Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Perspective on Using Machine Learning in 3D Bioprinting.

ABSTRACT: Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.

SUBMITTER: Yu C

PROVIDER: S-EPMC7415853 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A Perspective on Using Machine Learning in 3D Bioprinting.

Yu Chunling C Jiang Jingchao J

International journal of bioprinting 20200124 1

Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning ...[more]

PMID: 32782987

Similar Datasets

Integration of 3D bioprinting and multi-algorithm machine learning identified glioma susceptibilities and microenvironment characteristics.

Project description:Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.

| S-EPMC11003988 | biostudies-literature

3D bioprinting processes: A perspective on classification and terminology.

Project description:This article aims to provide further classification of cell-compatible bioprinting processes and examine the concept of 3D bioprinting within the general technology field of 3D printing. These technologies are categorized into four distinct process categories, namely material jetting, vat photopolymerization, material extrusion and free-form spatial printing. Discussion will be presented on the definition of classification with example of techniques grouped under the same category. The objective of this article is to establish a basic framework for standardization of process terminology in order to accelerate the implementation of bioprinting technologies in research and commercial landscape.

| S-EPMC7582007 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Modelling glioma invasion using 3D bioprinting and scaffold-free 3D culture.

Project description:Glioma is a highly aggressive form of brain cancer, with some subtypes having 5-year survival rates of less than 5%. Tumour cell invasion into the surrounding parenchyma seems to be the primary driver of these poor outcomes, as most gliomas recur within 2 cm of the original surgically-resected tumour. Many current approaches to the development of anticancer therapy attempt to target genetic weaknesses in a particular cancer, but may not take into account the microenvironment experienced by a tumour and the patient-specific genetic differences in susceptibility to treatment. Here we demonstrate the use of complementary approaches, 3D bioprinting and scaffold-free 3D tissue culture, to examine the invasion of glioma cells into neural-like tissue with 3D confocal microscopy. We found that, while both approaches were successful, the use of 3D tissue culture for organoid development offers the advantage of broad accessibility. As a proof-of-concept of our approach, we developed a system in which we could model the invasion of human glioma cells into mouse neural progenitor cell-derived spheroids. We show that we can follow invasion of human tumour cells using cell-tracking dyes and 3D laser scanning confocal microscopy, both in real time and in fixed samples. We validated these results using conventional cryosectioning. Our scaffold-free 3D approach has broad applicability, as we were easily able to examine invasion using different neural progenitor cell lines, thus mimicking differences that might be observed in patient brain tissue. These results, once applied to iPSC-derived cerebral organoids that incorporate the somatic genetic variability of patients, offer the promise of truly personalized treatments for brain cancer.

| S-EPMC6235776 | biostudies-literature

3D Bioprinting using UNIversal Orthogonal Network (UNION) Bioinks.

Project description:Three-dimensional (3D) bioprinting is a promising technology to produce tissue-like structures, but a lack of diversity in bioinks is a major limitation. Ideally each cell type would be printed in its own customizable bioink. To fulfill this need for a universally applicable bioink strategy, we developed a versatile, bioorthogonal bioink crosslinking mechanism that is cell compatible and works with a range of polymers. We term this family of materials UNIversal, Orthogonal Network (UNION) bioinks. As demonstration of UNION bioink versatility, gelatin, hyaluronic acid (HA), recombinant elastin-like protein (ELP), and polyethylene glycol (PEG) were each used as backbone polymers to create inks with storage moduli spanning 200 to 10,000 Pa. Because UNION bioinks are crosslinked by a common chemistry, multiple materials can be printed together to form a unified, cohesive structure. This approach is compatible with any support bath that enables diffusion of UNION crosslinkers. Both matrix-adherent human corneal mesenchymal stromal cells and non-matrix-adherent human induced pluripotent stem cell-derived neural progenitor spheroids were printed with UNION bioinks. The cells retained high viability and expressed characteristic phenotypic markers after printing. Thus, UNION bioinks are a versatile strategy to expand the toolkit of customizable materials available for 3D bioprinting.

| S-EPMC7888563 | biostudies-literature

Identifying transcription factor-DNA interactions using machine learning

Project description:We focused on building models that incorporated transcription factor (TF)-DNA interaction data for 12 members of the Auxin Response Factor (ARF) family from soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq).

2023-06-01 | GSE193400 | GEO

Quantifying Biopolymer Sequence Recognition using Biophysically Informed Machine Learning

Project description:Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene expression regulation and signal transduction. Given their importance, next-generation sequencing (NGS) based assays that characterize such recognition with high-throughput are increasingly being used to profile a range of protein classes and interactions. However, these methods do not measure the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to quantify sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from \textit{in vivo} data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it quantifies the absolute affinity of protein-ligand interactions. Its applicability extends beyond thermodynamic equilibrium binding, to the kinetics of kinase-substrate interactions. Altogether, ProBound provides a versatile algorithmic framework for understanding sequence recognition in a wide variety of biological contexts.

2021-06-02 | GSE175942 | GEO

Optimization of a 3D bioprinting process using ultrashort peptide bioinks.

Project description:The field of three-dimensional (3D) bioprinting is rapidly emerging as an additive manufacturing method for tissue and organ fabrication. The demand for tissues and organ transplants is ever increasing, although donors are not as readily available. Consequently, tissue engineering is gaining much attention to alleviate this problem. The process of achieving well-structured 3D bioprinted constructs using hydrogel bioinks depends on symmetrical precision, regulated flow rates, and viability of cells. Even with the mentioned parameters optimized, the printed structures need additional refining by removing excessive liquids, as peptide hydrogel bioprints encapsulate water. However, it is challenging to eliminate the confined fluids without compromising the printing process. In this paper, we introduced a vacuum system to our 3D bioprinting robotic arm and thus optimized the printing quality for complex and refined 3D scaffolds. Moreover, the proposed vacuum system supports printing with cells. Our results show improved printing resolution which facilitates the printing of higher and more stable structures.

| S-EPMC7415865 | biostudies-literature

Expanding and optimizing 3D bioprinting capabilities using complementary network bioinks.

Project description:A major challenge in three-dimensional (3D) bioprinting is the limited number of bioinks that fulfill the physicochemical requirements of printing while also providing a desirable environment for encapsulated cells. Here, we address this limitation by temporarily stabilizing bioinks with a complementary thermo-reversible gelatin network. This strategy enables the effective printing of biomaterials that would typically not meet printing requirements, with instrument parameters and structural output largely independent of the base biomaterial. This approach is demonstrated across a library of photocrosslinkable bioinks derived from natural and synthetic polymers, including gelatin, hyaluronic acid, chondroitin sulfate, dextran, alginate, chitosan, heparin, and poly(ethylene glycol). A range of complex and heterogeneous structures are printed, including soft hydrogel constructs supporting the 3D culture of astrocytes. This highly generalizable methodology expands the palette of available bioinks, allowing the biofabrication of constructs optimized to meet the biological requirements of cell culture and tissue engineering.

| S-EPMC7500929 | biostudies-literature

COVID and nutrition: A machine learning perspective.

Project description:A self-report questionnaire survey was conducted online to collect big data from over 16000 Iranian families (who were the residents of 1000 urban and rural areas of Iran). The resulting data storage contained over 1 M records of data and over 1G records of automatically inferred information. Based on this data storage, a series of machine learning experiments was conducted to investigate the relationship between nutrition and the risk of contracting COVID-19. With highly accurate scores, the findings strongly suggest that foods and water sources containing certain natural bioactive and phytochemical agents may help to reduce the risk of apparent COVID-19 infection.

| S-EPMC8767975 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data