Assessment of clustering techniques to support the analyses of soybean seed vigor.

Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting.

Classification of Skin Lesion through Active Learning Strategies.

Background And Objective: According to the National Cancer Institute, among all malignant tumors, non-melanoma skin cancer, and melanoma are the most frequent in Brazil. Despite having a lower incidence, the melanoma type has accelerated growth and greater lethality. Several studies have been performed in recent years in the computer vision area to assist in the early diagnosis of skin cancer.

Computational Analysis of Transposable Elements and CircRNAs in Plants.

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features.

TERL: classification of transposable elements by convolutional neural networks.

Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology-based search, which could be inefficient for classifying non-homologous sequences.

Active semi-supervised learning for biological data classification.

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting.

Contributing to agriculture by using soybean seed data from the tetrazolium test.

Agribusiness has a great relevance in the world׳s economy. It generates a considerable impact in the gross national product of several nations. Hence, it is the major driver of many national economies.

Exploring Active Learning Based on Representativeness and Uncertainty for Biomedical Data Classification.

Nowadays, there is an abundance of biomedical data, such as images and genetic sequences, among others. However, there is a lack of annotation to such volume of data, due to the high costs involved to perform this task. Thus, it is mandatory to develop techniques to ease the burden of human annotation.

Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.

Motivation: Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles.

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity.