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Modern Molecular Taxonomy01:29

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Machine Learning Methods for Small Data Challenges in Molecular Science.

Bozheng Dou1, Zailiang Zhu1, Ekaterina Merkurjev2

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Small data challenges in molecular science are addressed by advanced machine learning (ML) and deep learning (DL) techniques. This review highlights solutions for issues like data diversity and high-dimensionality, leveraging big data advancements.

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Area of Science:

  • Molecular science, encompassing chemical and biological disciplines.
  • Focus on machine learning (ML) and deep learning (DL) applications.

Background:

  • Small data, constrained by time, cost, ethics, and technical limitations, is prevalent in research.
  • Despite big data's focus, small data challenges in ML/DL, including diversity, imputation, noise, imbalance, and high-dimensionality, are severe.
  • Advancements in ML, DL, and AI, spurred by big data, offer solutions for small data problems.

Purpose of the Study:

  • To review and analyze emerging solutions for small data challenges in molecular science.
  • To summarize progress in ML and DL for small data over the past decade.
  • To discuss promising future trends in this domain.

Main Methods:

  • Review of basic ML algorithms: linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), gradient boosting trees (GBT).
  • Analysis of advanced DL techniques: artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer.
  • Exploration of other methods: transfer learning, active learning, graph-based semi-supervised learning, hybrid ML/DL approaches, and physical model-based data augmentation.

Main Results:

  • Significant progress has been made in applying ML and DL to overcome small data limitations in molecular science.
  • Various advanced techniques, originally developed for big data, are proving effective for small data scenarios.
  • Emerging solutions address key issues such as data diversity, imputation, noise, imbalance, and high-dimensionality.

Conclusions:

  • Advanced ML and DL techniques provide powerful solutions for small data challenges in molecular science.
  • The synergy between big data technologies and small data needs is driving innovation.
  • Future research trends indicate continued development in specialized algorithms and hybrid approaches for small data problems.