Microbiome Sample Comparison and Search: From Pair-Wise Calculations to Model-Based Matching

Genomics, Proteomics &Amp; Bioinformatics

Yang

et al. 2022

Self Cite

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Section: The Dilemma Of Traditional Methods Could Be Solved By Deep L...mentioning

confidence: 99%

Microbial Dark Matter: From Discovery to Applications

Zha

Genomics, Proteomics &Amp; Bioinformatics

Yang

et al. 2022

Self Cite

“…stages I, II, III and IV), and the purpose of classification in this context is to assign gut community samples to the correct stages of CRC [ 12 ]. Generally, the complexity of microbial community classification is positively correlated with the number of classes and negatively correlated with the number of community samples [ 13 ]. For a given context that involved multi-disease classes but limited number of samples, such as classification of 4026 samples from 28 case–control microbial studies spanning 10 diseases [ 14 ], low prediction accuracies are naturally expected for identification of disease-specific patterns, rending microbiome-based classification unpractical.…”

Section: Introductionmentioning

confidence: 99%

“…Faced with these contexts, current methods for microbial community classification have limitations in dealing with such paramount of complex relationships and biome-specific patterns. While it becomes extremely difficult when there exists biomes in which there are only a few samples, a ‘Big Data, Small Sample’ problem [ 13 ]. Random forest model is suitable for classification among numerus samples, and it has been used in many applications, such as chronological age prediction [ 15 ] and fecal source identification [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

EXPERT: transfer learning-enabled context-aware microbial community classification

Briefings in Bioinformatics

Zha

et al. 2022

Self Cite

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

Tracing human life trajectory using gut microbial communities by context-aware deep learning

Zhang

Briefings in Bioinformatics

et al. 2023

The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-related factors remains unclear. The lack of context-awareness, insufficient data, and the existence of batch effect are the three major issues, making the life trajection of the host based on gut microbial communities problematic. Here, we used a novel computational approach (microDELTA, microbial-based deep life trajectory) to track longitudinal human gut microbial communities’ alterations, which employs transfer learning for context-aware mining of gut microbial community dynamics at different life stages. Using an infant cohort, we demonstrated that microDELTA outperformed Neural Network for accurately predicting the age of infant with different delivery mode, especially for newborn infants of vaginal delivery with the area under the receiver operating characteristic curve of microDELTA and Neural Network at 0.811 and 0.436, respectively. In this context, we have discovered the influence of delivery mode on infant gut microbial communities. Along the human lifespan, we also applied microDELTA to a Chinese traveler cohort, a Hadza hunter-gatherer cohort and an elderly cohort. Results revealed the association between long-term dietary shifts during travel and adult gut microbial communities, the seasonal cycling of gut microbial communities for the Hadza hunter-gatherers, and the distinctive microbial pattern of elderly gut microbial communities. In summary, microDELTA can largely solve the issues in tracing the life trajectory of the human microbial communities and generate accurate and flexible models for a broad spectrum of microbial-based longitudinal researches.