D. Michael Ando
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View article: Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action
Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action Open
View article: A deep learning and digital archaeology approach for mosquito repellent discovery
A deep learning and digital archaeology approach for mosquito repellent discovery Open
Insect-borne diseases kill > 0.5 million people annually. Currently available repellents for personal or household protection are limited in their efficacy, applicability, and safety profile. Here, we describe a machine-learning-driven …
View article: Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action
Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action Open
Treatments for tuberculosis remain lengthy, motivating a search for new drugs with novel mechanisms of action. However, it remains challenging to elucidate the direct targets of a drug, and even more so, to determine which disrupted cellul…
View article: Digital staining in optical microscopy using deep learning - a review
Digital staining in optical microscopy using deep learning - a review Open
Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-stand…
View article: JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations
JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations Open
Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here…
View article: Digital staining in optical microscopy using deep learning -- a review
Digital staining in optical microscopy using deep learning -- a review Open
Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-stand…
View article: A deep learning and digital archaeology approach for mosquito repellent discovery
A deep learning and digital archaeology approach for mosquito repellent discovery Open
Insect-borne diseases kill >0.5 million people annually. Currently available repellents for personal or household protection are limited in their efficacy, applicability, and safety profile. Here, we describe a machine-learning-driven high…
View article: Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks
Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks Open
Microscopic examination of blood smears remains the gold standard for diagnosis and laboratory studies with malaria. Inspection of smears is, however, a tedious manual process dependent on trained microscopists with results varying in accu…
View article: Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks
Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks Open
Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We …
View article: A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens Open
Machine learning is applied to high-throughput microscopy images of malaria parasites to define antimalarial drug mode of action.
View article: Batch equalization with a generative adversarial network
Batch equalization with a generative adversarial network Open
Motivation Advances in automation and imaging have made it possible to capture a large image dataset that spans multiple experimental batches of data. However, accurate biological comparison across the batches is challenged by batch-to-bat…
View article: Morphological profiling of tubercle bacilli identifies drug pathways of action
Morphological profiling of tubercle bacilli identifies drug pathways of action Open
Significance Tuberculosis is a leading cause of death in the world and requires treatment with an arduous multidrug regimen. Many new tuberculosis drugs are in development, and the drug development pipeline would benefit from more rapid me…
View article: Physics-enhanced machine learning for virtual fluorescence microscopy
Physics-enhanced machine learning for virtual fluorescence microscopy Open
This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy. Our results show that by including a model of illumination within the first layers of a deep convolutional neural network, it is possi…
View article: Morphological profiling of tubercule bacilli identifies drug pathways of action
Morphological profiling of tubercule bacilli identifies drug pathways of action Open
Morphological profiling is a method to classify target pathways of antibacterials based on how bacteria respond to treatment through changes to cellular shape and spatial organization. Here, we utilized the cell-to-cell variation in morpho…
View article: Batch Equalization with a Generative Adversarial Network
Batch Equalization with a Generative Adversarial Network Open
Advances in automation and imaging have made it possible to capture large image datasets for experiments that span multiple weeks with multiple experimental batches of data. However, accurate biological comparisons across the batches is ch…
View article: A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens Open
Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current …
View article: Applying Deep Neural Network Analysis to High-Content Image-Based Assays
Applying Deep Neural Network Analysis to High-Content Image-Based Assays Open
View article: In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images Open
View article: Assessing microscope image focus quality with deep learning
Assessing microscope image focus quality with deep learning Open
Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetical…
View article: Assessing microscope image focus quality with deep learning
Assessing microscope image focus quality with deep learning Open
Background
\n Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image anal…
View article: Improving Phenotypic Measurements in High-Content Imaging Screens
Improving Phenotypic Measurements in High-Content Imaging Screens Open
Image-based screening is a powerful technique to reveal how chemical, genetic, and environmental perturbations affect cellular state. Its potential is restricted by the current analysis algorithms that target a small number of cellular phe…
View article: Nrf2 mitigates LRRK2- and α-synuclein–induced neurodegeneration by modulating proteostasis
Nrf2 mitigates LRRK2- and α-synuclein–induced neurodegeneration by modulating proteostasis Open
Significance The prevailing view of nuclear factor erythroid 2-related factor (Nrf2) function in the central nervous system is that it acts by a cell-nonautonomous mechanism to activate a program of gene expression that mitigates reactive …
View article: A Three-Groups Model for High-Throughput Survival Screens
A Three-Groups Model for High-Throughput Survival Screens Open
Summary Amyotrophic lateral sclerosis (ALS) is a neurodegenerative condition characterized by the progressive deterioration of motor neurons in the cortex and spinal cord. Using an automated robotic microscope platform that enables the lon…
View article: Automated, High-throughput Analysis of Neurite Dynamics in Neurodegenerative Disease
Automated, High-throughput Analysis of Neurite Dynamics in Neurodegenerative Disease Open
Despite decades of research, there are no effective therapies for neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Parkinson's Disease (PD). These diseases are marked by a progressive loss of the neuronal processe…