opposed to being a place to treat it.
known_drug evidence	An association arising because a drug hitting this target treats this disease. A statement about therapy, not aetiology — and the reason tubulins look like breast cancer genes.

Part 2: Building a Knowledge Graph with NetworkX

Term	Definition
NetworkX	The Python graph library used throughout. nx.Graph() is undirected, nx.DiGraph() directed.
Node attribute	Arbitrary data stored on a node — here type, name and extra.
Edge attribute	Arbitrary data stored on an edge — here type, and weight/evidence on association edges only.
Unweighted edge	An edge with no weight attribute. is_a and maps_to are unweighted; treating a missing weight as zero silently deletes them when thresholding.
Construction artefact	A property of the graph caused by how it was built rather than by the underlying biology — e.g. diseases are hubs here only because the build kept the top 30 genes per disease.
Annotation sparsity	Missing edges that should exist, because nobody has studied the relationship or because the evidence was recorded against a different term. The default state of biomedical knowledge graphs.
Vocabulary mismatch	When a fact exists but is filed under an equivalent-but-different ontology term — e.g. basal-like breast carcinoma has zero genes here because the evidence sits on triple-negative breast carcinoma.

Part 3: Querying the Knowledge Graph

Term	Definition
ICD-10	WHO International Classification of Diseases, 10th revision — the clinical coding system used in hospital records, death certificates and insurance claims.
ICD10CM	The US “Clinical Modification” of ICD-10, more finely subdivided than the WHO version.
ICD10WHO	The WHO’s international version of ICD-10.
ICD-O-3	International Classification of Diseases for Oncology — the separate classification that codes tumour morphology. This is where subtype information lives; ICD-10 does not carry it.
Granularity mismatch	Two vocabularies describing the same thing at different resolutions. ICD-10 subdivides breast cancer anatomically (by quadrant), so it has no code for a molecular subtype — and no data source can supply one.
Ontology	A structured vocabulary with defined relationships between terms. The is_a edges make ours ontology-backed rather than flat. An ontology is a network, not a tree — a term can have several parents (triple-negative breast carcinoma has three, one per receptor).
is_a / subsumption	“X is a kind of Y”. Directional and transitive, which is what makes climbing possible.
Ancestor	Any term reachable by following is_a edges upwards.
Ontology traversal / climbing	Following is_a edges to recover information missing on a specific term — raises ICD-10 coverage here from 19/90 to 82/90.
Inherited mapping	A code found on an ancestor rather than the node itself. Real, but a weaker claim: it describes the parent, and the mapping is many-to-one.
Cross-reference (xref)	A recorded correspondence between a term in one vocabulary and a term in another.
PAM50	A 50-gene expression signature classifying breast tumours into LumA, LumB, Basal, Her2 and Normal. The Session 2 prediction target — and all five labels are disease nodes in this graph.
Versioned Ensembl ID	An Ensembl ID carrying an annotation-release suffix (ENSG00000012048.23). TCGA uses these, Open Targets does not; joining without stripping the suffix matches nothing, silently.
HBOC	Hereditary Breast and Ovarian Cancer syndrome — driven by BRCA1/BRCA2/BRIP1, and recoverable from this graph purely by asking which genes breast and ovarian cancer share.
Community detection	Partitioning a graph into groups of nodes more connected to each other than to the rest. Louvain on our causal projection recovers cancers, autoimmune and metabolic diseases without being told they exist.
Modularity	What community detection optimises: how much denser the within-group connections are than chance would give.
Promiscuous gene	A gene associated with many unrelated diseases (TP53, tubulins). High degree, low specificity — a shared promiscuous gene rarely means shared biology.