Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Item
http://www.wikidata.org/entity/Q38835169
Proceedings of the third international molecular pathological epidemiology (MPE) meeting.Influence of wound fluid on chemotherapy sensitivity in primary breast cancer cells.Prediction of novel target genes and pathways involved in irinotecan-resistant colorectal cancer.Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer.A PRObabilistic Pathway Score (PROPS) for Classification with Applications to Inflammatory Bowel Disease.Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.Thymoquinone synergizes gemcitabine anti-breast cancer activity via modulating its apoptotic and autophagic activities
P2860

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Item
http://www.wikidata.org/entity/Q38835169
description
2015 nî lūn-bûn
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2015年の論文
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2015年論文
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2015年論文
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2015年論文
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2015年論文
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2015年論文
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2015年论文
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2015年论文
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2015年论文
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name
Genomic signatures for paclita ...... r derived by machine learning.
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type
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Genomic signatures for paclita ...... r derived by machine learning.
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prefLabel
Genomic signatures for paclita ...... r derived by machine learning.
@en
P1433
P1476
P2093
P2860
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Genomic signatures for paclita ...... r derived by machine learning.
@en
P2093
P2860
P304
P31
P356
10.1016/J.MOLONC.2015.07.006
P433
P478
P50
P577
P5875
P698
P921
P932