AIMC Topic: Systems Biology
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Knowledge graphs and their applications in drug discovery.
Expert opinion on drug discovery
Apr 12, 2021
INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems b...
Using machine learning approaches for multi-omics data analysis: A review.
Biotechnology advances
Mar 29, 2021
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from ...
Incorporating Machine Learning into Established Bioinformatics Frameworks.
International journal of molecular sciences
Mar 12, 2021
The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and ge...
Quantitative PET in the 2020s: a roadmap.
Physics in medicine and biology
Mar 12, 2021
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in h...
Medical Oncology
History, 21st Century
History, 20th Century
Kinetics
Radiopharmaceuticals
Positron-Emission Tomography
Prognosis
Humans
Tomography, X-Ray Computed
Systems Biology
Artificial Intelligence
Imaging, Three-Dimensional
Neoplasms
Image Processing, Computer-Assisted
Positron Emission Tomography Computed Tomography
Reduction of quantitative systems pharmacology models using artificial neural networks.
Journal of pharmacokinetics and pharmacodynamics
Mar 2, 2021
Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output rel...
Unsupervised manifold learning of collective behavior.
PLoS computational biology
Feb 12, 2021
Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generat...
Cognitive analysis of metabolomics data for systems biology.
Nature protocols
Jan 22, 2021
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. M...
An ensemble learning approach for modeling the systems biology of drug-induced injury.
Biology direct
Jan 12, 2021
BACKGROUND: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being ...
A protein folding robot driven by a self-taught agent.
Bio Systems
Dec 29, 2020
This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to sol...
Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.
International journal of molecular sciences
Dec 26, 2020
In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for t...
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