Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers

BackgroundGenetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions. Methodology/Principal Findings Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPARγ, TSHR, GNAS and NRAS genes. Conclusion/Significance We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas

CLEF2014 working notes

This paper describes the participation of Inria within the Pl@ntNet project7 at the LifeCLEF2014 plant identication task. The aim of the task was to produce a list of relevant species for each plant observation in a test dataset according to a training dataset. Each plant observation contains several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). Our system treated independently each category of organ/view and then a late hierarchical fusion is used in order to combine the results on visual content analysis from the most local level analysis in pictures to the highest level related to a plant observation. For the photographs of flowers, leaves, fruits, stems, branches and entire views of plants, a large scale matching approach of local features extracted using different spatial constraints is used. For scans, the method combines the large scale matching approach with shape descriptors and geometric parameters on shape boundary. Then, several fusion methods are experimented through the four submitted runs in order to combine hierarchically the local responses to the final response at the plant observation level. The four submitted runs obtained good results and got the 4th to the 7th place over 27 submitted runs by 10 participating teams

Biodiversity information retrieval through large scale content-based identification: a long-term evaluation

Identifying and naming living plants or animals is usually impossible for the general public and often a difficult task for professionals and naturalists. Bridging this gap is a key challenge towards enabling effective biodiversity information retrieval systems. This taxonomic gap was actually already identified as one of the main ecological challenges to be solved during the Rio de Janeiro United Nations “Earth Summit” in 1992. Since 2011, the LifeCLEF challenges conducted in the context of the CLEF evaluation forum have been boosting and evaluating the advances in this domain. Data collections with an unprecedented volume and diversity have been shared with the scientific community to allow repeatable and long-term experiments. This paper describes the methodology of the conducted evaluation campaigns as well as providing a synthesis of the main results and lessons learned along the years

Quels botanistes pour le 21e siècle ? Métiers, enjeux et opportunités

Pl@ntNet est un réseau humain s'appuyant sur une infrastructure informatique, permettant l'identification, l'agrégation et le partage d'observations botaniques à très grande échelle. Cette initiative mobilise différentes institutions de recherche dans divers champs scientifiques (informatique, agronomie, écologie) et de larges réseaux associatifs de naturalistes ; elle a permis au cours des 20 derniers mois la collecte de plusieurs dizaines de milliers d'observations de plantes sur le territoire européen. Celles-ci sont collectées à travers un système web ou mobile d'aide à l'identification des plantes par l'image, au sein duquel chacun des participants peut partager des observations déterminées ou non, avec différents niveaux d'informations. Ces observations, sont ensuite révisées collaborativement à travers 2 applications web, l'une dédiée à la révision de la détermination des observations (IdentiPlante), l'autre à l'estimation de la qualité visuelle des images illustrant l'observation (PictoFlora). Les observations révisées et / ou validées, viennent alors enrichir la base d'observations publiques exploitée par le système d'aide à l'identification par l'image. Bien que cette infrastructure logicielle soit encore récente (lancement des applications mobiles en févier 2013), elle a été exploitée au cours des 20 derniers mois par près de 300 000 utilisateurs à travers le monde. Les modalités de fonctionnement de celle-ci, ainsi que les perspectives d'évolution sont présentées dans cet article, qui se conclut par une discussion sur les changements actuels que les nouvelles technologies de l'information permettent d'opérer en Botanique