Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models

Perez Cortes, Victoria M.; Chatterjee, Arnab; Khovalyg, Dolaana

doi:10.1016/j.bspc.2023.105381

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105381

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Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models

Victoria M. Perez Cortes,

Arnab Chatterjee,

Dolaana Khovalyg

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Unveiling the World of Bioinformatics

Dhiman,

Dhiman

2024

Applying Machine Learning Techniques to Bioinformatics

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This chapter take a fascinating voyage across the complex bioinformatics landscapes. The authors begin by defining bioinformatics as the essential connection that connects unprocessed biological data to useful knowledge, laying the groundwork for an investigation into the core fields of proteomics, genomics, and data mining. The story flows naturally, showing how feature refinement techniques, machine learning's predictive power, and deep learning's revolutionary ability to understand intricate biological processes all work together in harmony. Applications in the real world emerge, demonstrating the palpable effects of bioinformatics on drug discovery, the prediction of protein functionality, and the rapidly expanding field of personalised medicine. But this journey is not without its difficulties; we must negotiate the nuances of data integration, contend with the interpretability of machine learning models, fight with moral dilemmas, and face the computing demands of big data. Every obstacle turns into a doorway for creativity.

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Unveiling the World of Bioinformatics

Dhiman,

Dhiman

2024

Applying Machine Learning Techniques to Bioinformatics

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Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models

Cited by 1 publication

References 28 publications

Unveiling the World of Bioinformatics

Unveiling the World of Bioinformatics

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