Molecular Biology Literature Research Articles

Artificial intelligence applied to musculoskeletal oncology: a systematic review.

Developments in artificial intelligence have the potential to improve the care of patients with musculoskeletal tumors. We performed a systematic review of the published scientific literature to identify the current state of the art of artificial intelligence applied to musculoskeletal oncology, including both primary and metastatic tumors, and across the radiology, nuclear medicine, pathology, clinical research, and molecular biology literature. Through this search, we identified 252 primary research articles, of which 58 used deep learning and 194 used other machine learning techniques. Articles involving deep learning have mostly involved bone scintigraphy, histopathology, and radiologic imaging. Articles involving other machine learning techniques have mostly involved transcriptomic analyses, radiomics, and clinical outcome prediction models using medical records. These articles predominantly present proof-of-concept work, other than the automated bone scan index for bone metastasis quantification, which has translated to clinical workflows in some regions. We systematically review and discuss this literature, highlight opportunities for multidisciplinary collaboration, and identify potentially clinically useful topics with a relative paucity of research attention. Musculoskeletal oncology is an inherently multidisciplinary field, and future research will need to integrate and synthesize noisy siloed data from across clinical, imaging, and molecular datasets. Building the data infrastructure for collaboration will help to accelerate progress towards making artificial intelligence truly useful in musculoskeletal oncology.

A comprehensive overview on the molecular biology of human glioma: what the clinician needs to know.

The molecular biology of human glioma is a complex and fast-growing field in which basic research needs to meet clinical expectations in terms of anti-tumor efficacy. Although much effort is being done in molecular biology research, significant contribution to the quality of life and overall survival still lacks. The vastness of molecular biology literature makes it virtually impossible for clinicians to keep up to date in the field. This paper reviews some practical concepts regarding glioma tumorigenesis from the clinician's perspective. Five main aspects are discussed: major intracellular signaling pathways involved in glioma formation; genomic, epigenetic and transcriptomic relevant features of glioma; the prognostic and predictive values of molecular markers according to the new WHO classification of glial tumors; the importance of molecular and cellular heterogeneity in glioblastoma, responsible for its therapy resistance; and the interaction between glioma and the immune system, in view of the novel and promising targeted therapies.

Special Issue on the Curative Power of Medical Data

With the massive amounts of medical data made available online, language technologies have proven to be indispensable in processing biomedical and molecular biology literature, health data or patient records. With huge amount of reports, evaluating their impact has long ceased to be a trivial task. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and the discovery of structured clinical information and could foster a major leap in natural language processing and in health research. The aim of this Special Issue, “Curative Power of Medical Data” in Data, is to gather innovative approaches for the exploitation of biomedical data using semantic web technologies and linked data by developing a community involvement in biomedical research. This Special Issue contains four surveys, which include a wide range of topics, from the analysis of biomedical articles writing style, to automatically generating tests from medical references, constructing a Gold standard biomedical corpus or the visualization of biomedical data.

A Molecular View of Plant Local Adaptation: Incorporating Stress-Response Networks.

Ecological specialization in plants occurs primarily through local adaptation to different environments. Local adaptation is widely thought to result in costly fitness trade-offs that result in maladaptation to alternative environments. However, recent studies suggest that such trade-offs are not universal. Further, there is currently a limited understanding of the molecular mechanisms responsible for fitness trade-offs associated with adaptation. Here, we review the literature on stress responses in plants to identify potential mechanisms underlying local adaptation and ecological specialization. We focus on drought, high and low temperature, flooding, herbivore, and pathogen stresses. We then synthesize our findings with recent advances in the local adaptation and plant molecular biology literature. In the process, we identify mechanisms that could cause fitness trade-offs and outline scenarios where trade-offs are not a necessary consequence of adaptation. Future studies should aim to explicitly integrate molecular mechanisms into studies of local adaptation.

Towards Identifying Author Confidence in Biomedical Articles

In an era where the volume of medical literature is increasing daily, researchers in the biomedical and clinical areas have joined efforts with language engineers to analyze the large amount of biomedical and molecular biology literature (such as PubMed), patient data, or health records. With such a huge amount of reports, evaluating their impact has long stopped being a trivial task. In this context, this paper intended to introduce a non-scientific factor that represents an important element in gaining acceptance of claims. We postulated that the confidence that an author has in expressing their work plays an important role in shaping the first impression that influences the reader’s perception of the paper. The results discussed in this paper were based on a series of experiments that were ran using data from the open archives initiative (OAI) corpus, which provides interoperability standards to facilitate effective dissemination of the content. This method may be useful to the direct beneficiaries (i.e., authors, who are engaged in medical or academic research), but also, to the researchers in the fields of biomedical text mining (BioNLP) and NLP, etc.

Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

Improving protein coreference resolution by simple semantic classification

BackgroundCurrent research has shown that major difficulties in event extraction for the biomedical domain are traceable to coreference. Therefore, coreference resolution is believed to be useful for improving event extraction. To address coreference resolution in molecular biology literature, the Protein Coreference (COREF) task was arranged in the BioNLP Shared Task (BioNLP-ST, hereafter) 2011, as a supporting task. However, the shared task results indicated that transferring coreference resolution methods developed for other domains to the biological domain was not a straight-forward task, due to the domain differences in the coreference phenomena.ResultsWe analyzed the contribution of domain-specific information, including the information that indicates the protein type, in a rule-based protein coreference resolution system. In particular, the domain-specific information is encoded into semantic classification modules for which the output is used in different components of the coreference resolution. We compared our system with the top four systems in the BioNLP-ST 2011; surprisingly, we found that the minimal configuration had outperformed the best system in the BioNLP-ST 2011. Analysis of the experimental results revealed that semantic classification, using protein information, has contributed to an increase in performance by 2.3% on the test data, and 4.0% on the development data, in F-score.ConclusionsThe use of domain-specific information in semantic classification is important for effective coreference resolution. Since it is difficult to transfer domain-specific information across different domains, we need to continue seek for methods to utilize such information in coreference resolution.

Biological event composition.

BackgroundIn recent years, biological event extraction has emerged as a key natural language processing task, aiming to address the information overload problem in accessing the molecular biology literature. The BioNLP shared task competitions have contributed to this recent interest considerably. The first competition (BioNLP'09) focused on extracting biological events from Medline abstracts from a narrow domain, while the theme of the latest competition (BioNLP-ST'11) was generalization and a wider range of text types, event types, and subject domains were considered. We view event extraction as a building block in larger discourse interpretation and propose a two-phase, linguistically-grounded, rule-based methodology. In the first phase, a general, underspecified semantic interpretation is composed from syntactic dependency relations in a bottom-up manner. The notion of embedding underpins this phase and it is informed by a trigger dictionary and argument identification rules. Coreference resolution is also performed at this step, allowing extraction of inter-sentential relations. The second phase is concerned with constraining the resulting semantic interpretation by shared task specifications. We evaluated our general methodology on core biological event extraction and speculation/negation tasks in three main tracks of BioNLP-ST'11 (GENIA, EPI, and ID).ResultsWe achieved competitive results in GENIA and ID tracks, while our results in the EPI track leave room for improvement. One notable feature of our system is that its performance across abstracts and articles bodies is stable. Coreference resolution results in minor improvement in system performance. Due to our interest in discourse-level elements, such as speculation/negation and coreference, we provide a more detailed analysis of our system performance in these subtasks.ConclusionsThe results demonstrate the viability of a robust, linguistically-oriented methodology, which clearly distinguishes general semantic interpretation from shared task specific aspects, for biological event extraction. Our error analysis pinpoints some shortcomings, which we plan to address in future work within our incremental system development methodology.

Extracting Genetic Pathways From Text and Grounding at the Spatio-Temporal Level

The molecular biology literature is believed to contain a wealth of information that has not yet made it into any structured database. Of particular current interest is information about genetic pathways, and there are many ongoing studies into automatically extracting such pathways from the molecular biology literature. In the area of development, however, pathways must be linked to changes in the developing tissues, usually described in terms of cellular processes and where they are happening. Thus our aim is to extract complementary information regarding the tissue location in which pathways are active, along with the biological process they are active in and the stage of embryonic development in which the process occurs.

Evaluation of Genetic Enhancement: Will Human Wisdom Properly Acknowledge the Value of Evolution?

Evaluation of Genetic Enhancement: Will Human Wisdom Properly Acknowledge the Value of Evolution?

Retroposon mapping in molecular systematics.

Advances in genome sciences are demonstrating the dynamic nature of noncoding DNA regions, which are comprised largely of repetitive elements with no apparent function. Retroposons are one class of mobile genetic elements that amplify and move about the genome via a copy-and-paste mechanism that employs an RNA intermediate. Short and long interspersed elements (SINEs and LINEs, respectively) are types of retroposons of particular interest because of their active role in shaping the architecture of genomes and their diagnostic value as evolutionary markers for studies of phylogeny and population biology. Although the use of SINEs and LINEs for molecular systematic studies is proliferating, a comprehensive laboratory protocol that explicitly outlines how to isolate and characterize retroposons for systematic studies in a detailed, step-by-step fashion has been lacking. The present chapter addresses this gap in the literature by focusing on the strategy for isolating new SINEs from a genomic library, the screening process, the sequencing and characterization of clones into subfamilies, quantification of copy number in host taxa, and the critical diagnosis of phylogenetically informative SINE and LINE insertion patterns. Practical limits to the method are discussed in relation to sampling design, systematic character theory, and the empirical distribution of elements observed in eukaryotic lineages. Major steps in the experimental process are illustrated with case examples from a diversity of taxonomic groups and by published results in the molecular biology and systematics literature.

Of truth and pathways: chasing bits of information through myriads of articles

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

The frame-based module of the SUISEKI information extraction system

SUISEKI, an information extraction system, uses morphological, syntactical, and contextual information to detect gene and protein names and interactions in scientific texts. This article describes the system's rules (called frames) used to detect and analyze interaction networks described in the molecular biology literature.

Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain

This article describes our work to identify and classify terms in the domain of molecular biology according to examples that have been marked up by a domain expert in a corpus of abstracts taken from a controlled search of the Medline database. Automatic acquisition of biomedical term lists has so far been slow due to high variability in both the terms and their classification scheme, which we attribute to the diversity of research disciplines involved. Nevertheless, the explosive growth in online molecular biology literature makes a persuasive case for automating many tasks. This includes acquisition of records for gene-product databases such as SwissProt which are currently updated by human experts, a task that is both time consuming and often highly idiosyncratic. In this article we report results from a tool based on a hidden-Markov model for extracting and classifying terms that can be used as a key component in an information extraction system. We discuss the results in light of lexical, syntactic and semantic properties of terms that were revealed by our study.

The virtual library. I. Searching MEDLINE.

Staying current with the molecular biology literature has eased appreciably over the last few years, as information resource producers provide more of their information via the World Wide Web (WWW). The ability to access these resources using one access point (WWW) and single piece of software (a Web browser), enables the researcher and clinician to easily stay current of the published literature and research in molecular biology and genetics. In addition, current developments now allow the researcher to quickly link across data resources to retrieve full articles and related numeric information on DNA and protein sequences.

The early introduction of dynamic programming into computational biology.

In 1994–1995, DIMACS sponsored a theme year on computational biology. Among the numerous seminars and workshops was one organized by Alberto Apostolico and Raffaele Giancarlo, recapitulated in their 1998 paper, on the history and motivations for sequence comparison. In my participation in this event, I was led to consider some of the early interactions, at the Centre de recherches mathematiques (CRM) and elsewhere, in the field now known as computational biology. A short time earlier, I had read a 1989 paper by Walter Goad on the impact of Stanislaw Ulam in this field. The penultimate sentence in this article was a quote from Ulam himself ‘I started all this’, which puzzled me greatly. As far as I knew, Ulam had no impact in the early development of the field, and while he gave talks on it at least from 1971 (cf. Ulam, 1972), the distance he defined was already published (Levenshtein, 1965) and the problem he proposed had already been solved, for all intents and purposes, in the molecular biology literature (Needleman and Wunsch, 1970) and elsewhere (Vintsyuk, 1968). I had also read a joint interview of Ulam and Mark Kac by Feigenbaum (1982), and this led me to reflect on this misperception on the part of Ulam (and of Goad), and to crystallize the realization that ironically, Kac, his colleague of many years, had played a crucial, if indirect, role in the earliest development of the field, especially that associated with the Centre de recherches mathematiques (CRM). Despite the fact that Kac had no personal research interest in the field, his encouragement of a number of junior mathematicians, and his role, intentional or not, in bringing researchers together, recur as important influences in several aspects of computational biology, and explain why I dedicate this article to his memory. In presenting some of these thoughts to the DIMACS workshop, focusing on the early 1970s, my understanding of this period was broadened by comments from a number of participants, particularly Jerrold Griggs and Pavel Pevzner, and clarified by the presentation immediately

Expected frequencies of DNA patterns using whittle's formula

Given a realisation of a Markov chain, one can count the numbers of state transitions of each type. One can ask how many realisations are there with these transition counts and the same initial state. Whittle (1955) has answered this question, by finding an explicit though complicated formula, and has also shown that each realisation is equally likely. In the analysis of DNA sequences which comprise letters from the set {A, C, G, T}, it is often useful to count the frequency of a pattern, say ACGCT, in a long sequence and compare this with the expected frequency for all sequences having the same start letter and the same transition counts (or ‘dinucleotide counts' as they are called in the molecular biology literature). To date, no exact method exists; this paper rectifies that deficiency.

Expected frequencies of DNA patterns using whittle's formula

Given a realisation of a Markov chain, one can count the numbers of state transitions of each type. One can ask how many realisations are there with these transition counts and the same initial state. Whittle (1955) has answered this question, by finding an explicit though complicated formula, and has also shown that each realisation is equally likely. In the analysis of DNA sequences which comprise letters from the set {A, C, G, T}, it is often useful to count the frequency of a pattern, say ACGCT, in a long sequence and compare this with the expected frequency for all sequences having the same start letter and the same transition counts (or ‘dinucleotide counts' as they are called in the molecular biology literature). To date, no exact method exists; this paper rectifies that deficiency.