Machine Learning and Its Applications to Biology

By Adi L. Tarca, Vincent J. Carey, Xue-wen Chen, Roberto Romero, Sorin Draghici. 2007

635 citations recorded by the source corpus.

The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechanization should be acknowledged when considering machine learning in broad terms. Firstly, it is intended that the classification and prediction tasks can be accomplished by a suitably programmed computing machine. That is, the product of machine learning is a classifier that can be feasibly used on available hardware. Secondly, it is intended that the creation of the classifier should itself be highly mechanized, and should not involve too much human input. This second facet is inevitably vague, but the basic objective is that the use of automatic algorithm construction methods can minimize the possibility that human biases could affect the selection and performance of the algorithm. Both the creation of the algorithm and its operation to classify objects or predict events are to be based on concrete, observable data. The history of relations between biology and the field of machine learning is long and complex. An early technique [1] for machine learning called the perceptron constituted an attempt to model actual neuronal behavior, and the field of artificial neural network (ANN) design emerged from this attempt. Early work on the analysis of translation initiation sequences [2] employed the perceptron to define criteria for start sites in Escherichia coli. Further artificial neural network architectures such as the adaptive resonance theory (ART) [3] and neocognitron [4] were inspired from the organization of the visual nervous system. In the intervening years, the flexibility of machine learning techniques has grown along with mathematical frameworks for measuring their reliability, and it is natural to hope that machine learning methods will improve the efficiency of discovery and understanding in the mounting volume and complexity of biological data. This tutorial is structured in four main components. Firstly, a brief section reviews definitions and mathematical prerequisites. Secondly, the field of supervised learning is described. Thirdly, methods of unsupervised learning are reviewed. Finally, a section reviews methods and examples as implemented in the open source data analysis and visualization language R (http://www.r-project.org).

Subfields: Molecular Biology

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Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, methodology): “portant tasks. For example, classiﬁcation and regression models are employed to analyze current and historical trends to make predictions in ﬁnancial markets [24, 33, 8], to study biological problems [54], to support medical diagnosis [30, 42, 57], to classify network traﬃc or detect anomalies [22, ”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “Thus, many techniques of Artificial Intelligence (AI), in particular machine learning (ML), have been proposed over time to facilitate recognition, classification, and prediction of patterns in biological data [12].”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Chief among these analyses are machine-learning methods that encompass data-driven models for both supervised and unsupervised learning (Tarca et al., 2007).”
Experimental analysis of new algorithms for learning ternary classifiers (2015, background): “In such setting, the ratio N/p is so small that both feature selection and sparse learning is required to diminish the risk of overfitting [24].”
A Review on UAV-Based Applications for Precision Agriculture (2019, background): “…for prediction and/or identiﬁcation purposes, with great results in many domains, such as medical systems [126,127], marketing [128], biology [129], etc. Machine learning techniques are often been applied in Precision Agriculture to exploit the information from the large amount of data…”
High-Content Screening for Quantitative Cell Biology. (2016, background): “In supervised machine learning, algorithms such as logistic regression, random forests, and support vector machines automatically discover novel positive items from training data (reviewed in [41]).”
Ten quick tips for machine learning in computational biology (2017, background): “If yes, your problem can be attributed to the supervised learning category of tasks, and, if not, to the the unsupervised learning category [4].”
Support Vector Machines and Kernels for Computational Biology (2008, background): “Techniques such as N - fold cross-validation can help if the parts become too small to reliably measure prediction performance (see, for example, [47,48]).”
The in silico human surfaceome (2018, background): “Machine-learning algorithms enable pattern recognition, classification, and prediction based on models derived from existing data (35).”
Deep Learning in Mining Biological Data (2020, methodology): “This triggered the development of rational, reliable, reusable, rigorous, and robust software tools [11] using machine learning (ML) based methods to facilitate recognition, classification, and prediction of patterns in the biological big data [13].”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, methodology): “Commonly used methods for building unsupervised models include hierarchical or K-mean clustering (85).”
VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology (2017, background): “In silico prediction of vaccine candidates has great significance in various life science disciplines, including biomedical research [1].”