A versatile information retrieval framework for evaluating profile strength and similarity.

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise.

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations.

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset.

Reproducible image-based profiling with Pycytominer.

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as "image-based profiling".

Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis.

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts.

SOCRAT: a Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization.

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific.

3D convolutional neural networks for stalled brain capillary detection.

Adequate blood supply is critical for normal brain function. Brain vasculature dysfunctions, including stalled blood flow in cerebral capillaries, are associated with cognitive decline and pathogenesis in Alzheimer's disease. Recent advances in imaging technology enabled generation of high-quality 3D images that can be used to visualize stalled blood vessels.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells.

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape.

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.

Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants.

Publisher Correction: 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Hypothesis: Caco-2 cell rotational 3D mechanogenomic turing patterns have clinical implications to colon crypts.

Colon crypts are recognized as a mechanical and biochemical Turing patterning model. Colon epithelial Caco-2 cell monolayer demonstrated 2D Turing patterns via force analysis of apical tight junction live cell imaging which illuminated actomyosin meshwork linking the actomyosin network of individual cells. Actomyosin forces act in a mechanobiological manner that alters cell/nucleus/tissue morphology.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements.

Deep learning in pharmacogenomics: from gene regulation to patient stratification.

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications.

The modern web is a successful platform for large scale interactive web applications, including visualizations. However, there are no established design principles for building complex visual analytics (VA) web applications that could efficiently integrate visualizations with data management, computational transformation, hypothesis testing, and knowledge discovery. This imposes a time-consuming design and development process on many researchers and developers.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood.

: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies.

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information.

Introduction: Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data exploration, discovery and analytics get considerably harder. Exploratory querying of heterogeneous and multi-source information is both difficult and necessary to advance our knowledge about the world around us.

Calmodulin 4 is dispensable for epidermal barrier formation and wound healing in mice.

Calcium-mediated signals play important roles in epidermal barrier formation, skin homoeostasis and wound repair. Calmodulin 4 (Calm4) is a small, Ca2+ -binding protein with strong expression in suprabasal keratinocytes. In mice, Calm4 first appears in the skin at the time of barrier formation, and its expression increases in response to epidermal barrier challenges.

Role of Scarf and its binding target proteins in epidermal calcium homeostasis.

The novel Ca2+-binding protein, Scarf (skin calmodulin-related factor) belongs to the calmodulin-like protein family and is expressed in the differentiated layers of the epidermis. To determine the roles of Scarf during stratification, we set out to identify the binding target proteins by affinity chromatography and subsequent analysis by mass spectrometry. Several binding factors, including 14-3-3s, annexins, calreticulin, ERp72 (endoplasmic reticulum protein 72), and nucleolin, were identified, and their interactions with Scarf were corroborated by co-immunoprecipitation and co-localization analyses.

Identification and specificity profiling of protein prenyltransferase inhibitors using new fluorescent phosphoisoprenoids.

Posttranslational modification of proteins with farnesyl and geranylgeranyl isoprenoids is a widespread phenomenon in eukaryotic organisms. Isoprenylation is conferred by three protein prenyltransferases: farnesyl transferase (FTase), geranylgeranyl transferase type-I (GGTase-I), and Rab geranylgeranyltransferase (RabGGTase). Inhibitors of these enzymes have emerged as promising therapeutic compounds for treatment of cancer, viral and parasite originated diseases, as well as osteoporosis.

Characterization of human epiplakin: RNAi-mediated epiplakin depletion leads to the disruption of keratin and vimentin IF networks.

Epiplakin is a member of the plakin family with multiple copies of the plakin repeat domain (PRD). We studied the subcellular distribution and interactions of human epiplakin by immunostaining, overlay assays and RNAi knockdown. Epiplakin decorated the keratin intermediate filaments (IF) network and partially that of vimentin.

Breaking the connection: caspase 6 disconnects intermediate filament-binding domain of periplakin from its actin-binding N-terminal region.

Periplakin is a member of the plakin family of cytolinkers that connect cytoskeletal networks to each other as well as to the cell junctional complexes. Here, we demonstrate a direct molecular interaction between actin and periplakin. Furthermore, the oligomerization state of periplakin was shown to determine specificity of its binding to intermediate filaments (IF) in vitro.

In vitro assembly, purification, and crystallization of the rab geranylgeranyl transferase:substrate complex.

Posttranslational modification with the geranygeranyl moiety is essential for the ability of Rab GTPases to control processes of membrane docking and fusion. This modification is conferred by Rab geranylgeranyltransferase (RabGGTase), which catalyzes the transfer of two 20-carbon geranylgeranyl groups from geranylgeranyl pyrophosphate onto C-terminal cysteine residues of Rab proteins. The enzyme consists of a catalytic alpha/beta heterodimer and an accessory protein termed Rab escort protein (REP-1) that delivers the newly prenylated Rab proteins to their target membrane.