An Accurate Cell Detection with Minimal Training Effort

Wining talk at the 12th Annual Graduate Research Fair at UNC Charlotte. This is the practice version.

Leukemia (blood cancer)

stress, viral infection, drug intake

healthy

flu, poisoning

What would aid them to move toward cancer tumor?

Automatic
Detection

100
clicks

Incorrectly Undetected Correctly
detected cell cell detected cell

Automatic
Detection

10
clicks

Incorrectly Undetected Correctly
detected cell cell detected cell

Our
Method

10
clicks

Incorrectly Undetected Correctly
detected cell cell detected cell

1. many types 2. first-timer 3. label effort

Construct cell Learn from
size distribution previous types ?

Training Image
Cell

Non-
cell

Training Samples

User
Size Distribution

GATLAB
label effort
random
interactive
Previous types
Select most important
samples for user to label.

Training Image
Cell

Non-
cell

Training Samples

User
Size Distribution

Detection Confidence
GATLAB

Previous types

White Blood Cells HT29 Cancer Natural Killer T Drosophila Red Blood Cells

AdaBoost uses Adaptive Boosting
TaskTrAdaBoost learns from previous cell types

GlobalTrAdaBoost obtains cell size distribution
GATLAB selects most important samples

Freund and Schapire (2000)
Yao and Doretto (2010)
Nguyen et al. (2011)

Training samples were selected from 1 to 10.
Execute training and testing 30 times.

Training samples were selected up to 100 samples.

Natural Killer T-cells

*presented in the 11th GRF (2011)

An accurate cell detection algorithm.

Require minimal training effort.

Help biologists to study various cell types.

N. Nguyen, E. Norris, M. Clemens, M. Shin. “Rapidly Adaptive Cell Detection.”
Machine Vision and Applications (MVA), Special Issue: Machine Learning in
Medical Imaging [in review].

N. Nguyen and M. Shin. “Active Transfer Boosting to Reduce Training Effort in
Multi-class Data classification." IEEE International Conference on Computer
Vision and Pattern Recognition (CVPR), Providence, Rhode Island, June 18-20, 2012
[in review].

N. Nguyen, E. Norris, M. Clemens, M. Shin. “Rapidly Adaptive Cell Detection using
Transfer Learning with a Global Parameter.” The Second International
Workshop on Machine Learning in Medical Imaging (MLMI), Toronto, Canada.
September 18-22, 2011.

N. Nguyen, S. Keller, E. Norris, T. Huynh, M. Clemens, M. Shin. “Tracking Colliding
Cells in vivo Microscopy Video.” IEEE Transactions on Biomedical Engineering
(TBE), 58(8):2391-2400, August 2011.

N. Nguyen, S. Keller, T. Huynh, M. Shin. “Tracking Colliding Cells”. IEEE Workshop
on Applications of Computer Vision (WACV), Snowbird, UT December 07-09,
2009.

Min Shin, PhD Mark Clemens, PhD Eric Norris, MS Toan Huynh, MD Steve Keller, MS

An Accurate Cell Detection with Minimal Training Effort

In this deck from DOE CSGF 2019, Sarah Middleton from GlaxoSmithKline presents: Single-Cell Sequencing for Drug Discovery: Applications and Challenges. "Most gene expression studies have been performed on “bulk” tissue samples, consisting of millions of cells of various types and functions mixed together. Although such research is extremely useful for comparing general characteristics of tissues – for example, before and after therapeutic drug treatment – they are limited in their ability to inform on the characteristics and responses of different cell types within the tissue. More recently, advances in techniques for single-cell RNA sequencing have made it possible to profile gene expression in individual cells on a large scale, opening up the possibility to explore the heterogeneity of expression within and across cell types. This exciting technology is now being applied to almost every tissue in the human body, with some experiments generating expression profiles for more than 100,000 cells at a time. However, several roadblocks still stand in the way of making full use of this information, including issues related to missing data and scaling up existing bioinformatics analytical tools. Here, I will discuss some of the ways single-cell RNA-sequencing is being used to improve drug discovery and development, as well as some of the challenges we currently face. I’ll also highlight a new approach I am developing to help overcome some of these challenges." Watch the video: https://wp.me/p3RLHQ-lkw Learn more: https://www.krellinst.org/csgf/conf/2019/agenda Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

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Next generation sequencing (NGS) of circulating tumor DNA (ctDNA) from patient plasma is becoming more widespread in oncology clinical trials. The noninvasive nature of acquiring these samples is particularly important when resection of representative tumor samples is not advised or not possible. However, profiling of ctDNA has challenges to overcome, such as low concentration of ctDNA shed from the tumor and a low signal:noise ratio caused by somatic alterations with less than 1% variant allele fraction. Improving the sensitivity of these assays to detect low allele frequency events with high confidence requires robust sequencing of low input libraries while employing error correction to reduce background noise. To overcome these challenges, we have incorporated unique molecular identifiers (UMIs) into our NGS workflow. Using these novel adapters paired with our proprietary bioinformatics pipeline (AstraZeneca), the number of false positive variants reported for allele fractions less than 0.5% was reduced tenfold. We also refined our curation based on the mapping quality and strand bias in the vicinity of each variant to further reduce the background noise. The use of xGen® Dual Index UMI Adapters—Tech Access (Integrated DNA Technologies) has enabled us to sequence thousands of plasma samples from diverse tumor indications and at differing time points during our trials. The generated data are highly informative with the potential to answer critical questions relating to individual response or resistance to experimental therapies. During this webinar, we discuss our current NGS ctDNA workflow and our future plans to increase our sequencing sensitivity with these novel UMI adapters.

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The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.

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Over the past 5 years, single-cell genomics have become a powerful technology for studying small samples and rare cells, and for dissecting complex populations such as heterogeneous tumors. Single-cell technology is enabling many new insights into diverse research areas from oncology, immunology and microbiology to neuroscience, stem cell and developmental biology. This webinar introduces single-cell technology and summarizes the newest scientific applications in various research areas, all in the context of current literature.

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A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction. Decision trees provide an intuitive visual form that can capture complex interactions between multiple variables. Effective methods exist for inferring decision trees automatically but it has been shown that these techniques can be improved upon via the manual interventions of experts. Here, we introduce Branch, a new Web-based tool for the interactive construction of decision trees from genomic datasets. Branch offers the ability to: (1) upload and share datasets intended for classification tasks (in progress), (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision trees, (4) create feature functions that aggregate content from multiple independent features into single decision nodes (e.g. pathways) and (5) evaluate decision tree classifiers in terms of precision and recall. The tool is optimized for genomic use cases through the inclusion of gene and pathway-based search functions. Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers. A prototype of Branch is available at http://biobranch.org/

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Large scale machine learning challenges for systems biology by dr. Yvan Saeys - Machine Learning and Data Mining group, Bioinformatics and Systems Biology Division, VIB-UGent Department of Plant Systems Biology Due to technological advances, the amount of biological data, and the pace at which it is generated has increased dramatically during the past decade. To extract new knowledge from these ever increasing data sets, automated techniques such as data mining and machine learning techniques have become standard practice. In this talk, I will give an overview of large scale machine learning challenges in bioinformatics and systems biology, highlighting the importance of using scalable and robust techniques such as ensemble learning methods implemented on large computing grids. I will present some of our state-of-the-art tools to solve problems such as biomarker discovery, large scale network inference, and biomedical text mining at PubMed scale.

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Artficial Intelligence (AI) studies and designs intelligent agents, e.g. systems that perceive their environment and take actions that maximize the chances of success. In microscopy a success is often understood when the automated image analysis effciently detects phenotypes, as in biological screens, or retrieves a diagnostically relevant statistics from images, as in the medical diagnosis. During the talk I will present two applications aimed at supporting high-content screening and diagnosis of cervical cancer where subdomains of AI, e.g. Evolutionary Algorithm, Neural Networks and Machine Learning techniques were applied.

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