BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Background: Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. Results: We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. Conclusions: The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge. net/projects/maxbin/.

/pdf/maxbin-an-automated-binning-method-to-recover-individual-4n6vhittp7.pdf

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm.

Data of different levels of complexity and of ever growing diversity of characteristics are the raw materials that machine learning practitioners try to model using their wide palette of methods and tools. The obtained models are meant to be a synthetic representation of the available, observed data that captures some of their intrinsic regularities or patterns. Therefore, the use of machine learning techniques for data analysis can be understood as a problem of pattern recognition or, more informally, of knowledge discovery and data mining. There exists a gap, though, between data modeling and knowledge extraction. Models, de- pending on the machine learning techniques employed, can be described in diverse ways but, in order to consider that some knowledge has been achieved from their description, we must take into account the human cog- nitive factor that any knowledge extraction process entails. These models as such can be rendered powerless unless they can be interpreted ,a nd the process of human interpretation follows rules that go well beyond techni- cal prowess. For this reason, interpretability is a paramount quality that machine learning methods should aim to achieve if they are to be applied in practice. This paper is a brief introduction to the special session on interpretable models in machine learning, organized as part of the 20 th European Symposium on Artificial Neural Networks, Computational In- telligence and Machine Learning. It includes a discussion on the several works accepted for the session, with an overview of the context of wider research on interpretability of machine learning models.

Making machine learning models interpretable

The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.

Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

Advances in neural information processing systems 3

https://pure.rug.nl/ws/files/41826209/es2012_99.pdf

Visualizing the quality of dimensionality reduction

https://research.birmingham.ac.uk/portal/files/18560768/Mokbela_et_al_Metric_learning_for_sequences_Neurocomputing_2015.pdf

Metric learning for sequences in relational LVQ

t-distributed stochastic neighbor embedding (t-SNE) constitutes a nonlinear dimensionality reduction technique which is particularly suited to visualize high dimensional data sets with intrinsic nonlinear structures. A major drawback, however, consists in its squared complexity which makes the technique infeasible for large data sets or online application in an interactive framework. In addition, since the technique is non parametric, it possesses no direct method to extend the technique to novel data points. In this contribution, we propose an extension of t-SNE to an explicit mapping. In the limit, it reduces to standard non-parametric t-SNE, while offering a feasible nonlinear embedding function for other parameter choices. We evaluate the performance of the technique when trained on a small subpart of the given data only. It turns out that its generalization ability is good when evaluated with the standard quality curve. Further, in many cases, it obtains a quality which approximates the quality of t-SNE when trained on the full data set, albeit only 10% of the data are used for training. This opens the way towards efficient nonlinear dimensionality reduction techniques as required in interactive settings.

Linear basis-function t-SNE for fast nonlinear dimensionality reduction

Relational generative topographic mapping

ESANN 2012 : 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 25-27 April 2012

Bassam Mokbel

Papers

Visualizing the quality of dimensionality reduction

Metric learning for sequences in relational LVQ

Linear basis-function t-SNE for fast nonlinear dimensionality reduction

Relational generative topographic mapping

Out-of-sample kernel extensions for nonparametric dimensionality reduction