scholarly journals Quantification of Osteoclasts in Culture, Powered by Machine Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Edo Cohen-Karlik ◽  
Zamzam Awida ◽  
Ayelet Bergman ◽  
Shahar Eshed ◽  
Omer Nestor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Automatic Process ◽  
Type I ◽  
Bone Biology ◽  
Manual Task ◽  
Multinucleated Cells ◽  
Osteoclast Number

In vitro osteoclastogenesis is a central assay in bone biology to study the effect of genetic and pharmacologic cues on the differentiation of bone resorbing osteoclasts. To date, identification of TRAP+ multinucleated cells and measurements of osteoclast number and surface rely on a manual tracing requiring specially trained lab personnel. This task is tedious, time-consuming, and prone to operator bias. Here, we propose to replace this laborious manual task with a completely automatic process using algorithms developed for computer vision. To this end, we manually annotated full cultures by contouring each cell, and trained a machine learning algorithm to detect and classify cells into preosteoclast (TRAP+ cells with 1–2 nuclei), osteoclast type I (cells with more than 3 nuclei and less than 15 nuclei), and osteoclast type II (cells with more than 15 nuclei). The training usually requires thousands of annotated samples and we developed an approach to minimize this requirement. Our novel strategy was to train the algorithm by working at “patch-level” instead of on the full culture, thus amplifying by >20-fold the number of patches to train on. To assess the accuracy of our algorithm, we asked whether our model measures osteoclast number and area at least as well as any two trained human annotators. The results indicated that for osteoclast type I cells, our new model achieves a Pearson correlation (r) of 0.916 to 0.951 with human annotators in the estimation of osteoclast number, and 0.773 to 0.879 for estimating the osteoclast area. Because the correlation between 3 different trained annotators ranged between 0.948 and 0.958 for the cell count and between 0.915 and 0.936 for the area, we can conclude that our trained model is in good agreement with trained lab personnel, with a correlation that is similar to inter-annotator correlation. Automation of osteoclast culture quantification is a useful labor-saving and unbiased technique, and we suggest that a similar machine-learning approach may prove beneficial for other morphometrical analyses.

In vitro molecular machine learning algorithm via symmetric internal loops of DNA

Biosystems ◽  
2017 ◽  
Vol 158 ◽  
pp. 1-9 ◽  
Author(s):  
Ji-Hoon Lee ◽  
Seung Hwan Lee ◽  
Christina Baek ◽  
Hyosun Chun ◽  
Je-hwan Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Molecular Machine ◽  
Machine Learning Algorithm ◽  
Internal Loops

scholarly journals Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

2021 ◽  
Author(s):  
Li Chen ◽  
Wen Li ◽  
Yuxiu Liu ◽  
Zhihang Peng ◽  
Liyi Cai ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Learning Algorithm ◽  
Embryo Selection ◽  
Human Embryos ◽  
Utilization Rate ◽  
Grading System ◽  
Vitro Fertilization ◽  
Embryo Grading

Abstract BackgroundThe success rates of in vitro fertilization (IVF) treatment are limited by the aneuploidy of human embryos. Pre-implantation genetic testing for aneuploidy(PGT-A) is often used to select embryos with normal ploidy but requires invasive embryo biopsy. MethodsWe performed chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples and developed a noninvasive embryo grading system based on the random forest machine-learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was performed to investigate clinical outcomes between machine learning-guided and traditional niPGT-A analyses. We graded embryos as A, B, or C using machine learning-guided niPGT-A analysis according to their euploidy probability levels predicted by noninvasive chromosomal screening. ResultsWe observed higher live birth rate in A- versus C-grade embryos (50.4% versus 27.1%, p=0.006) and B- versus C-grade embryos (45.3% versus 27.1%, p=0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, p=0.026) and B- versus C-grade embryos (14.3% versus 33.3%, p=0.021). The embryo utilization rate was significantly higher through machine learning strategy compared to the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, p<0.001). We observed better outcomes in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through machine learning strategies than traditional niPGT-A analysis. ConclusionThese results demonstrate that the machine learning-guided embryo grading system can optimize embryo selection and avoid wasting potential embryos.Trial registrationChinese Clinical Trial Registry,ChiCTR-RRC-17010396.Registered 11 January 2017, http://www.chictr.org.cn/ChiCTR-RRC-17010396

scholarly journals Machine Learning Algorithm for DNA-Based Molecular Ligands

2019 ◽  
Author(s):  
Christina Baek ◽  
Sang-Woo Lee ◽  
Beom-Jin Lee ◽  
Dong-Hyun Kwak
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Learning Algorithm ◽  
Dna Nanotechnology ◽  
Search Space ◽  
Molecular Computing ◽  
Training Data ◽  
Molecular Systems ◽  
Novel Approach

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

scholarly journals Machine Learning Techniques to Predict Daily Rainfall Amount

2021 ◽  
Author(s):  
Chalachew Muluken Liyew ◽  
Haileyesus Amsaya Melese
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Correlation Technique ◽  
Learning Techniques ◽  
Machine Learning Model

Abstract It is crucial to predict the amount of daily rainfall to improve agricultural productivities to secure food, and water quality supply to keep the citizen healthy. To predict rainfall, various researches are conducted using data mining and machine learning techniques of different countries’ environmental datasets. The Pearson correlation technique is used to select relevant environmental variables which are used as an input for the machine learning model of this study. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The dataset is collected from the local meteorological office to measure the performance of three machine learning techniques as Multivariate Linear Regression, Random Forest and Extreme Gradient Boost. Root mean squared error and Mean absolute Error are used to measure the performance of the machine learning model for this study. The result of the study shows that the Extreme Gradient Boost gradient descent machine learning algorithm performs better than others.

scholarly journals Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Thuy-Anh Nguyen ◽  
Hai-Bang Ly ◽  
Hai-Van Thi Mai ◽  
Van Quan Tran
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Compressive Strength ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Feedforward Neural Network ◽  
Machine Learning Algorithms ◽  
Concrete Compressive Strength ◽  
Squared Error

Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.

A MACHINE LEARNING ALGORITHM CAN OPTIMIZE THE DAY OF TRIGGER TO IMPROVE IN VITRO FERTILIZATION OUTCOMES

2021 ◽  
Vol 116 (3) ◽  
pp. e169
Author(s):  
Eduardo Hariton ◽  
Ethan A. Chi ◽  
Gordon Chi ◽  
Jerrine R. Morris ◽  
Jon F. Braatz ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Vitro Fertilization

Collagen fibrillogenesis in vitro: interaction of types I and V collagen

1986 ◽  
Vol 44 ◽  
pp. 210-211
Author(s):  
Arthur J. Wasserman ◽  
Kathy C. Kloos ◽  
David E. Birk
Keyword(s):  
Type I Collagen ◽  
Corneal Stroma ◽  
Minor Component ◽  
Homogeneous Distribution ◽  
Type I ◽  
Type V Collagen ◽  
Fibril Morphology ◽  
Type V ◽  
In Vitro Interaction

Type I collagen is the predominant collagen in the cornea with type V collagen being a quantitatively minor component. However, the content of type V collagen (10-20%) in the cornea is high when compared to other tissues containing predominantly type I collagen. The corneal stroma has a homogeneous distribution of these two collagens, however, immunochemical localization of type V collagen requires the disruption of type I collagen structure. This indicates that these collagens may be arranged as heterpolymeric fibrils. This arrangement may be responsible for the control of fibril diameter necessary for corneal transparency. The purpose of this work is to study the in vitro assembly of collagen type V and to determine whether the interactions of these collagens influence fibril morphology.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery
Sign in / Sign up

Export Citation Format

Share Document