Next-Generation Machine Learning for Biological Networks.

By Diogo M. Camacho, K. M. Collins, Rani K. Powers, James C. Costello, James J. Collins. Cell, 2018

833 citations recorded by the source corpus.

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/d2c12b2e57d1262a3c8beb119bbadf596d7a116a

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Citations in the corpus

Artificial intelligence in cancer target identification and drug discovery (2022, background): “The ﬁ rst challenge is the lack of consistent data for validation 33 .”
A deep learning approach to antibiotic discovery (2020, background): “Historically, antibiotics were discovered largely through screening soil-dwelling microbes for secondary metabolites that prevented the growth of pathogenic bacteria (Clardy et al., 2006; Wright, 2017).”
Discovery of a structural class of antibiotics with explainable deep learning (2023, methodology): “This approach relies on Chemprop, a platform for graph neural networks 10,11 , which are typically black box models 19 , or models that are not readily interpreted or explained.”
Ensemble deep learning in bioinformatics (2020, background): “At the forefront of machine learning, ensemble learning and deep learning have independently made a substantial impact on the field of bioinformatics through their widespread applications, from basic nucleotide and protein sequence analysis to systems biology 2,3 .”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “…need of any processing of filtering steps, hence providing opportunity for significant improvements in precision oncology in terms of both treatment response prediction accuracy as well as resources and time required for data processing (Camacho et al. 2018; Chang et al. 2018; Ding et al. 2018).”
Systems Biology of Cancer Metastasis (2019, background): “Deep learning and machine learning: Artificial intelligence has found many applications in network analysis, compound property and prediction of drug activity (Camacho et al., 2018; Chen et al., 2018)”
Deep learning in drug discovery: an integrative review and future challenges (2022, background): “Furthermore, Neural networks that can do multiple tasks are ideal for simultaneously learning the properties of many sorts of data (Camacho et al. 2018).”
A Deep Learning Approach to Antibiotic Discovery. (2020, neutral): “Given recent advancements in machine learning (Camacho et al., 2018), the field is now ripe for the application of algorithmic solutions for molecular property prediction to identify novel structural classes of antibiotics.”
How Machine Learning will Transform Biomedicine (2020, neutral): “A recent review provides a brief tutorial on machine learning approaches in the life sciences (Camacho et al., 2018).”
A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, positive): “Therefore, there is a need to develop biological discovery approaches that integrate biochemical screens with network modeling and advanced data analysis techniques to enhance our understanding of complex drug mechanisms (Camacho et al., 2018; Wainberg et al., 2018; Xie et al., 2017).”
The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets (2020, background): “They enable the inference of molecular functions through the ‘guilt-by-association’ principle (4,5), allow the characterization of modularity in biological processes (6–8) and serve as substrates for deep learning (9,10).”
AlphaFold, Artificial Intelligence (AI), and Allostery (2022, background): “204 They impact disease biology, drug discovery, microbiome research, and synthetic biology.”
Graph Representation Learning in Biomedicine and Healthcare (2021, neutral): “Some aspects of graph representation learning have been covered extensively in the literature: deep learning on structured data 17,18 ; graph neural networks 19–21 (GNNs); representation learning for homogeneous and heterogeneous graphs 22–24 , solely heterogeneous graphs 25 and dynamic graphs 26 ;…”