AIMC Topic: Systems Biology

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Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

Omics : a journal of integrative biology
One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and planetary ecosystem health. For example, not only lung cancer (LC) and gastroesophageal reflux disease ...

Machine learning for the advancement of genome-scale metabolic modeling.

Biotechnology advances
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies...

Molecular causality in the advent of foundation models.

Molecular systems biology
Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attemp...

Thinking Beyond Disease Silos: Dysregulated Genes Common in Tuberculosis and Lung Cancer as Identified by Systems Biology and Machine Learning.

Omics : a journal of integrative biology
The traditional way of thinking about human diseases across clinical and narrow phenomics silos often masks the underlying shared molecular substrates across human diseases. One Health and planetary health fields particularly address such complexitie...

A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health.

Omics : a journal of integrative biology
A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability sc...

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Scientific reports
Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate me...

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Cell reports methods
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest...

AI-Aristotle: A physics-informed framework for systems biology gray-box identification.

PLoS computational biology
Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identificati...

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Frontiers in immunology
INTRODUCTION: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable...

Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements ...