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Interfacing Biology and Electronics with Memristive Materials.

Advanced materials (Deerfield Beach, Fla.)
Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technologica...

Cartography of the multiple formal systems of molecular autopoiesis: from the biology of cognition and enaction to anticipation and active inference.

Bio Systems
A rich literature has grown up over the years that bears with autopoiesis, which tends to assume that it is a model, a theory, a principle, a definition of life, a property, refers to self-organization or even to hastily conclude that it is hylomorph...

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology.

Journal of chemical information and modeling
The recent breakthrough made in the field of three-dimensional (3D) structure prediction by artificial intelligence softwares, such as initially AlphaFold2 (AF2) and RosettaFold (RF) and more recently large Language Models (LLM), has revolutionized t...

Transfer learning enables predictions in network biology.

Nature
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...

The neuromechanics of animal locomotion: From biology to robotics and back.

Science robotics
Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating...

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

BMC bioinformatics
BACKGROUND: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, whi...

Advancing structural biology through breakthroughs in AI.

Current opinion in structural biology
The past century has witnessed an exponential increase in our atomic-level understanding of molecular and cellular mechanisms from a structural perspective, with multiple landmark achievements contributing to the field. This, coupled with recent and ...

AlphaFold2 and its applications in the fields of biology and medicine.

Signal transduction and targeted therapy
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most chal...

Collective computational intelligence in biology - Emergence of memory in somatic tissues.

Bio Systems
Role of memory in the function of biological tissues, organs and organisms remains unexplored with many unanswered questions. In this study, the emergence of associative memory in somatic (non-neural) tissues and its potential relation to tissue func...

Deep reinforcement learning for optimal experimental design in biology.

PLoS computational biology
The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence-reinforcement learning-to the optima...