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Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing
In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization. [2] Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures. [2]
DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures.The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. [2]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. [ 1 ] His interests spanned several areas of artificial intelligence , language technologies and machine learning .
A computer-assisted design (CAD) tool for synthetic biology, used to design genetic constructs based on grammar rules. Linux, macOS, Windows: Apache License 2.0 GenoCAD Team (Virginia Bioinformatics Institute) Genomespace: Centralized web application that provides data format transformations and facilitates connections with other bioinformatics ...
Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons , which are mathematical functions that are designed to be analogous to the mechanisms used by neural circuits .
Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at Uber stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent deep learning algorithms, in part because neuroevolution was found to be less likely to get stuck in local minima.