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PubMed2002

Patchwork peptide sequencing: extraction of sequence information from accurate mass data of peptide tandem mass spectra recorded at high resolution.

The accurate mass values of all immonium, y(1), y(2), a(2), and b(2) ions of tryptic peptides composed of the 20 standard amino acids were calculated. The differences between adjacent masses in this data set are greater than 10 mDa for more than 80% of the values. Using this mass list, the majority of low mass ions in quadrupole-time of flight tandem mass spectra of peptides from tryptic digests and from an elastase digest could be assigned. Besides the a(2)/b(2) ions, which carry residues 1-2 from the N-terminus, a variety of internal dipeptide b ions were regularly observed. In case internal proline was present, corresponding dipeptide b ions carrying proline at the N-terminal position occurred. By assigning the dipeptide b ions on the basis of their accurate mass, bidirectional or unidirectional sequence information was obtained, which is localized to the peptide N-terminus (a(2)/b(2) ions) or not localized (internal b ions). Identification of the y(1) and y(2) ions by their accurate mass provides unidirectional sequence information localized to the peptide C-terminus. It is shown that this patchwork-type sequence information extractable from accurate mass data of low-mass ions is highly efficient for protein identification.

Amino Acid Sequence↗

PubMed2003

MedScan, a natural language processing engine for MEDLINE abstracts.

MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

Abstracting and Indexing↗

PubMed1999

On decoding the responses of a population of neurons from short time windows.

The effectiveness of various stimulus identification (decoding) procedures for extracting the information carried by the responses of a population of neurons to a set of repeatedly presented stimuli is studied analytically, in the limit of short time windows. It is shown that in this limit, the entire information content of the responses can sometimes be decoded, and when this is not the case, the lost information is quantified. In particular, the mutual information extracted by taking into account only the most likely stimulus in each trial turns out to be, if not equal, much closer to the true value than that calculated from all the probabilities that each of the possible stimuli in the set was the actual one. The relation between the mutual information extracted by decoding and the percentage of correct stimulus decodings is also derived analytically in the same limit, showing that the metric content index can be estimated reliably from a few cells recorded from brief periods. Computer simulations as well as the activity of real neurons recorded in the primate hippocampus serve to confirm these results and illustrate the utility and limitations of the approach.

Algorithms↗

PubMed1999

Extraction of information to the left of the fixated word in reading.

The present experiment used 2 different eye-contingent display change techniques to determine whether information is extracted from English text even when it is to the left of the currently fixated word. Preview display changes were during the 1st saccade entering the target word region, whereas postview display changes were during the 1st saccade leaving that region. Previews and postviews were either identical, related, or unrelated to the target word. "Wrong" information in the target-word region affected reading even when that information was seen only after readers were fixating to the right of that region: When readers skipped the target word, such information caused readers to regress to the target word more; when readers initially fixated the target word, such information increased "2nd-pass" processing time on the target region. The data suggest that readers often still attend to a word after it is skipped and that when readers fixate a word, they occasionally attend to the word after they have begun to fixate the next word.

Fixation, Ocular↗

PubMed2005

PRIME: automatically extracted PRotein Interactions and Molecular Information databasE.

With the exponentially increasing amount of information in the biomedical field, the significance of advanced information retrieval and information extraction, as well as the role of databases, has been increasing. PRIME is an integrated gene/protein informatics database based on natural language processing. It provides automatically extracted protein/family/gene/compound interaction information including both physical and genetic interactions, gene ontology based functions, and graphic pathway viewers. Gene/protein/family names and functional terms are recognized based on dictionaries developed in our laboratory. The interaction and functional information are extracted by syntactic dependencies and various phrase patterns. We have included about 920,000 (non-redundant) protein interactions and 360,000 annotated gene-function relationships for major eukaryotes. By combining the sequence and text information, the pathway comparison between two organisms and simple pathway deduction based on other organism interaction data, and pathway filtering using tissue expression data, are also available. This database is accessible at http://prime.ontology.ims.u-tokyo.ac.jp:8081.

Abstracting and Indexing↗

PubMed2025

Large Language Model and Knowledge Graph-Driven AJCC Staging of Prostate Cancer Using Pathology Reports.

Background/Objectives: To develop an automated American Joint Committee on Cancer (AJCC) staging system for radical prostatectomy pathology reports using large language model-based information extraction and knowledge graph validation. Methods: Pathology reports from 152 radical prostatectomy patients were used. Five additional parameters (Prostate-specific antigen (PSA) level, metastasis stage (M-stage), extraprostatic extension, seminal vesicle invasion, and perineural invasion) were extracted using GPT-4.1 with zero-shot prompting. A knowledge graph was constructed to model pathological relationships and implement rule-based AJCC staging with consistency validation. Information extraction performance was evaluated using a local open-source large language model (LLM) (Mistral-Small-3.2-24B-Instruct) across 16 parameters. The LLM-extracted information was integrated into the knowledge graph for automated AJCC staging classification and data consistency validation. The developed system was further validated using pathology reports from 88 radical prostatectomy patients in The Cancer Genome Atlas (TCGA) dataset. Results: Information extraction achieved an accuracy of 0.973 and an F1-score of 0.986 on the internal dataset, and 0.938 and 0.968, respectively, on external validation. AJCC staging classification showed macro-averaged F1-scores of 0.930 and 0.833 for the internal and external datasets, respectively. Knowledge graph-based validation detected data inconsistencies in 5 of 150 cases (3.3%). Conclusions: This study demonstrates the feasibility of automated AJCC staging through the integration of large language model information extraction and knowledge graph-based validation. The resulting system enables privacy-protected clinical decision support for cancer staging applications with extensibility to broader oncologic domains.

artificial intelligence↗

PubMed2000

Automated extraction of information in molecular biology.

We review data mining techniques in molecular biology, specifically those that extract information from the scientific literature itself. As more of the biological literature is published electronically, there is an opportunity, and even a need, to automatically summarize the literature in a customized way, for example by associating keywords to a topic. These keywords can be extracted from relevant publications. The process of keyword extraction can be automated and optimized to keep literature pointers automatically up-to-date or to filter relevant information from the literature. To illustrate these points, OMIM (Online Mendelian Inheritance in Man), a database of human inherited diseases, was linked to the literature and keywords were derived that covered distinct aspects such as genetic information on the one hand and disease-specific protein and phenotypic information on the other. They were used to extract information that is helpful for keeping entries about disease up-to-date.

Databases, Factual↗

PubMed2006

An automated procedure for the extraction of metabolic network information from time series data.

Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm). The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.

Algorithms↗

PubMed2005

Comparative experiments on learning information extractors for proteins and their interactions.

OBJECTIVE: Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. METHODS AND MATERIAL: We used a variety of machine learning methods to automatically develop information extraction systems for extracting information on gene/protein name, function and interactions from Medline abstracts. We present cross-validated results on identifying human proteins and their interactions by training and testing on a set of approximately 1000 manually-annotated Medline abstracts that discuss human genes/proteins. RESULTS: We demonstrate that machine learning approaches using support vector machines and maximum entropy are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules. CONCLUSION: Our results show that it is promising to use machine learning to automatically build systems for extracting information from biomedical text. The results also give a broad picture of the relative strengths of a wide variety of methods when tested on a reasonably large human-annotated corpus.

Algorithms↗

PubMed2006

Automatic pathway building in biological association networks.

BACKGROUND: Scientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined. RESULTS: We describe the methodology for automatic curation of Biological Association Networks (BANs) derived by a natural language processing technology called Medscan. The curated data is used for automatic pathway reconstruction. The algorithm for the reconstruction of signaling pathways is also described and validated by comparison with manually curated pathways and tissue-specific gene expression profiles. CONCLUSION: Biological Association Networks extracted by MedScan technology contain sufficient information for constructing thousands of mammalian signaling pathways for multiple tissues. The automatically curated MedScan data is adequate for automatic generation of good quality signaling networks. The automatically generated Regulome pathways and manually curated pathways used for their validation are available free in the ResNetCore database from Ariadne Genomics, Inc. 1. The pathways can be viewed and analyzed through the use of a free demo version of PathwayStudio software. The Medscan technology is also available for evaluation using the free demo version of PathwayStudio software.

Databases, Bibliographic↗

PubMed2005

Text-mining approaches in molecular biology and biomedicine.

Biomedical articles provide functional descriptions of bioentities such as chemical compounds and proteins. To extract relevant information using automatic techniques, text-mining and information-extraction approaches have been developed. These technologies have a key role in integrating biomedical information through analysis of scientific literature. In this article, important applications such as the identification of biologically relevant entities in free text and the construction of literature-based networks of protein-protein interactions will be introduced. Also, the use of text mining to aid the interpretation of microarray data and the analysis of pathology reports will be discussed. Finally, we will consider the recent evolution of this field and the efforts for community-based evaluations.

Biomedical Research↗

PubMed2001

Automatic extraction of acronym-meaning pairs from MEDLINE databases.

Acronyms are widely used in biomedical and other technical texts. Understanding their meaning constitutes an important problem in the automatic extraction and mining of information from text. Here we present a system called ACROMED that is part of a set of Information Extraction tools designed for processing and extracting information from abstracts in the Medline database. In this paper, we present the results of two strategies for finding the long forms for acronyms in biomedical texts. These strategies differ from previous automated acronym extraction methods by being tuned to the complex phrase structures of the biomedical lexicon and by incorporating shallow parsing of the text into the acronym recognition algorithm. The performance of our system was tested with several data sets obtaining a performance of 72 % recall with 97 % precision. These results are found to be better for biomedical texts than the performance of other acronym extraction systems designed for unrestricted text.

Abbreviations as Topic↗

PubMed1990

Multimicroprocessor system for online monitoring in a CCU.

A real-time monitoring system for physiological signals, developed for patients in coronary care units (CCUs), is described. This system monitors the signals that have the greatest clinical value in a CCU environment (ECG and cardiovascular pressures), taking charge of detecting dangerous situations and of extracting information significant to the correct monitoring of the patient. The information it extracts, mainly from the ECG, is presented to the user in an ergonomic way using written reports and graphs which collect and compile the information, facilitating its interpretation. Some utilities have been developed to allow the user to modify certain monitoring conditions as well as to correct results derived from them, thereby improving the reliability of the monitoring process. The system uses a multimicroprocessor architecture (imposed by the need to perform a large number of tasks in real time) with a block based on the VME bus, charged with acquiring and processing the monitored signals, and an IBM-compatible PC/XT which is used as a system user interface and a massive storage device in which the information resulting from the monitoring of signals is stored.

Coronary Care Units↗

PubMed2004

Single molecule kinetics. II. Numerical Bayesian approach.

As discussed in the companion paper [J. B. Witkoskie and J. S. Cao, J. Chem. Phys. 121, 6361 (2004), preceding paper], quantitative extraction of information from single molecule experiments by several proposed indicators is difficult since the experiments only observe certain characteristics of the system, even though the indicators can contain all available information. This paper shows how one can circumvent the shortcomings of these indicators by combining information extracted from indicators with a numerical Bayesian statistical approach. The Bayesian approach determines the relative probability of various models reproducing the entire sequence of the single molecules trajectory, instead of binning and averaging over the data, which removes much of this information.

Journal Article↗

PubMed2006

Do avian brood parasites eavesdrop on heterospecific sexual signals revealing host quality? A review of the evidence.

Eavesdropping can be defined as the extraction of information from the interactions between other individuals. It provides a relatively cheap way of gathering relevant information for fitness enhancement. Here, we propose that obligate avian brood parasites, which always lay their eggs in foreign nests of individuals of other species, may eavesdrop on their host sexual signals to locate nests of high quality individuals in which to lay their parasitic eggs. Sexual signal variation can honestly signal parental quality. Thus, by eavesdropping on sexual signals, parasites may select high quality foster parents for their own offspring. Such a use of sexual signals within host populations by brood parasites differs from signal exploitation theory that proposes that parasite only use signals to locate potential host independently from signaller quality. Here, we review the avian literature concerning host choice within a host species by obligate avian brood parasites and find evidence for host selection within individuals of a host species on the basis of cues potentially functioning as sexually selected traits, or at least revealing parental abilities. We have also found support for the existence of benefits linked to host selection by avian brood parasites. Finally, one study reported on the attenuation of a sexual ornament in host populations under strong pressure by brood parasites. Most of these findings have been interpreted as evidence for host selection by avian brood parasites based on the conspicuousness of sexual signals. We suggest, however, that these findings may in fact reveal eavesdropping on host signalling performance by brood parasites which would use the information extracted to choose the better individuals among conspecifics of a given host. This provides a new perspective for the study of host selection in obligate brood parasites, and raises interesting questions for the study of animal cognition that would deserve experimental studies.

Animals↗

PubMed2005

A graphic tool for curating molecular interaction networks from the literature.

We propose a graphic tool for curating molecular interaction networks constructed from the literature by information extraction (IE). In order to turn preliminary results from IE into useful biomedical resources, we propose to use a controlled environment in which visualization and IE work synergistically. The usability of the proposed graphic tool is shown with respect to the identification of incorrectly extracted results that are due to the much troubling coordination phenomena in natural language texts. Through the experiment on molecular interactions in Saccaharomyces cerevisiae, we have seen a meaningful increase (from 91.5% to 97.5%) in the number of correctly extracted interaction information.

Computational Biology↗

PubMed2005

3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding.

This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.

Animals↗

PubMed1998

The effectiveness of acupuncture in treating acute dental pain: a systematic review.

OBJECTIVE: Acupuncture is frequently advocated as an effective treatment of dental pain. The question whether or not it is effective for this indication remains controversial. The aim of this systematic review therefore was to assess the effectiveness of acupuncture in dental pain. DATA SOURCES: Four electronic databases were searched: Medline, Embase, CISCOM, and the Cochrane Library. Only controlled trials were included in this review. DATA EXTRACTION: Information was extracted from included studies and entered on standard forms independently by both authors. Methodological quality was assessed using the Jadad score. MAIN RESULTS: 16 such studies were located. The majority of these trials imply that acupuncture is effective in dental analgesia. However, important questions remain unanswered. CONCLUSION: It is concluded that acupuncture can alleviate dental pain and that future investigations should define the optimal acupuncture technique and its relative efficacy compared with conventional methods of analgesia.

Acupuncture Analgesia↗