Microchromosomes and their association with human diseases

Supply table-S1: Commonly used patient derived cell lines, number of average microchromosomes, citation to original article. Supply table-S2: List of PubMed abstracts and annotation of disease identified by artificial intelligence-based technique related to incidence of microchromosomes in human Machine Learning_NER_code_output_20220120.zip: File containing the PubMed abstracts, machine learning analysis output and disease interpretation output. Cell lines karyotype.zip: File containing the raw karyotype data of all head & neck cancer cell lines. Brief description of methodology: To investigate the incidence of microchromosomes in human genome, we mine the PubMed literature for studies related to keywords “((microchromosome) OR ("marker chromosome") OR ("small chromosome"))” and applying the filter “human”. A total of 1,365 abstracts are obtained from PubMed as per date 08-Jan-2022. We analyze the PubMed abstracts using the Named Entity Recognition (NER) technique of Machine Learning (ML) implemented in Spacy (3.0) – scispaCy (0.4.0) – Python (3.7) running on Windows 11 system. The scispaCy package NER model “en_ner_bc5cdr_md” which is pretrained on BC5CDR corpus was used for disease entity recognition (https://allenai.github.io/scispacy/). Approximately 2000 disease entities are recognized by the model from the abstract text of the 1365 articles. The disease entities present in the abstract texts are extracted and then grouped together for most common broad disease classes as shown in excel file Supply_Table-S1.xlsx. The Python code, PubMed input and output files are available in "Machine Learning_NER_code_output_20220120.zip". Overall, inherited or somatically acquired microchromosomes in human individuals are frequently reported with diseases and disorders like Cancer, Trisomy, Turner’s syndrome, Epilepsy, Infertility, and Autism.补充表S1（Supply table-S1）：常用患者来源细胞系、平均微染色体(microchromosome)数目及原始文献引用信息。补充表S2（Supply table-S2）：基于人工智能技术识别的、与人类微染色体发生率相关的疾病注释信息及PubMed摘要列表。"Machine Learning_NER_code_output_20220120.zip"：包含PubMed摘要、机器学习(Machine Learning, ML)分析结果及疾病解读结果的压缩文件。"Cell lines karyotype.zip"：包含所有头颈部癌(head & neck cancer)细胞系的原始核型数据的压缩文件。方法学简要说明：为探究人类基因组中微染色体的发生率，我们以"(微染色体(microchromosome)) OR (标记染色体(marker chromosome)) OR (小染色体(small chromosome))"为关键词检索PubMed文献，并筛选人类相关研究。截至2022年1月8日，我们从PubMed中共检索得到1365篇摘要。我们采用运行于Windows 11系统、Python(3.7)环境下的Spacy(3.0)-scispaCy(0.4.0)工具包，借助机器学习的命名实体识别(Named Entity Recognition, NER)技术对上述PubMed摘要进行分析。本次分析选用在BC5CDR语料库上预训练的scispaCy工具包NER模型"en_ner_bc5cdr_md"完成疾病实体识别（模型来源：https://allenai.github.io/scispacy/）。该模型从1365篇文献的摘要文本中共识别出约2000个疾病实体。我们提取摘要文本中的疾病实体，并将其归类为常见的宽泛疾病类别，具体信息见Excel文件Supply_Table-S1.xlsx。相关Python代码、PubMed文献输入文件及分析输出文件均可在"Machine Learning_NER_code_output_20220120.zip"压缩包中获取。综上，人类个体中遗传性或体细胞获得性微染色体常与癌症(Cancer)、三体综合征(Trisomy)、特纳综合征(Turner’s syndrome)、癫痫(Epilepsy)、不育症(Infertility)及自闭症(Autism)等疾病或病症相关。