Artificial intelligence to assist specialists in the detection of haematological diseases.

Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes. The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includes several tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals. In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studied and combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data. The use of these machine learning techniques could reduce the cost and decision time and improve the quality of life for both specialists and patients while producing more precise diagnoses.

Unraveling Genome Evolution Throughout Visual Analysis: The XCout Portal.

Due to major breakthroughs in sequencing technologies throughout the last decades, the time and cost per sequencing experiment have reduced drastically, overcoming the data generation barrier during the early genomic era. Such a shift has encouraged the scientific community to develop new computational methods that are able to compare large genomic sequences, thus enabling large-scale studies of genome evolution. The field of comparative genomics has proven itself invaluable for studying the evolutionary mechanisms and the forces driving genome evolution. In this line, a full genome comparison study between 2 species requires a quadratic number of comparisons in terms of the number of sequences (around 400 chromosome comparisons in the case of mammalian genomes); however, when studying conserved syntenies or evolutionary rearrangements, many sequence comparisons can be skipped for not all will contain significant signals. Subsequently, the scientific community has developed fast heuristics to perform multiple pairwise comparisons between large sequences to determine whether significant sets of conserved similarities exist. The data generation problem is no longer an issue, yet the limitations have shifted toward the analysis of such massive data. Therefore, we present XCout, a Web-based visual analytics application for multiple genome comparisons designed to improve the analysis of large-scale evolutionary studies using novel techniques in Web visualization. XCout enables to work on hundreds of comparisons at once, thus reducing the time of the analysis by identifying significant signals between chromosomes across multiple species. Among others, XCout introduces several techniques to aid in the analysis of large-scale genome rearrangements, particularly (1) an interactive heatmap interface to display comparisons using automatic color scales based on similarity thresholds to ease detection at first sight, (2) an overlay system to detect individual signal contributions between chromosomes, (3) a tracking tool to trace conserved blocks across different species to perform evolutionary studies, and (4) a search engine to search annotations throughout different species.

Ultra-fast genome comparison for large-scale genomic experiments.

In the last decade, a technological shift in the bioinformatics field has occurred: larger genomes can now be sequenced quickly and cost effectively, resulting in the computational need to efficiently compare large and abundant sequences. Furthermore, detecting conserved similarities across large collections of genomes remains a problem. The size of chromosomes, along with the substantial amount of noise and number of repeats found in DNA sequences (particularly in mammals and plants), leads to a scenario where executing and waiting for complete outputs is both time and resource consuming. Filtering steps, manual examination and annotation, very long execution times and a high demand for computational resources represent a few of the many difficulties faced in large genome comparisons. In this work, we provide a method designed for comparisons of considerable amounts of very long sequences that employs a heuristic algorithm capable of separating noise and repeats from conserved fragments in pairwise genomic comparisons. We provide software implementation that computes in linear time using one core as a minimum and a small, constant memory footprint. The method produces both a previsualization of the comparison and a collection of indices to drastically reduce computational complexity when performing exhaustive comparisons. Last, the method scores the comparison to automate classification of sequences and produces a list of detected synteny blocks to enable new evolutionary studies.

Combining Strengths for Multi-genome Visual Analytics Comparison.

The eclosion of data acquisition technologies has shifted the bottleneck in molecular biology research from data acquisition to data analysis. Such is the case in Comparative Genomics, where sequence analysis has transitioned from genes to genomes of several orders of magnitude larger. This fact has revealed the need to adapt software to work with huge experiments efficiently and to incorporate new data-analysis strategies to manage results from such studies. In previous works, we presented GECKO, a software to compare large sequences; now we address the representation, browsing, data exploration, and post-processing of the massive amount of information derived from such comparisons. GECKO-MGV is a web-based application organized as client-server architecture. It is aimed at visual analysis of the results from both pairwise and multiple sequences comparison studies combining a set of common commands for image exploration with improved state-of-the-art solutions. In addition, GECKO-MGV integrates different visualization analysis tools while exploiting the concept of layers to display multiple genome comparison datasets. Moreover, the software is endowed with capabilities for contacting external-proprietary and third-party services for further data post-processing and also presents a method to display a timeline of large-scale evolutionary events. As proof-of-concept, we present 2 exercises using bacterial and mammalian genomes which depict the capabilities of GECKO-MGV to perform in-depth, customizable analyses on the fly using web technologies. The first exercise is mainly descriptive and is carried out over bacterial genomes, whereas the second one aims to show the ability to deal with large sequence comparisons. In this case, we display results from the comparison of the first Homo sapiens chromosome against the first 5 chromosomes of Mus musculus.

mORCA: ubiquitous access to life science web services.

BACKGROUND: Technical advances in mobile devices such as smartphones and tablets have produced an extraordinary increase in their use around the world and have become part of our daily lives. The possibility of carrying these devices in a pocket, particularly mobile phones, has enabled ubiquitous access to Internet resources. Furthermore, in the life sciences world there has been a vast proliferation of data types and services that finish as Web Services. This suggests the need for research into mobile clients to deal with life sciences applications for effective usage and exploitation. RESULTS: Analysing the current features in existing bioinformatics applications managing Web Services, we have devised, implemented, and deployed an easy-to-use web-based lightweight mobile client. This client is able to browse, select, compose parameters, invoke, and monitor the execution of Web Services stored in catalogues or central repositories. The client is also able to deal with huge amounts of data between external storage mounts. In addition, we also present a validation use case, which illustrates the usage of the application while executing, monitoring, and exploring the results of a registered workflow. The software its available in the Apple Store and Android Market and the source code is publicly available in Github. CONCLUSIONS: Mobile devices are becoming increasingly important in the scientific world due to their strong potential impact on scientific applications. Bioinformatics should not fall behind this trend. We present an original software client that deals with the intrinsic limitations of such devices and propose different guidelines to provide location-independent access to computational resources in bioinformatics and biomedicine. Its modular design makes it easily expandable with the inclusion of new repositories, tools, types of visualization, etc.

mORCA: sailing bioinformatics world with mobile devices.

Motivation: Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers to mobile devices but nothing could be further from the truth. The transition demands new paradigms, designs and novel implementations. Results: Throughout an in-depth analysis of requirements of existing bioinformatics applications we designed and deployed an easy-to-use web-based lightweight mobile client. Such client is able to browse, select, compose automatically interface parameters, invoke services and monitor the execution of Web Services using the service's metadata stored in catalogs or repositories. Availability and implementation: mORCA is available at http://bitlab-es.com/morca/app as a web-app. It is also available in the App store by Apple and Play Store by Google. The software will be available for at least 2 years. Contact: ortrelles@uma.es. Supplementary information: Source code, final web-app, training material and documentation is available at http://bitlab-es.com/morca.