Dataset Information

Image-based crystal detection: a machine-learning approach.

ABSTRACT: The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing crystallization-analysis pipelines, whereby specialists examine images as they normally would with the exception that the images appear in rank order according to a simple real-valued score. Promising results are shown for 319 112 images associated with 150 structures solved by the Joint Center for Structural Genomics pipeline during the 2006-2007 year. Overall, the algorithm achieves a mean receiver operating characteristic score of 0.919 and a 78% reduction in human effort per set when considering an absolute score cutoff for screening images, while incurring a loss of five out of 150 structures.

SUBMITTER: Liu R

PROVIDER: S-EPMC2585161 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Image-based crystal detection: a machine-learning approach.

Liu Roy R Freund Yoav Y Spraggon Glen G

Acta crystallographica. Section D, Biological crystallography 20081118 Pt 12

The ability of computers to learn from and annotate large databases of crystallization-trial images provides not only the ability to reduce the workload of crystallization studies, but also an opportunity to annotate crystallization trials as part of a framework for improving screening methods. Here, a system is presented that scores sets of images based on the likelihood of containing crystalline material as perceived by a machine-learning algorithm. The system can be incorporated into existing ...[more]

PMID: 19018095

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Project description:The differentiation of induced pluripotent stem cells (iPSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variability, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, iPSC-to-cardiomyocyte (CM) differentiation is vulnerable with great sensitivity to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), iPSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial iPSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor to further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize iPSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. With the assistance of RNA sequencing, we transcriptomically characterize the image-recognized CPCs (IR-CPCs), the cells treated with different CHIR doses at stage I, iPSC-CM treated with or without BI-1347, and cells treated with or without BI-1347 at stage I.

Project description:BackgroundIn recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns.ObjectiveThis study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.MethodsReddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data.ResultsClassification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments.ConclusionsOpioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

Dataset Information

Image-based crystal detection: a machine-learning approach.

Publications

Image-based crystal detection: a machine-learning approach.

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