Kernel Dependency Estimation

We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images

Machine Learning approaches to protein ranking: discriminative, semi-supervised, scalable algorithms

A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST

Derivation of Chondrogenically-Committed Cells from Human Embryonic Cells for Cartilage Tissue Regeneration

Background: Heterogeneous and uncontrolled differentiation of human embryonic stem cells (hESCs) in embryoid bodies (EBs) limits the potential use of hESCs for cell-based therapies. More efficient strategies are needed for the commitment and differentiation of hESCs to produce a homogeneous population of specific cell types for tissue regeneration applications. Methodology/Principal Findings: We report here that significant chondrocytic commitment of feeder-free cultured human embryonic stem cells (FF-hESCs), as determined by gene expression and immunostaining analysis, was induced by coculture with primary chondrocytes. Furthermore, a dynamic expression profile of chondrocyte-specific genes was observed during monolayer expansion of the chondrogenically-committed cells. Chondrogenically-committed cells synergistically responded to transforming growth factor-b1 (TGF-b1) and b1-integrin activating antibody by increasing tissue mass in pellet culture. In addition, when encapsulated in hydrogels, these cells formed cartilage tissue both in vitro and in vivo. In contrast, the absence of chondrocyte co-culture did not result in an expandable cell population from FF-hESCs. Conclusions/Significance: The direct chondrocytic commitment of FF-hESCs can be induced by morphogenetic factor

Qualitative Robustness of Bootstrap Approximations for Kernel Based Methods

status: publishe

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Graft healing in anterior cruciate ligament reconstruction

Successful anterior cruciate ligament reconstruction with a tendon graft necessitates solid healing of the tendon graft in the bone tunnel. Improvement of graft healing to bone is crucial for facilitating an early and aggressive rehabilitation and ensuring rapid return to pre-injury levels activity. Tendon graft healing in a bone tunnel requires bone ingrowth into the tendon. Indirect Sharpey fiber formation and direct fibrocartilage fixation confer different anchorage strength and interface properties at the tendon-bone interface. For enhancing tendon graft-to-bone healing, we introduce a strategy that includes the use of periosteum, hydrogel supplemented with periosteal progenitor cells and bone morphogenetic protein-2, and a periosteal progenitor cell sheet. Future studies include the use of cytokines, gene therapy, stem cells, platelet-rich plasma, and mechanical stress for tendon-to-bone healing. These strategies are currently under investigation, and will be applied in the clinical setting in the near future

Neural networks for genetic epidemiology: past, present, and future

During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes

Gelatin microparticles aggregates as three-dimensional scaffolding system in cartilage engineering

A three-dimensional (3D) scaffolding system for chondrocytes culture has been produced by agglomeration of cells and gelatin microparticles with a mild centrifuging process. The diameter of the microparticles, around 10 μ, was selected to be in the order of magnitude of the chondrocytes. No gel was used to stabilize the construct that maintained consistency just because of cell and extracellular matrix (ECM) adhesion to the substrate. In one series of samples the microparticles were charged with transforming growth factor, TGF-β1. The kinetics of growth factor delivery was assessed. The initial delivery was approximately 48 % of the total amount delivered up to day 14. Chondrocytes that had been previously expanded in monolayer culture, and thus dedifferentiated, adopted in this 3D environment a round morphology, both with presence or absence of growth factor delivery, with production of ECM that intermingles with gelatin particles. The pellet was stable from the first day of culture. Cell viability was assessed by MTS assay, showing higher absorption values in the cell/unloaded gelatin microparticle pellets than in cell pellets up to day 7. Nevertheless the absorption drops in the following culture times. On the contrary the cell viability of cell/TGF-β1 loaded gelatin microparticle pellets was constant during the 21 days of culture. The formation of actin stress fibres in the cytoskeleton and type I collagen expression was significantly reduced in both cell/gelatin microparticle pellets (with and without TGF-β1) with respect to cell pellet controls. Total type II collagen and sulphated glycosaminoglycans quantification show an enhancement of the production of ECM when TGF-β1 is delivered, as expected because this growth factor stimulate the chondrocyte proliferation and improve the functionality of the tissue.JLGR acknowledge the support of the Spanish Ministry of Education through project No. MAT2010-21611-C03-01 (including the FEDER financial support). The support of the Instituto de Salud Carlos III (ISCIII) through the CIBER initiative of the Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) is also acknowledged