Leveraging machine vision in cell-based diagnostics to do more with less

Doan, Minh; Carpenter, Anne E.

doi:10.1038/s41563-019-0339-y

Cited by 49 publications

(31 citation statements)

References 27 publications

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“…Apart for the largest value of k (where the graph tends to a fully connected), in all the other cases, the average maximal degree seems able to split AF from CD patients aiming for proper classification. Right panel: Distributions of the degree standard deviations for values of k (5,10,20,30) ∈ ; notice that, since for a certain choice of k, the average degree evaluated on different realizations of G H,AF,CD is constant and equal to k, the standard deviation correponds, a constant k apart, to the variation coefficient. ∈ .…”

Section: Correlation Analysis and Dimensionality Reduction The Simplmentioning

confidence: 99%

“…In particular, systematically, AF patients share lower clustering while CD patients share higher values of clustering. Right panel: Distributions of the GR for values of k (5,10,20,30) ∈ . While the larger k the more pronounced the overlap between H and CD patients, yet classification of AF patients seems robust.…”

Section: Correlation Analysis and Dimensionality Reduction The Simplmentioning

confidence: 99%

“…As mentioned above, a particularly important application of machine learning in a healthcare context is (digital) diagnosis (see e.g. [10][11][12][13][14]. Machine learning models can detect patterns (precursor) of certain diseases within patient electronic healthcare records and inform clinicians of any anomalies.…”

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confidence: 99%

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Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

Agliari

Barra

et al. 2020

Sci Rep

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in this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologies. In the first part of the work we analyze statistically the heartbeat series associated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markers well established in the reference community. These markers are then used as inputs for a multi-layer feed-forward neural network that we train in order to make it able to classify patients. However, before training the network, preliminary operations are in order to check the effective number of markers (via principal component analysis) and to achieve data augmentation (because of the broadness of the input data). With such groundwork, we finally train the network and show that it can classify with high accuracy (at most ~85% successful identifications) patients that are healthy from those displaying atrial fibrillation or congestive heart failure. In the second part of the work, we still start from raw data and we get a classification of pathologies in terms of their related networks: patients are associated to nodes and links are drawn according to a similarity measure between the related heartbeat series. We study the emergent properties of these networks looking for features (e.g., degree, clustering, clique proliferation) able to robustly discriminate between networks built over healthy patients or over patients suffering from cardiac pathologies. We find overall very good agreement among the two paved routes. Artificial intelligence (AI) is gaining a growing role in healthcare: in the last years, several devices and advanced algorithms have been successfully employed to assist medical workers (see e.g. 1-5). Among the most important goals of this partnership between humans and machines is the wide accessibility (even in low-income and remote areas) to medical assistance and the reduction of the time needed to reach a diagnosis. Of course, in order for AI-based devices to analyze large amounts of information and make (fast and correct) decisions, they first need to undergo a suitable training 6-9. Basically, during training, a machine-learning model is exposed to examples and its internal parameters are tuned accordingly; once training is over, new data are presented to the model which then uses what it has learned to explain that data. For instance, a model meant to classify images of skin lesions as benign lesions or malignant skin cancer will be trained on a dataset of skin pictures from different patients, previously labeled as benign or malignant, through which the model learns to detect in the input image specific patterns that are hallmarks of malignancies. Clearly, the more accurate the training and the better the performance. Nowadays, an accurate training is in principle possible given that each patient generates large volumes of health data such as...

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Section: Correlation Analysis and Dimensionality Reduction The Simplmentioning

confidence: 99%

Section: Correlation Analysis and Dimensionality Reduction The Simplmentioning

confidence: 99%

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Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

Agliari

Barra

et al. 2020

Sci Rep

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“…Given the pressing need for novel antimalarials, accelerated hit identification through the use of AI as has been presented in this work would be of great interest. Artificial intelligence has revolutionized many fields of medicine including drug discovery (Wang and Shen, 2017;Doan and Carpenter, 2019;Topol, 2019). Expensive HTS, low hit rate of synthetic libraries, incompatibility of natural products with HTS, non-diverse libraries etc., are some of the reasons for limited success of many of today's drug discovery efforts (Koehn and Carter, 2005;Li and Vederas, 2009;Schneider, 2017).…”

Section: Discussionmentioning

confidence: 99%

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

et al. 2020

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Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive. While virtual in silico screening is a solution to this problem, however, the generalization of the models is not ideal. Artificial intelligence (AI), utilizing either structure-based or ligand-based approaches, has demonstrated highly accurate performances in the field of chemical property prediction. Leveraging the existing data, AI would be a suitable alternative to blind-search HTS or fingerprint-based virtual screening. The AI model would learn patterns within the data and help to search for hit compounds efficiently. In this work, we introduce DeepMalaria, a deeplearning based process capable of predicting the anti-Plasmodium falciparum inhibitory properties of compounds using their SMILES. A graph-based model is trained on 13,446 publicly available antiplasmodial hit compounds from GlaxoSmithKline (GSK) dataset that are currently being used to find novel drug candidates for malaria. We validated this model by predicting hit compounds from a macrocyclic compound library and already approved drugs that are used for repurposing. We have chosen macrocyclic compounds as these ligandbinding structures are underexplored in malaria drug discovery. The in silico pipeline for this process also consists of additional validation of an in-house independent dataset consisting mostly of natural product compounds. Transfer learning from a large dataset was leveraged to improve the performance of the deep learning model. To validate the DeepMalaria generated hits, we used a commonly used SYBR Green I fluorescence assay based phenotypic screening. DeepMalaria was able to detect all the compounds with nanomolar activity and 87.5% of the compounds with greater than 50% inhibition. Further experiments to reveal the compounds' mechanism of action have shown that not only does one of the hit compounds, DC-9237, inhibits all asexual stages of Plasmodium falciparum, but is a fast-acting compound which makes it a strong candidate for further optimization.

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“…In addition, ML allows algorithms to interpret data by learning patterns through experience [105]. Success stories of ML cover various research fields, including robotics [106], bioinformatics [107], biochemistry [108], medical diagnosis [109,110], meteorology [111] and climatology [112]. In agricultural research, ML techniques have been used for predicting regulatory and non-regulatory regions in the maize genome [113], predicting mRNA expression levels in maize [114], polyadenylation site prediction in Arabidopsis thaliana [115] and predicting macronutrient deficiencies in tomato [116].…”

Section: Applications Of Phenomics and Machine Learning For Evaluatinmentioning

confidence: 99%

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

2020

Crop Breed Genet Genom.

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Diseases are among the most important limiting factors for wheat production. Breeding for fungal diseases of wheat, primarily for rusts and Fusarium head blight (FHB), are major resource consuming activities in most breeding programs which prevent breeders from focusing entirely on improving yield. Breeding for these diseases is challenging because resistance is inherited mostly in a quantitative fashion and is greatly influenced by weather conditions. Recent advances in genomics, phenomics and big-data analysis provide opportunities for accelerating the development of low-cost and efficient selection methods for such complex traits. Genomic selection (GS) may provide opportunities for

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Leveraging machine vision in cell-based diagnostics to do more with less

Cited by 49 publications

References 27 publications

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

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