Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming

Dasgupta, Pritika; Hughes, James Alexander; Daley, Mark; Sejdić, Ervin

doi:10.1016/j.cmpb.2021.106104

Cited by 1 publication

(1 citation statement)

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“…Similarly, in study [ 219 ], a comprehensible risk model was generated to predict survival rate of breast cancer patients. Further applications are found in analyzing measurements towards hepatocellular carcinoma diagnosis (liver cancer) [ 220 ], estimating hemoglobin and glucose levels in blood by modelling key features from fingertip videos [ 221 ], pairing patients that show similar characteristics on the way to radiotherapy dose reconstruction and therefore improve the design of radiation treatments [ 222 ] or analyzing measurements of different body areas towards human walking modelling [ 223 ].…”

Section: Application In Science and Technologymentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

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Section: Application In Science and Technologymentioning

confidence: 99%