scholarly journals Improving the classification of neuropsychiatric conditions using gene ontology terms as features

2019 ◽  
Vol 180 (7) ◽  
pp. 508-518 ◽  
Author(s):  
Thomas P. Quinn ◽  
Samuel C. Lee ◽  
Svetha Venkatesh ◽  
Thin Nguyen
Keyword(s):  
Gene Ontology ◽  
Neuropsychiatric Conditions

scholarly journals Improving the classification of neuropsychiatric conditions using gene ontology terms as features

2018 ◽  
Author(s):  
Thomas P. Quinn ◽  
Samuel C. Lee ◽  
Svetha Venkatesh ◽  
Thin Nguyen
Keyword(s):  
Gene Ontology ◽  
Feature Space ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Biological Importance ◽  
Process Database ◽  
High Level ◽  
Neuropsychiatric Conditions

AbstractAlthough neuropsychiatric disorders have a well-established genetic background, their specific molecular foundations remain elusive. This has prompted many investigators to design studies that identify explanatory biomarkers, and then use these biomarkers to predict clinical outcomes. One approach involves using machine learning algorithms to classify patients based on blood mRNA expression from high-throughput transcriptomic assays. However, these endeavours typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because informative genes do not necessarily have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the performance of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that improves the performance and stability of blood-based classifiers for neuropsychiatric conditions. We also show how annotation features can improve the interpretability of classifiers: since annotation databases are often used to assign biological importance to genes, annotation-based classifiers are easy to interpret because the biological importance of the features are the features themselves. We found that using annotations as features improves the performance and stability of classifiers. We also noted that the top ranked annotations tend contain the top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

Functional relationship and gene ontology classification of breast cancer biomarkers

2003 ◽  
Vol 18 (4) ◽  
pp. 241-272 ◽  
Author(s):  
C. Arciero ◽  
S.B. Somiari ◽  
C.D. Shriver ◽  
H. Brzeski ◽  
R. Jordan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Ontology ◽  
Functional Relationship ◽  
Cancer Biomarkers ◽  
Gene Ontology Classification ◽  
Breast Cancer Biomarkers

scholarly journals Molecular classification of IDH-mutant glioblastomas based on gene expression profiles

2019 ◽  
Vol 40 (7) ◽  
pp. 853-860 ◽  
Author(s):  
Fan Wu ◽  
Rui-Chao Chai ◽  
Zhiliang Wang ◽  
Yu-Qing Liu ◽  
Zheng Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Dna Replication ◽  
High Risk ◽  
Molecular Classification ◽  
Lower Grade ◽  
Diffuse Glioma ◽  
Cox Regression Analysis ◽  
Genome Atlas

Abstract Isocitrate dehydrogenase (IDH) mutant glioblastoma (GBM), accounts for ~10% GBMs, arises from lower grade diffuse glioma and preferentially appears in younger patients. Here, we aim to establish a robust gene expression-based molecular classification of IDH-mutant GBM. A total of 33 samples from the Chinese Glioma Genome Atlas RNA-sequencing data were selected as training set, and 21 cases from Chinese Glioma Genome Atlas microarray data were used as validation set. Consensus clustering identified three groups with distinguished prognostic and molecular features. G1 group, with a poorer clinical outcome, mainly contained TERT promoter wild-type and male cases. G2 and G3 groups had better prognosis differed in gender. Gene ontology analysis showed that genes enriched in G1 group were involved in DNA replication, cell division and cycle. On the basis of the differential genes between G1 and G2/G3 groups, a six-gene signature was developed with a Cox proportional hazards model. Kaplan–Meier analysis found that the acquired signature could differentiate the outcome of low- and high-risk cases. Moreover, the signature could also serve as an independent prognostic factor for IDH-mutant GBM in the multivariate Cox regression analysis. Gene ontology and gene set enrichment analyses revealed that gene sets correlated with high-risk group were involved in cell cycle, cell proliferation, DNA replication and repair. These finding highlights heterogeneity within IDH-mutant GBMs and will advance our molecular understanding of this lethal cancer.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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