AIMC Topic: Kinetochores

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Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets.

Nature communications Nov 11, 2021
Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning mo...
Machine Learning Genome-Wide Association Study Systems Biology Candida albicans Antifungal Agents Kinetochores
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DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types.

BMC medical genomics Feb 24, 2020
BACKGROUND: Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an import...
Humans Female Breast Neoplasms Biomarkers, Tumor Transcriptome Deep Learning RNA-Seq Models, Biological RNA, Neoplasm Kinetochores
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Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data.

Molecular biology of the cell Jan 5, 2017
Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about thei...
Machine Learning Computer Simulation Proteomics Structure-Activity Relationship DNA-Binding Proteins Datasets as Topic Adenosine Triphosphatases Multiprotein Complexes Kinetochores
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