Application of Artificial Intelligence and Machine Learning in Biology

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cell Methods".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 8904

Special Issue Editors


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Guest Editor
Institut de Recherche en Cancérologie de Montpellier, Cancer Bioinformatics and Systems Biology Lab, 34090 Montpellier, France
Interests: systems biology; computational proteomics; cancer; tumor microenvironment; functional analysis of biological networks; multi-omics; machine learning

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Guest Editor
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL, USA
Interests: unsupervised learning; cybersecurity; datamining; machine learning; critical infrastructure; Artificial Intelligence

Special Issue Information

Dear Colleagues,

The use of artificial intelligence and machine learning is transforming the way biologists conduct research, interpret their findings, and apply them to solve problems in biology. As science becomes more interdisciplinary, researchers are leveraging the capabilities of machine learning to solve emerging problems in biology. The application of machine learning is solving an array of research problems, such as classification of cellular images, genome analysis, and drug discovery, and in correlating the image and genome data to electronic medical records. Other biology systems’ applications of machine learning include enzyme function prediction, high-throughput microarray data analysis, analysis of genome-wide association studies to better understand markers of disease, and protein function prediction. Moreover, machine learning has facilitated the development of tools such as Cell Profile, DeepVariant, and Atomwise, which are solving challenging research problems.
 
We invite all scientists working on the application of machine learning in biology to take part in this Special Issue. Original research articles, reviews, or shorter perspective articles on all aspects of the applications of machine learning are welcome.

Prof. Dr. Jacques Colinge
Dr. Rishabh Das
Guest Editors

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Keywords

  • enzyme function prediction
  • classification of cellular images
  • genome analysis
  • drug discovery
  • protein function prediction

Published Papers (3 papers)

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Research

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19 pages, 5049 KiB  
Article
PPSW–SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging
by Polat Goktas and Ricardo Simon Carbajo
Cells 2023, 12(10), 1384; https://doi.org/10.3390/cells12101384 - 13 May 2023
Cited by 3 | Viewed by 1839
Abstract
Advancements in high–throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in–depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. High–content microscopy screening often suffers from systematic noise, such as uneven illumination or [...] Read more.
Advancements in high–throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in–depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. High–content microscopy screening often suffers from systematic noise, such as uneven illumination or vignetting artifacts, which can result in false–negative findings in AI models. Traditionally, AI models have been expected to learn to deal with these artifacts, but success in an inductive framework depends on sufficient training examples. To address this challenge, we propose a two–fold approach: (1) reducing noise through an image decomposition and restoration technique called the Periodic Plus Smooth Wavelet transform (PPSW) and (2) developing an interpretable machine learning (ML) platform using tree–based Shapley Additive exPlanations (SHAP) to enhance end–user understanding. By correcting artifacts during pre–processing, we lower the inductive learning load on the AI and improve end–user acceptance through a more interpretable heuristic approach to problem solving. Using a dataset of human Mesenchymal Stem Cells (MSCs) cultured under diverse density and media environment conditions, we demonstrate supervised clustering with mean SHAP values, derived from the ‘DFT Modulus’ applied to the decomposition of bright–field images, in the trained tree–based ML model. Our innovative ML framework offers end-to-end interpretability, leading to improved precision in cell characterization during CT manufacturing. Full article
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27 pages, 1206 KiB  
Article
Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling
by José Salomón Altamirano-Flores, Luis Ángel Alvarado-Hernández, Juan Carlos Cuevas-Tello, Peter Tino, Sandra E. Guerra-Palomares and Christian A. Garcia-Sepulveda
Cells 2023, 12(5), 772; https://doi.org/10.3390/cells12050772 - 28 Feb 2023
Viewed by 1328
Abstract
Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered [...] Read more.
Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests. Full article
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Review

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28 pages, 3898 KiB  
Review
Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders
by Lealem Gedefaw, Chia-Fei Liu, Rosalina Ka Ling Ip, Hing-Fung Tse, Martin Ho Yin Yeung, Shea Ping Yip and Chien-Ling Huang
Cells 2023, 12(13), 1755; https://doi.org/10.3390/cells12131755 - 30 Jun 2023
Cited by 7 | Viewed by 4457
Abstract
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to [...] Read more.
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome. Full article
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