Gut microbiome, big data and machine learning to promote precision medicine for cancer

Cammarota, Giovanni; Ianiro, Gianluca; Ahern, Anna M.; Carbone, Carmine; Temko, Andriy; Claesson, Marcus J.; Gasbarrini, Antonio; Tortora, Giampaolo

doi:10.1038/s41575-020-0327-3

Cited by 221 publications

(133 citation statements)

References 208 publications

Supporting

Mentioning

133

Contrasting

Order By: Relevance

“…ML and ML-methods used to address clinical problems in autoimmune disease have been emerging, where personalized care and personalized medicine will begin to shape the future for patients suffering from such diseases. Additionally, patients with multiple autoimmune comorbidities will also benefit from this paradigm of personalized healthcare approaches, which chemometrics, machine learning, or artificial intelligence will realize this goal, and many healthcare systems are already beginning to invest in heavily [252]. The use of biochemical sensors, instruments to measure changes in samples, biochemical signature analysis, and then applying chemometrics is routinely used in the environment, food, and the health sciences [253].…”

Section: Chemometrics and Machine Learning (Ml)mentioning

confidence: 99%

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Gangadoo

Rajapaksha

Cheeseman

et al. 2021

IJMS

View full text Add to dashboard Cite

Gastrointestinal (GIT) diseases have risen globally in recent years, and early detection of the host’s gut microbiota, typically through fecal material, has become a crucial component for rapid diagnosis of such diseases. Human fecal material is a complex substance composed of undigested macromolecules and particles, and the processing of such matter is a challenge due to the unstable nature of its products and the complexity of the matrix. The identification of these products can be used as an indication for present and future diseases; however, many researchers focus on one variable or marker looking for specific biomarkers of disease. Therefore, the combination of genomics, transcriptomics, proteomics and metabonomics can give a detailed and complete insight into the gut environment. The proper sample collection, sample preparation and accurate analytical methods play a crucial role in generating precise microbial data and hypotheses in gut microbiome research, as well as multivariate data analysis in determining the gut microbiome functionality in regard to diseases. This review summarizes fecal sample protocols involved in profiling coeliac disease.

show abstract

Section: Chemometrics and Machine Learning (Ml)mentioning

confidence: 99%

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Gangadoo

Rajapaksha

Cheeseman

et al. 2021

IJMS

View full text Add to dashboard Cite

show abstract

“…Nowadays, application of deep learning such as deep neural network (DNN) or convolutional neural network (CNN) has been shifted from computer vision problems to microbial biological field [17] . By parallel-computing-based hardware-level boost of multi-core CPU and many-core GPU, deep learning approach shows its advantages in big data integration and robustness to data heterogeneous [84] , while the particular parameters in model construction still need to be optimized for solving different questions.…”

Section: Data Mining For Status Identification and Classificationmentioning

confidence: 99%

“…Newly developed bioinformatics tools are bringing opportunities in deciphering the microbiome data, from general-purpose algorithms such as sequence alignment and machine learning (ML), to microbiome-specific approaches like operational taxonomy unit (OTU) picking [12] and phylogeny-based distance metrics [13] , [14] . On the other hand, challenges have also already been placed by the vast volume of microbiome data, especially in integration of datasets produced by multiple studies and platforms [15] , comparison among samples [16] and status or disease classification and prediction by training on large-scale datasets [17] , [18] .…”

Section: Introductionmentioning

confidence: 99%

Method development for cross-study microbiome data mining: Challenges and opportunities

Jing

Zhang

et al. 2020

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the “microbiome data space”.

show abstract

“…Machine Learning (ML) can perform modeling and analysis on big data [17] to assist humans in various areas of technology such as language detection and translation [18], facial expression and motion analysis [19], medicine [20], etc. In recent years, ML has gained increasing attention in the AM field due to the application of regression, classification, and clustering.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

Zhang¹,

Femi-Oyetoro²,

Fidan³

et al. 2021

Preprint

View full text Add to dashboard Cite

Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

show abstract

Gut microbiome, big data and machine learning to promote precision medicine for cancer

Cited by 221 publications

References 208 publications

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

Method development for cross-study microbiome data mining: Challenges and opportunities

Prediction of Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

Contact Info

Product

Resources

About