A review on machine learning approaches and trends in drug discovery.

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Using Machine Learning to Collect and Facilitate Remote Access to Biomedical Databases: Development of the Biomedical Database Inventory.

BACKGROUND: Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. OBJECTIVE: To address this issue, we developed the Biomedical Database Inventory (BiDI), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them seamlessly. METHODS: We designed an ensemble of deep learning methods to extract database mentions. To train the system, we annotated a set of 1242 articles that included mentions of database publications. Such a data set was used along with transfer learning techniques to train an ensemble of deep learning natural language processing models targeted at database publication detection. RESULTS: The system obtained an F1 score of 0.929 on database detection, showing high precision and recall values. When applying this model to the PubMed and PubMed Central databases, we identified over 10,000 unique databases. The ensemble model also extracted the weblinks to the reported databases and discarded irrelevant links. For the extraction of weblinks, the model achieved a cross-validated F1 score of 0.908. We show two use cases: one related to "omics" and the other related to the COVID-19 pandemic. CONCLUSIONS: BiDI enables access to biomedical resources over the internet and facilitates data-driven research and other scientific initiatives. The repository is openly available online and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (ie, biomedical and others).

A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images.

BACKGROUND AND OBJECTIVE: Nanoparticles present properties that can be applied to a wide range of fields such as biomedicine, electronics or optics. The type of properties depends on several characteristics, being some of them related with the particle structure. A proper characterization of nanoparticles is crucial since it could affect their applications. To characterize a particle shape and size, the nanotechnologists employ Electron Microscopy (EM) to obtain images of nanoparticles and perform measures over them. This task could be tedious, repetitive and slow, we present a Deep Learning method based on Convolutional Neural Networks (CNNs) to detect, segment, infer orientations and reconstruct microscope images of nanoparticles. Since machine learning algorithms depend on annotated data and there is a lack of annotated datasets of nanoparticles, our work makes use of artificial datasets of images resembling real nanoparticles photographs. METHODS: Our work is divided into three tasks. Firstly, a method to create annotated datasets of artificial images resembling Scanning Electron Microscope (SEM). Secondly, two models of convolutional neural networks are trained using the artificial datasets previously generated, the first one is in charge of the detection and segmentation of the nanoparticles while the second one will infer the nanoparticle orientation. Finally, the 3D reconstruction module will recreate in a 3D scene the set of detected particles. RESULTS: We have tested our method with five different shapes of basic nanoparticles: spheres, cubes, ellipsoids, hexagonal discs and octahedrons. An analysis of the reconstructions was conducted by manually comparing each of them with the real images. The results obtained have been promising, the particles are segmented and reconstructed accordingly to their shapes and orientations. CONCLUSIONS: We have developed a method for nanoparticle detection and segmentation in microscope images. Moreover, we can also infer an approximation of the 3D orientation of the particles and, in conjunction with the detections, create a 3D reconstruction of the photographs. The novelty of our approximation lies in the dataset used. Instead of using annotated images, we have created the datasets simulating the microscope images by using basic geometrical objects that imitate real nanoparticles.

genoDraw: A Web Tool for Developing Pedigree Diagrams Using the Standardized Human Pedigree Nomenclature Integrated with Biomedical Vocabularies.

The integration of genetic information in current clinical routine has raised a need for tools to exploit family genetic knowledge. On the clinical side, an application for managing and visualizing pedigree diagrams could provide genetics specialists with an integrated environment with potential positive impact on their current practice. This article presents a web tool (genoDraw) that provides clinical practitioners with the ability to create, maintain and visualize patients' and their families' information in the form of pedigree diagrams. genoDraw implements a graph-based three-step process for generating diagrams according to a de facto standard in the area and clinical terminologies. It also complies with five characteristics identified as indispensable for the next-generation of pedigree drawing software: comprehensiveness, data-drivenness, automation, interactivity and compatibility with biomedical vocabularies. The platform was implemented and tested, confirming its potential interest to clinical routine.

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R²) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.

A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer.

INTRODUCTION: The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions due to omic profiling presents a complex scenario to carry out multi-center clinical trials. METHODS: This paper presents a standards-based approach to ensure semantic integration required to facilitate the analysis of clinico-genomic clinical trials. To ensure interoperability across different institutions, we have developed a Semantic Interoperability Layer (SIL) to facilitate homogeneous access to clinical and genetic information, based on different well-established biomedical standards and following International Health (IHE) recommendations. RESULTS: The SIL has shown suitability for integrating biomedical knowledge and technologies to match the latest clinical advances in healthcare and the use of genomic information. This genomic data integration in the SIL has been tested with a diagnostic classifier tool that takes advantage of harmonized multi-center clinico-genomic data for training statistical predictive models. CONCLUSIONS: The SIL has been adopted in national and international research initiatives, such as the EURECA-EU research project and the CIMED collaborative Spanish project, where the proposed solution has been applied and evaluated by clinical experts focused on clinico-genomic studies.

Research Strategies for Biomedical and Health Informatics. Some Thought-provoking and Critical Proposals to Encourage Scientific Debate on the Nature of Good Research in Medical Informatics.

BACKGROUND: Medical informatics, or biomedical and health informatics (BMHI), has become an established scientific discipline. In all such disciplines there is a certain inertia to persist in focusing on well-established research areas and to hold on to well-known research methodologies rather than adopting new ones, which may be more appropriate. OBJECTIVES: To search for answers to the following questions: What are research fields in informatics, which are not being currently adequately addressed, and which methodological approaches might be insufficiently used? Do we know about reasons? What could be consequences of change for research and for education? METHODS: Outstanding informatics scientists were invited to three panel sessions on this topic in leading international conferences (MIE 2015, Medinfo 2015, HEC 2016) in order to get their answers to these questions. RESULTS: A variety of themes emerged in the set of answers provided by the panellists. Some panellists took the theoretical foundations of the field for granted, while several questioned whether the field was actually grounded in a strong theoretical foundation. Panellists proposed a range of suggestions for new or improved approaches, methodologies, and techniques to enhance the BMHI research agenda. CONCLUSIONS: The field of BMHI is on the one hand maturing as an academic community and intellectual endeavour. On the other hand vendor-supplied solutions may be too readily and uncritically accepted in health care practice. There is a high chance that BMHI will continue to flourish as an important discipline; its innovative interventions might then reach the original objectives of advancing science and improving health care outcomes.

Discussion of "The New Role of Biomedical Informatics in the Age of Digital Medicine".

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "The New Role of Biomedical Informatics in the Age of Digital Medicine" written by Fernando J. Martin-Sanchez and Guillermo H. Lopez-Campos [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Martin-Sanchez and Lopez-Campos. In subsequent issues the discussion can continue through letters to the editor.

A method and software framework for enriching private biomedical sources with data from public online repositories.

Modern biomedical research relies on the semantic integration of heterogeneous data sources to find data correlations. Researchers access multiple datasets of disparate origin, and identify elements-e.g. genes, compounds, pathways-that lead to interesting correlations. Normally, they must refer to additional public databases in order to enrich the information about the identified entities-e.g. scientific literature, published clinical trial results, etc. While semantic integration techniques have traditionally focused on providing homogeneous access to private datasets-thus helping automate the first part of the research, and there exist different solutions for browsing public data, there is still a need for tools that facilitate merging public repositories with private datasets. This paper presents a framework that automatically locates public data of interest to the researcher and semantically integrates it with existing private datasets. The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI).

Towards the taxonomic categorization and recognition of nanoparticle shapes.

The shape of nanoparticles and nanomaterials is a fundamental characteristic that has been shown to influence a number of their properties and effects, particularly for nanomedical applications. The information related with this feature of nanoparticles and nanomaterials is, therefore, crucial to exploit and foster in existing and future research in this area. We have found that descriptions of morphological and spatial properties are consistently reported in the nanotechnology literature, and in general, these morphological properties can be observed and measured using various microscopy techniques. In this paper, we outline a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts that can be used to address the problem of nanomaterial categorization. We employ an image segmentation technique, belonging to the mathematical morphology field, which is capable of identifying shapes in images that can be used to (semi-) automatically annotate nanoparticle images. FROM THE CLINICAL EDITOR: This team of authors outlines a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts enabling nanomaterial categorization. They also employ a mathematical morphology-based image segmentation system, capable of identifying shapes and can be utilized in semi-automated annotation of nanoparticle images.

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov.

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Assessing the prognoses on Health care in the information society 2013--thirteen years after.

Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 - have been fulfilled in 2013 and which might be the reasons for match or mismatch. Twenty international experts in biomedical and health informatics met in May 2013 in a workshop to discuss match or mismatch of each of the 71 prognoses. After this meeting a web-based survey among workshop participants took place. Thirty-three prognoses were assessed matching; they reflect e.g. that there is good progress in storing patient data electronically in health care institutions. Twenty-three prognoses were assessed mismatching; they reflect e.g. that telemedicine and home monitoring as well as electronic exchange of patient data between institutions is not established as widespread as expected. Fifteen prognoses were assessed neither matching nor mismatching. ICT tools have considerably influenced health care in the last decade, but in many cases not as far as it was expected by Haux et al. in 2002. In most cases this is not a matter of the availability of technical solutions but of organizational and ethical issues. We need innovative and modern information system architectures which support multiple use of data for patient care as well as for research and reporting and which are able to integrate data from home monitoring into a patient centered health record. Since innovative technology is available the efficient and wide-spread use in health care has to be enabled by systematic information management.

Past and next 10 years of medical informatics.

More than 10 years ago Haux et al. tried to answer the question how health care provision will look like in the year 2013. A follow-up workshop was held in Braunschweig, Germany, for 2 days in May, 2013, with 20 invited international experts in biomedical and health informatics. Among other things it had the objectives to discuss the suggested goals and measures of 2002 and how priorities on MI research in this context should be set from the viewpoint of today. The goals from 2002 are now as up-to-date as they were then. The experts stated that the three goals: "patient-centred recording and use of medical data for cooperative care"; "process-integrated decision support through current medical knowledge" and "comprehensive use of patient data for research and health care reporting" have not been reached yet and are still relevant. A new goal for ICT in health care should be the support of patient centred personalized (individual) medicine. MI as an academic discipline carries out research concerning tools that support health care professionals in their work. This research should be carried out without the pressure that it should lead to systems that are immediately and directly accepted in practice.

NCBI2RDF: enabling full RDF-based access to NCBI databases.

RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over different NCBI repositories and presenting to users the query results in SPARQL results format, thus enabling this data to be integrated and/or stored with other RDF-compliant repositories. SPARQL queries are dynamically resolved, decomposed, and forwarded to the NCBI-provided E-utilities programmatic interface to access the NCBI data. Furthermore, we show how our approach increases the expressiveness of the native NCBI querying system, allowing several databases to be accessed simultaneously. This feature significantly boosts productivity when working with complex queries and saves time and effort to biomedical researchers. Our approach has been validated with a large number of SPARQL queries, thus proving its reliability and enhanced capabilities in biomedical environments.

Fostering ontology alignment sharing: a general-purpose RDF mapping format.

RDF has established in the last years as the language for describing, publishing and sharing biomedical resources. Following this trend, a great amount of RDF-based data sources, as well as ontologies, have appeared. Using a common language as RDF has provided a unified syntactic for sharing resources, but the semantics remain as the main cause of heterogeneity, hampering data integration and homogenization efforts. To overcome this issue, ontology alignment based solutions have been typically used. However, alignment information is usually codified using ad-hoc formats. In this paper, we present a general purpose ontology mapping format, totally independent from the homogenization approach to be applied. The format is accompanied with a Java API that offers mapping construction and parsing features, as well as some basic algorithms for applying it to data translation solutions.

A data model based on semantically enhanced HL7 RIM for sharing patient data of breast cancer clinical trials.

Breast cancer clinical trial researchers have to handle heterogeneous data coming from different data sources, overloading biomedical researchers when they need to query data for retrospective analysis. This paper presents the Common Data Model (CDM) proposed within the INTEGRATE EU project to homogenize data coming from different clinical partners. This CDM is based on the Reference Information Model (RIM) from the Health Level 7 (HL7) version 3. Semantic capabilities through an SPARQL endpoint were also required to ensure the sustainability of the platform. For the SPARQL endpoint implementation, a comparison has been carried out between a Relational SQL database + D2R and a RDF database. The results show that the first option can store all clinical data received from institutions participating in the project with a better performance. It has been also evaluated by the EU Commission within a patient recruitment demonstrator.

Analyzing SNOMED CT and HL7 terminology binding for semantic interoperability on post-genomic clinical trials.

Current post-genomic clinical trials in cancer involve the collaboration of several institutions. Multi-centric retrospective analysis requires advanced methods to ensure semantic interoperability. In this scenario, the objective of the EU funded INTEGRATE project, is to provide an infrastructure to share knowledge and data in post-genomic breast cancer clinical trials. This paper presents the process carried out in this project, to bind domain terminologies in the area, such as SNOMED CT, with the HL7 v3 Reference Information Model (RIM). The proposed terminology binding follow the HL7 recommendations, but should also consider important issues such as overlapping concepts and domain terminology coverage. Although there are limitations due to the large heterogeneity of the data in the area, the proposed process has been successfully applied within the context of the INTEGRATE project. An improvement in semantic interoperability of patient data from modern breast cancer clinical trials, aims to enhance the clinical practice in oncology.

Enhancing research capacity of African institutions through social networking.

Traditionally, participation of African researchers in top Biomedical Informatics (BMI) scientific journals and conferences has been scarce. Looking beyond these numbers, an educational goal should be to improve overall research and, therefore, to increase the number of scientists/authors able to produce and publish high quality research. In such scenario, we are carrying out various efforts to expand the capacities of various institutions located at four African countries - Egypt, Ghana, Cameroon and Mali - in the framework of a European Commission-funded project, AFRICA BUILD. This project is currently carrying out activities such as e-learning, collaborative development of informatics tools, mobility of researchers, various pilot projects, and others. Our main objective is to create a self-sustained South-South network of BMI developers.