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Biology

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Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research.

Journal of biomedical semantics
BACKGROUND: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and met...

Testing the reproducibility and robustness of the cancer biology literature by robot.

Journal of the Royal Society, Interface
Scientific results should not just be 'repeatable' (replicable in the same laboratory under identical conditions), but also 'reproducible' (replicable in other laboratories under similar conditions). Results should also, if possible, be 'robust' (rep...

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

Biomacromolecules
This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of . The achievement of these tasks requir...

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy
Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be let...

AI revolutions in biology: The joys and perils of AlphaFold.

EMBO reports
AlphaFold is the most ground-breaking application of AI in science so far; it will revolutionize structural biology, but caution is warranted.

A guide to machine learning for biologists.

Nature reviews. Molecular cell biology
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models ...

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.

Biochimica et biophysica acta. Reviews on cancer
The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the com...

Biology transcends the limits of computation.

Progress in biophysics and molecular biology
Cognition-sensing and responding to the environment-is the unifying principle behind the genetic code, origin of life, evolution, consciousness, artificial intelligence, and cancer. However, the conventional model of biology seems to mistake cause an...

Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.

Plant reproduction
Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answe...