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我的最终目标是将从PubMed收到的元数据加载到 pyspark 数据框中。到目前为止，我已经设法使用 shell 脚本从 PubMed 数据库中下载了我想要的数据。下载的数据为 asn1 格式。以下是数据输入的示例：

Pubmed-entry ::= {
  pmid 31782536,
  medent {
    em std {
      year 2019,
      month 11,
      day 30,
      hour 6,
      minute 0
    },
    cit {
      title {
        name "Impact of CYP2C19 genotype and drug interactions on voriconazole
 plasma concentrations: a spain pharmacogenetic-pharmacokinetic prospective
 multicenter study."
      },
      authors {
        names std {
          {
            name ml "Blanco Dorado S",
            affil str "Pharmacy Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain.; Clinical
 Pharmacology Group, University Clinical Hospital, Health Research Institute
 of Santiago de Compostela (IDIS). Santiago de Compostela, Spain.; Department
 of Pharmacology, Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy,
 University of Santiago de Compostela (USC). Santiago de Compostela, Spain."
          },
          {
            name ml "Maronas O",
            affil str "Genomic Medicine Group, Centro Nacional de Genotipado
 (CEGEN-PRB3), CIBERER, CIMUS, University of Santiago de Compostela (USC),
 Santiago de Compostela, Spain."
          },
          {
            name ml "Latorre-Pellicer A",
            affil str "Genomic Medicine Group, Centro Nacional de Genotipado
 (CEGEN-PRB3), CIBERER, CIMUS, University of Santiago de Compostela (USC),
 Santiago de Compostela, Spain."
          },
          {
            name ml "Rodriguez Jato T",
            affil str "Pharmacy Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain."
          },
          {
            name ml "Lopez-Vizcaino A",
            affil str "Pharmacy Department, University Hospital Lucus Augusti
 (HULA). Lugo, Spain."
          },
          {
            name ml "Gomez Marquez A",
            affil str "Pharmacy Department, University Hospital Ourense
 (CHUO). Ourense, Spain."
          },
          {
            name ml "Bardan Garcia B",
            affil str "Pharmacy Department, University Hospital Ferrol (CHUF).
 A Coruna, Spain."
          },
          {
            name ml "Belles Medall D",
            affil str "Pharmacy Department, General University Hospital
 Castellon (GVA). Castellon, Spain."
          },
          {
            name ml "Barbeito Castineiras G",
            affil str "Microbiology Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain."
          },
          {
            name ml "Perez Del Molino Bernal ML",
            affil str "Microbiology Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain."
          },
          {
            name ml "Campos-Toimil M",
            affil str "Department of Pharmacology, Pharmacy and Pharmaceutical
 Technology, Faculty of Pharmacy, University of Santiago de Compostela (USC).
 Santiago de Compostela, Spain."
          },
          {
            name ml "Otero Espinar F",
            affil str "Department of Pharmacology, Pharmacy and Pharmaceutical
 Technology, Faculty of Pharmacy, University of Santiago de Compostela (USC).
 Santiago de Compostela, Spain."
          },
          {
            name ml "Blanco Hortas A",
            affil str "Epidemiology Unit. Fundacion Instituto de Investigacion
 Sanitaria de Santiago de Compostela (FIDIS), University Hospital Lucus
 Augusti (HULA), Spain."
          },
          {
            name ml "Duran Pineiro G",
            affil str "Clinical Pharmacology Group, University Clinical
 Hospital, Health Research Institute of Santiago de Compostela (IDIS).
 Santiago de Compostela, Spain."
          },
          {
            name ml "Zarra Ferro I",
            affil str "Pharmacy Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain.; Clinical
 Pharmacology Group, University Clinical Hospital, Health Research Institute
 of Santiago de Compostela (IDIS). Santiago de Compostela, Spain."
          },
          {
            name ml "Carracedo A",
            affil str "Genomic Medicine Group, Centro Nacional de Genotipado
 (CEGEN-PRB3), CIBERER, CIMUS, University of Santiago de Compostela (USC),
 Santiago de Compostela, Spain.; Galician Foundation of Genomic Medicine,
 Health Research Institute of Santiago de Compostela (IDIS), SERGAS, Santiago
 de Compostela, Spain."
          },
          {
            name ml "Lamas MJ",
            affil str "Clinical Pharmacology Group, University Clinical
 Hospital, Health Research Institute of Santiago de Compostela (IDIS).
 Santiago de Compostela, Spain."
          },
          {
            name ml "Fernandez-Ferreiro A",
            affil str "Pharmacy Department, University Clinical Hospital
 Santiago de Compostela (CHUS). Santiago de Compostela, Spain.; Clinical
 Pharmacology Group, University Clinical Hospital, Health Research Institute
 of Santiago de Compostela (IDIS). Santiago de Compostela, Spain.; Department
 of Pharmacology, Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy,
 University of Santiago de Compostela (USC). Santiago de Compostela, Spain."
          }
        }
      },
      from journal {
        title {
          iso-jta "Pharmacotherapy",
          ml-jta "Pharmacotherapy",
          issn "1875-9114",
          name "Pharmacotherapy"
        },
        imp {
          date std {
            year 2019,
            month 11,
            day 29
          },
          language "eng",
          pubstatus aheadofprint,
          history {
            {
              pubstatus other,
              date std {
                year 2019,
                month 11,
                day 30,
                hour 6,
                minute 0
              }
            },
            {
              pubstatus pubmed,
              date std {
                year 2019,
                month 11,
                day 30,
                hour 6,
                minute 0
              }
            },
            {
              pubstatus medline,
              date std {
                year 2019,
                month 11,
                day 30,
                hour 6,
                minute 0
              }
            }
          }
        }
      },
      ids {
        pubmed 31782536,
        doi "10.1002/phar.2351",
        other {
          db "ELocationID doi",
          tag str "10.1002/phar.2351"
        }
      }
    },
    abstract "BACKGROUND: Voriconazole, a first-line agent for the treatment
 of invasive fungal infections, is mainly metabolized by cytochrome P450 (CYP)
 2C19. A significant portion of patients fail to achieve therapeutic
 voriconazole trough concentrations, with a consequently increased risk of
 therapeutic failure. OBJECTIVE: To show the association between
 subtherapeutic voriconazole concentrations and factors affecting voriconazole
 pharmacokinetics: CYP2C19 genotype and drug-drug interactions. METHODS:
 Adults receiving voriconazole for antifungal treatment or prophylaxis were
 included in a multicenter prospective study conducted in Spain. The
 prevalence of subtherapeutic voriconazole troughs were analyzed in the rapid
 metabolizer and ultra-rapid metabolizer patients (RMs and UMs, respectively),
 and compared with the rest of the patients. The relationship between
 voriconazole concentration, CYP2C19 phenotype, adverse events (AEs), and
 drug-drug interactions was also assessed. RESULTS: In this study 78 patients
 were included with a wide variability in voriconazole plasma levels with only
 44.8% of patients attaining trough concentrations within the therapeutic
 range of 1 and 5.5 microg/ml. The allele frequency of *17 variant was found
 to be 29.5%. Compared with patients with other phenotypes, RMs and UMs had a
 lower voriconazole plasma concentration (RM/UM: 1.85+/-0.24 microg/ml versus
 other phenotypes: 2.36+/-0.26 microg/ml, ). Adverse events were more common
 in patients with higher voriconazole concentrations (p<0.05). No association
 between voriconazole trough concentration and other factors (age, weight,
 route of administration, and concomitant administration of enzyme inducer,
 enzyme inhibitor, glucocorticoids, or proton pump inhibitors) was found.
 CONCLUSION: These results suggest the potential clinical utility of using
 CYP2C19 genotype-guided voriconazole dosing to achieve concentrations in the
 therapeutic range in the early course of therapy. Larger studies are needed
 to confirm the impact of pharmacogenetics on voriconazole pharmacokinetics.",
    pmid 31782536,
    pub-type {
      "Journal Article"
    },
    status publisher
  }
}

这就是我卡住的地方。我不知道如何从 asn1 中提取信息并将其放入 pyspark 数据框中。任何人都可以提出这样做​​的方法吗？

4

2 回答 2

1

上述数据绝对是“ASN.1 格式”。这种格式称为 ASN.1 值表示法，用于以文本方式表示 ASN.1 值。（这种格式早于 JSON 编码规则的标准化。今天，人们可以将 JSON 用于相同目的，但与 ASN.1 值表示法相比，JSON 的处理方式存在一些差异）。

正如 YaFred 自己所指出的，YaFred 上面发布的 ASN.1 模式包含一些错误。您自己发布的符号似乎也包含一些错误。我查看了 NCBI 的整套 ASN.1 文件，发现它们包含几个错误。因此，除非它们被修复，否则它们无法由符合标准的 ASN.1 工具（例如 ASN.1 游乐场）处理。其中一些错误很容易修复，但修复其他错误需要了解这些文件作者的意图。这种情况可能是由于 NCBI 项目使用了他们自己的 ASN.1 工具包，该工具包可能以某种非标准方式使用 ASN.1。

我想在 NCBI 工具包中应该有一些方法可以让你解码上述值符号，所以如果我是你，我会研究那个工具包。我无法给你更好的建议，因为我不知道 NCBI 工具包。

于 2019-12-07T15:28:57.763 回答
0

您的问题可能并不简单，但值得尝试。

方法一：

当您拥有规范时，您可以尝试寻找将创建数据模型的 ASN.1 工具（又名 ASN.1 编译器）。在您的情况下，因为您下载了文本 ASN.1 值，所以您需要此工具来提供 ASN.1 值解码器。

如果该工具正在生成 Java 代码，它将如下所示：

// decode a Pubmed-entry
// input is your data
Asn1ValueReader reader = new Asn1ValueReader(input);
PubmedEntry obj = PubmedEntry.readPdu(reader);
// access the data
obj.getPmid();
obj.getMedent();

一些警告：

  • 可以做所有这些的工具不是免费的（如果你找到了）。这里的问题是您有一个文本 ASN1 值，而工具通常会提供二进制解码器（BER、DER 等..）
  • 您需要编写大量胶水代码来创建进入 pyspark 数据帧的记录

我前段时间写过这个，但它没有文本 ASN1 值解码器

方法二：

如果您的数据足够简单并且是文本数据，您可以尝试编写自己的解析器（使用像 ANTLR 之类的工具）......如果您不熟悉解析器，评估此方法并不容易。

编辑：不幸的是，规范无效。

于 2019-12-07T08:13:03.050 回答