use hpo::Ontology;
    let ontology = Ontology::from_binary("your-hpo-binary.hpo").unwrap();

3. 另一种可能性是使用来自此 crate 的 Github 仓库 的快照，该快照包含本体论的二进制构建 https://github.com/anergictcell/hpo/blob/main/tests/ontology.hpo。它可能不会总是最新的，所以请自行核实。

本体

use hpo::{Ontology, HpoTermId};
use hpo::annotations::{GeneId, OmimDiseaseId, OrphaDiseaseId};

fn example() {
    let ontology = Ontology::from_standard("/path/to/master-data/").unwrap();

    // iterate HPO terms
    for term in &ontology {
        // do something with term
    }

    // iterate Genes
    for gene in ontology.genes() {
        // do something with gene
    }

    // iterate omim diseases
    for disease in ontology.omim_diseases() {
        // do something with disease
    }

    // iterate orpha diseases
    for disease in ontology.orpha_diseases() {
        // do something with disease
    }

    // get a single HPO term using HPO ID
    let hpo_id = HpoTermId::try_from("HP:0000123").unwrap();
    let term = ontology.hpo(hpo_id);

    // get a single HPO term using `u32` part of HPO ID
    let term = ontology.hpo(123u32);

    // get a single Omim disease
    let disease_id = OmimDiseaseId::from(12345u32);
    let disease = ontology.omim_disease(&disease_id);

    // get a single Orpha disease
    let disease_id = OrphaDiseaseId::from(12345u32);
    let disease = ontology.orpha_disease(&disease_id);

    // get a single Gene
    let hgnc_id = GeneId::from(12345u32);
    let gene = ontology.gene(&hgnc_id);

    // get a single Gene by its symbol
    let gene = ontology.gene_by_name("GBA");

}

HPO 术语

use hpo::Ontology;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();

    let term = ontology.hpo(123u32).unwrap();

    assert_eq!("Abnormality of the nervous system", term.name());
    assert_eq!("HP:000123".to_string(), term.id().to_string());

    // iterate all parents
    for p in term.parents() {
        println!("{}", p.name())
    }

    // iterate all children
    for p in term.children() {
        println!("{}", p.name())
    }

    let term2 = ontology.hpo(1u32).unwrap();

    assert!(term2.parent_of(&term));
    assert!(term.child_of(&term2));
}

相似性

use hpo::Ontology;
use hpo::similarity::GraphIc;
use hpo::term::InformationContentKind;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();
    let term1 = ontology.hpo(123u32).unwrap();
    let term2 = ontology.hpo(1u32).unwrap();

    let ic = GraphIc::new(InformationContentKind::Omim);
    let similarity = term1.similarity_score(&term2, &ic);
}

富集

识别哪些基因（或疾病）在 HpoTerms 集合中富集，例如在患者的临床信息或患者队列中

use hpo::Ontology;
use hpo::{HpoSet, term::HpoGroup};
use hpo::stats::hypergeom::gene_enrichment;

fn example() {
    let ontology = Ontology::from_binary("/path/to/binary.hpo").unwrap();

    let mut hpos = HpoGroup::new();
    hpos.insert(2943u32);
    hpos.insert(8458u32);
    hpos.insert(100884u32);
    hpos.insert(2944u32);
    hpos.insert(2751u32);
    let patient_ci = HpoSet::new(&ontology, hpos);

    let mut enrichments = gene_enrichment(&ontology, &patient_ci);

    // the results are not sorted by default
    enrichments.sort_by(|a, b| {
        a.pvalue().partial_cmp(&b.pvalue()).unwrap()
    });

    for gene in enrichments {
        println!("{}\t{}\t({})", gene.id(), gene.pvalue(), gene.enrichment());
    }
}

基准

俗话说：“先做出来，再做的好，最后做得快”。工作和好的部分已在PyHPO中实现。尽管我尽力使其快，但我仍然渴望更多。因此，我在2022年12月开始开发hpo Rust库。即使在没有像为PyHPO那样进行微基准测试和性能调优的情况下，hpo现在确实已经快得多。

以下基准测试是在非科学条件下运行的，您的结果可能会有所不同。我使用了MacBook Air M1，rustc 1.68.0，Python 3.9和/usr/bin/time进行计时。

技术设计

有关实现计划的详细信息，请参阅技术设计文档