Evaluation of semantic dependencies in a conceptual co-occurrence network of a medical vocabulary

The amount of medical knowledge is constantly growing thus providing new hope for people having health-relatedproblems. However a challenge is to develop flexible methods to facilitate managing and interpretinglarge medical knowledge entities. There is a need to enhance health literacy by developing personalized healthsupport tools. Furthermore there is a need to assist decision-making with decision support tools. The recent andon-going changes in everyday life both on technological and societal levels (for example adoption of smartphones and personal mobile medical tracking devices, social networking, open source and open data initiatives,fast growth of accumulated medical data, need for new self-care solutions for aging European population)motivate to invest in the development of new computerized personalized methods for knowledge management ofmedical data for diagnosis and treatment. To enable creation of new adaptive personalized health support toolswe have carried out an evaluation of semantic dependencies in a conceptual co-occurrence network covering aset of concepts of a medical vocabulary with experimental results ranging up to 2994 unique nouns, 82814unique conceptual links and 200000 traversed link steps.Peer reviewe

Enabling personalized healthcare by analyzing semantic dependencies in a conceptual co-occurrence network based on a medical vocabulary

The amount of medical knowledge is constantly growing thus providing new hope for people having health-related problems. However a challenge is to develop flexible methods to facilitate managing and interpreting large medical knowledge entities. There is a need to enhance health literacy by developing personalized health support tools. Furthermore there is a need to assist decision-making with decision support tools. The recent and on-going changes in everyday life both on technological and societal levels (for example adoption of smart phones and personal mobile medical tracking devices, social networking, open source and open data initiatives, fast growth of accumulated medical data, need for new self-care solutions for aging European population) motivate to invest in the development of new computerized personalized methods for knowledge management of medical data for diagnosis and treatment. To enable creation of new adaptive personalized health support tools we have carried out an evaluation of semantic dependencies in a conceptual co-occurrence network covering a set of concepts of a medical vocabulary with experimental results ranging up to 2994 unique nouns, 82814 unique conceptual links and 200000 traversed link steps.Peer reviewe

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences

Image informatics approaches to advance cancer drug discovery

High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

Distributed Online Anomaly Detection in High-Content Screening

This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive because it offers the possibility of immediate corrective action, early termination, and redesign of assays that may require many hours or days to execute. The proposed approach employs assay-specific image processing within an assay-independent framework for distributed control, machine learning, and anomaly reporting. Specifically, we exploit coarse-grained parallelism to distribute image processing over several computing nodes while efficiently aggregating sufficient statistics across nodes. This architecture also allows us to easily handle geographically-distributed data sources. Our results from two applications, adipocyte quantitation and neurite growth estimation, confirm that this online approach to anomaly detection is feasible, efficient, and accurate. Index Terms — Anomaly detection, biomedical image processing, distributed database searching, high-content microscopy, Open-Diamond ® search platfor