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Objectives: To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook., Methods: We performed a broad literature search to retrieve Bioinformatics and Translational Informatics (BTI) papers and coupled this with a series of editorial and peer reviews to identity the top papers in the area., Results: We identified a final candidate list of 15 BTI papers for peer-review; from these candidates, the top three papers were chosen to highlight in this synopsis. These papers expand the integration of multi-omics data with electronic health records and use advanced machine learning approaches to tailor models to individual patients. In addition, our honorable mention paper foreshadows the growing impact of BTI research on precision medicine through the continued development of large clinical consortia., Conclusion: In the top BTI papers this year, we observed several important trends, including the use of deep-learning approaches to analyse diverse data types, the development of integrative and web-accessible bioinformatics pipelines, and a continued focus on the power of individual genome sequencing for precision health., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Analyses of the molecular foundations of phenotypic plasticity are challenging, however, especially in the case of complex social phenotypes. Here we apply a machine learning approach to tackle this challenge by analyzing individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen. We find that caste-associated gene expression profiles respond strongly to queen loss, and that this change is partly explained by attributes such as age but occurs even in individuals that appear phenotypically unaffected. These results demonstrate that large changes in gene expression may occur in the absence of outwardly detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic level but not at the levels of ovarian development or behavior.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions., (© The Author(s) 2020. Published by Oxford University Press.)

Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.

Language models are playing an increasingly important role in many areas of artificial intelligence (AI) and computational biology. In this primer, we discuss the ways in which language models, both those based on natural language and those based on biological sequences, can be applied to biological research. This primer is primarily intended for biologists interested in using these cutting-edge AI technologies in their applications. We provide guidance on best practices and key resources for adapting language models for biology., (© 2024. Springer Nature America, Inc.)

Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers., (© 2024. Springer Nature America, Inc.)

Motivation: To analyze the relative proportion of bioinformatics papers and their non-bioinformatics counterparts in the top 20 most cited papers annually for the past two decades., Results: When defining bioinformatics papers as encompassing both those that provide software for data analysis or methods underlying data analysis software, we find that over the past two decades, more than a third (34%) of the most cited papers in science were bioinformatics papers, which is approximately a 31-fold enrichment relative to the total number of bioinformatics papers published. More than half of the most cited papers during this span were bioinformatics papers. Yet, the average 5-year JIF of top 20 bioinformatics papers was 7.7, whereas the average JIF for top 20 non-bioinformatics papers was 25.8, significantly higher (P < 4.5 × 10(-29)). The 20-year trend in the average JIF between the two groups suggests the gap does not appear to be significantly narrowing. For a sampling of the journals producing top papers, bioinformatics journals tended to have higher Gini coefficients, suggesting that development of novel bioinformatics resources may be somewhat 'hit or miss'. That is, relative to other fields, bioinformatics produces some programs that are extremely widely adopted and cited, yet there are fewer of intermediate success., Contact: jdwren@gmail.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Gene function curation of the literature with Gene Ontology (GO) concepts is one particularly time-consuming task in genomics, and the help from bioinformatics is highly requested to keep up with the flow of publications. In 2004, the first BioCreative challenge already designed a task of automatic GO concepts assignment from a full text. At this time, results were judged far from reaching the performances required by real curation workflows. In particular, supervised approaches produced the most disappointing results because of lack of training data. Ten years later, the available curation data have massively grown. In 2013, the BioCreative IV GO task revisited the automatic GO assignment task. For this issue, we investigated the power of our supervised classifier, GOCat. GOCat computes similarities between an input text and already curated instances contained in a knowledge base to infer GO concepts. The subtask A consisted in selecting GO evidence sentences for a relevant gene in a full text. For this, we designed a state-of-the-art supervised statistical approach, using a naïve Bayes classifier and the official training set, and obtained fair results. The subtask B consisted in predicting GO concepts from the previous output. For this, we applied GOCat and reached leading results, up to 65% for hierarchical recall in the top 20 outputted concepts. Contrary to previous competitions, machine learning has this time outperformed standard dictionary-based approaches. Thanks to BioCreative IV, we were able to design a complete workflow for curation: given a gene name and a full text, this system is able to select evidence sentences for curation and to deliver highly relevant GO concepts. Contrary to previous competitions, machine learning this time outperformed dictionary-based systems. Observed performances are sufficient for being used in a real semiautomatic curation workflow. GOCat is available at http://eagl.unige.ch/GOCat/., Database Url: http://eagl.unige.ch/GOCat4FT/., (© The Author(s) 2014. Published by Oxford University Press.)

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics., (© 2023 Wiley-VCH GmbH.)

The African BioGenome Project (AfricaBP) Open Institute for Genomics and Bioinformatics aims to overcome barriers to capacity building through its distributed African regional workshops and prioritizes the exchange of grassroots knowledge and innovation in biodiversity genomics and bioinformatics. In 2023, we implemented 28 workshops on biodiversity genomics and bioinformatics, covering 11 African countries across the 5 African geographical regions. These regional workshops trained 408 African scientists in hands-on molecular biology, genomics and bioinformatics techniques as well as the ethical, legal and social issues associated with acquiring genetic resources. Here, we discuss the implementation of transformative strategies, such as expanding the regional workshop model of AfricaBP to involve multiple countries, institutions and partners, including the proposed creation of an African digital database with sequence information relating to both biodiversity and agriculture. This will ultimately help create a critical mass of skilled genomics and bioinformatics scientists across Africa., (© 2024. Springer Nature America, Inc.)

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Language Models (PLMs)-ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters), TooT-PLM-ionCT employs a combination of traditional classifiers and deep learning models for nuanced protein classification. Originally validated on an existing dataset by previous researchers, our systems demonstrated superior performance in identifying ITs from MPs and distinguishing ICs from ITs, with the IC-MP discrimination achieving state-of-the-art results. In light of recommendations for additional validation, we introduced a new dataset, significantly enhancing the robustness and generalization of our models across bioinformatics challenges. This new evaluation underscored the effectiveness of TooT-PLM-ionCT in adapting to novel data while maintaining high classification accuracy. Furthermore, this study explores critical factors affecting classification accuracy, such as dataset balancing, the impact of using frozen versus fine-tuned PLM representations, and the variance between half and full precision in floating-point computations. To facilitate broader application and accessibility, a web server (https://tootsuite.encs.concordia.ca/service/TooT-PLM-ionCT) has been developed, allowing users to evaluate unknown protein sequences through our specialized systems for IC-MP, IT-MP, and IC-IT classification tasks., (© 2024 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.)

Background: Global estimates for cholera annually approximate 4 million cases worldwide with 95,000 deaths. Recent outbreaks, including Haiti and Yemen, are reminders that cholera is still a global health concern. Cholera outbreaks can rapidly induce high death tolls by overwhelming the capacity of health facilities, especially in remote areas or areas of civil unrest. Recent studies demonstrated that stool specimens preserved on filter paper facilitate molecular analysis of Vibrio cholerae in resource limited settings. Specimens preserved in a rapid, low-cost, safe and sustainable manner for sequencing provides previously unavailable data about circulating cholera strains. This may ultimately provide new information to shape public policy response on cholera control and elimination. Methodology/Principal findings: Whole genome sequencing (WGS) recovered close to a complete sequence of the V. cholerae O1 genome with satisfactory genome coverage from stool specimens enriched in alkaline peptone water (APW) and V. cholerae culture isolates, both spotted on filter paper. The minimum concentration of V. cholerae DNA sufficient to produce quality genomic information was 0.02 ng/μL. The genomic data confirmed the presence or absence of genes of epidemiological interest, including cholera toxin and pilus loci. WGS identified a variety of diarrheal pathogens from APW-enriched specimen spotted filter paper, highlighting the potential for this technique to explore the gut microbiome, potentially identifying co-infections, which may impact the severity of disease. WGS demonstrated that these specimens fit within the current global cholera phylogenetic tree, identifying the strains as the 7th pandemic El Tor. Conclusions: WGS results allowed for mapping of short reads from APW-enriched specimen and culture isolate spotted filter papers this provided valuable molecular epidemiological sequence information on V. cholerae strains from remote, low-resource settings. These results identified the presence of co-infecting pathogens while providing rare insight into the specific V. cholerae strains causing outbreaks in cholera-endemic areas. [ABSTRACT FROM AUTHOR]

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Dr. Rocío Trueba‐Gómez and her team from Mexico City, Mexico, have been awarded the Vox Sanguinis Best Paper Prize 2023 for their paper titled "Use of computational biology to compare the theoretical tertiary structures of the most common forms of RhCE and RhD." This prestigious prize is given annually for the best original paper published in Vox Sanguinis the previous year and includes a certificate and a cash prize of 5000 Euros. The authors will receive the award during the opening ceremony of the 38th International Congress of the International Society of Blood Transfusion in Barcelona. Congratulations to the authors on their achievement. [Extracted from the article]

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs., (© 2022. Springer Nature America, Inc.)