Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study.

Background: Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior.

Objective: To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user's likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management.

Methods: A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years.

TP-DDI: Transformer-based pipeline for the extraction of Drug-Drug Interactions.

Drug-Drug Interaction (DDI) extraction is the task of identifying drug entities and the potential interactions between drug pairs from biomedical literature. Computer-aided extraction of DDIs is vital for drug discovery, as this process remains extremely expensive and time consuming. Therefore, Machine Learning-based approaches can reduce the laborious task during the drug development cycle.

TransforMED: End-to-Εnd Transformers for Evidence-Based Medicine and Argument Mining in medical literature.

Argument Mining (AM) refers to the task of automatically identifying arguments in a text and finding their relations. In medical literature this is done by identifying Claims and Premises and classifying their relations as either Support or Attack. Evidence-Based Medicine (EBM) refers to the task of identifying all related evidence in medical literature to allow medical practitioners to make informed choices and form accurate treatment plans.

EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature.

Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population, Intervention, Comparison, Outcome), have been developed to enable fine-grained searches, as the first step to faster decision making.

Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models.

Background: In this paper we present the approach that we employed to deal with large scale multi-label semantic indexing of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge (2013-2017), a challenge concerned with biomedical semantic indexing and question answering.

Methods: Our main contribution is a MUlti-Label Ensemble method (MULE) that incorporates a McNemar statistical significance test in order to validate the combination of the constituent machine learning algorithms.

Chronic Lymphocytic Leukemia with Mutated IGHV4-34 Receptors: Shared and Distinct Immunogenetic Features and Clinical Outcomes.

We sought to investigate whether B cell receptor immunoglobulin (BcR IG) stereotypy is associated with particular clinicobiological features among chronic lymphocytic leukemia (CLL) patients expressing mutated BcR IG (M-CLL) encoded by the IGHV4-34 gene, and also ascertain whether these associations could refine prognostication. In a series of 19,907 CLL cases with available immunogenetic information, we identified 339 IGHV4-34-expressing cases assigned to one of the four largest stereotyped M-CLL subsets, namely subsets #4, #16, #29 and #201, and investigated in detail their clinicobiological characteristics and disease outcomes. We identified shared and subset-specific patterns of somatic hypermutation (SHM) among patients assigned to these subsets.

Integrating multiple immunogenetic data sources for feature extraction and mining somatic hypermutation patterns: the case of "towards analysis" in chronic lymphocytic leukaemia.

Background: Somatic Hypermutation (SHM) refers to the introduction of mutations within rearranged V(D)J genes, a process that increases the diversity of Immunoglobulins (IGs). The analysis of SHM has offered critical insight into the physiology and pathology of B cells, leading to strong prognostication markers for clinical outcome in chronic lymphocytic leukaemia (CLL), the most frequent adult B-cell malignancy. In this paper we present a methodology for integrating multiple immunogenetic and clinocobiological data sources in order to extract features and create high quality datasets for SHM analysis in IG receptors of CLL patients.

TRES: Identification of Discriminatory and Informative SNPs from Population Genomic Data.

The advent of high-throughput genomic technologies is enabling analyses on thousands or even millions of single-nucleotide polymorphisms (SNPs). At the same time, the selection of a minimum number of SNPs with the maximum information content is becoming increasingly problematic. Available locus ranking programs have been accused of providing upwardly biased results (concerning the predicted accuracy of the chosen set of markers for population assignment), cannot handle high-dimensional datasets, and some of them are computationally intensive.

Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia.

Νext generation sequencing studies in Homo sapiens have identified novel immunoglobulin heavy variable (IGHV) genes and alleles necessitating changes in the international ImMunoGeneTics information system (IMGT) GENE-DB and reference directories of IMGT/V-QUEST. In chronic lymphocytic leukaemia (CLL), the somatic hypermutation (SHM) status of the clonotypic rearranged IGHV gene is strongly associated with patient outcome. Correct determination of this parameter strictly depends on the comparison of the nucleotide sequence of the clonotypic rearranged IGHV gene with that of the closest germline counterpart.

Polyadenylation site prediction using PolyA-iEP method.

This chapter presents a method called PolyA-iEP that has been developed for the prediction of polyadenylation sites. More precisely, PolyA-iEP is a method that recognizes mRNA 3'ends which contain polyadenylation sites. It is a modular system which consists of two main components.

Pattern discovery for microsatellite genome analysis.

Microsatellite loci comprise an important part of eukaryotic genomes. Their applications in biology as genetic markers are related to numerous fields ranging from paternity analyses to construction of genetic maps and linkage to human disease. Existing software solutions which offer pattern discovery algorithms for the correct identification and downstream analysis of microsatellites are scarce and are proving to be inefficient to analyze large, exponentially increasing, sequenced genomes.

StackTIS: a stacked generalization approach for effective prediction of translation initiation sites.

The prediction of the translation initiation site in an mRNA or cDNA sequence is an essential step in gene prediction and an open research problem in bioinformatics. Although recent approaches perform well, more effective and reliable methodologies are solicited. We developed an adaptable data mining method, called StackTIS, which is modular and consists of three prediction components that are combined into a meta-classification system, using stacked generalization, in a highly effective framework.

MANTIS: a data mining methodology for effective translation initiation site prediction.

The prediction of the translation initiation site in a genomic sequence with the highest possible accuracy is an important problem that still has to be investigated by the research community. Current approaches perform quite well, however there is still room for a more general framework for the researchers who want to follow an effective and reliable methodology. We developed a prediction methodology that combines ad hoc as well as discovered knowledge in order to significantly increase the achieved accuracy reliably.

A novel approach for incremental uncertainty rule generation from databases with missing values handling: application to dynamic medical databases.

Current approaches for mining association rules usually assume that the mining is performed in a static database, where the problem of missing attribute values does not practically exist. However, these assumptions are not preserved in some medical databases, like in a home care system. In this paper, a novel uncertainty rule algorithm is illustrated, namely URG-2 (Uncertainty Rule Generator), which addresses the problem of mining dynamic databases containing missing values.