Real-world time series data often require analysis or interpretation from domain experts. Some tasks, like time series question answering, involve both time series and natural language questions, posing challenges for single-modality language models to understand their interaction. To this end, we present TSLA (Time Series Language Model), a framework designed to enhance the language model with the understanding of time series data for multi-modality tasks. TSLA comprises three key components. (1) Time Series Tokenizer learns how to represent time series data into discrete tokens, making it more manageable for language models. (2) Joint (Pre-)Training on task-agnostic time series and text data integrates time series tokens and text tokens to model the interplay between time series and language concepts. (3) Multi-task Instruction Tuning fine-tunes the pretrained TSLA for various downstream tasks relevant to user interests. For evaluation, we applied TSLA to time series data from human motions on four tasks: time series captioning, time series question answering, text-based time series synthesis, and text-based time series continuation. The results demonstrate TSLA’s effectiveness in handling multiple time series analysis tasks, pointing the way for future research endeavors.

Graph metric learning methods aim to learn the distance metric over graphs such that similar (e.g., same class) graphs are closer and dissimilar (e.g., different class) graphs are farther apart. This is of critical importance in many graph classification applications such as drug discovery and epidemics categorization. Most, if not all, graph metric learning techniques consider the input graph as static, and largely ignore the intrinsic dynamics of temporal graphs. However, in practice, a graph typically has heterogeneous dynamics (e.g., microscopic and macroscopic evolution patterns). As such, labeling a temporal graph is usually expensive and also requires background knowledge. To learn a good metric over temporal graphs, we propose a temporal graph metric learning framework, Temp-GFSM. With only a few labeled temporal graphs, Temp-GFSM outputs a good metric that can accurately classify different temporal graphs and be adapted to discover new subspaces for unseen classes. Each proposed component in Temp-GFSM answers the following questions: What patterns are evolving in a temporal graph? How to weigh these patterns to represent the characteristics of different temporal classes? And how to learn the metric with the guidance from only a few labels? Finally, the experimental results on real-world temporal graph classification tasks from various domains show the effectiveness of our Temp-GFSM.

Publishing articles in high‐impact English journals is difficult for scholars around the world, especially for non‐native English‐speaking scholars (NNESs), most of whom struggle with proficiency in English. To uncover the differences in English scientific writing between native English‐speaking scholars (NESs) and NNESs, we collected a large‐scale data set containing more than 150,000 full‐text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two‐fold perspective of linguistic complexity: (a) syntactic complexity, including measurements of sentence length and sentence complexity; and (b) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.

Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation‐based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross‐validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes’ early poetry.

BACKGROUND: We recently described "Author-ity," a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. METHODS: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. RESULTS: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ~98.8%. Lumping (putting two different individuals into the same cluster) affects ~0.5% of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ~2% of articles. IMPACT: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. AVAILABILITY: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.

This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature. The effects of different types of domain-specific knowledge in performance contribution are examined. Based on the TREC platform, we show that appropriate use of domain-specific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.

BACKGROUND: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator. METHODOLOGY/PRINCIPAL FINDINGS: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as 'gold standards;' each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search. CONCLUSIONS/SIGNIFICANCE: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community. AVAILABILITY: Two-node searches can be carried out freely at http://arrowsmith.psych.uic.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Publication records are often found in the authors’ personal home pages. If such a record is partitioned into a list of semantic fields of authors, title, date, etc., the unstructured texts can be converted into structured data, which can be used in other applications. In this paper, we present PEPURS, a publication record segmentation system. It adopts a novel "Split and Merge" strategy. A publication record is split into segments; multiple statistical classifiers compute their likelihoods of belonging to different fields; finally adjacent segments are merged if they belong to the same field. PEPURS introduces the punctuation marks and their neighboring texts as a new feature to distinguish different roles of the marks. PEPURS yields high accuracy scores in experiments.

MOTIVATION: Abbreviations are an important type of terminology in the biomedical domain. Although several groups have already created databases of biomedical abbreviations, these are either not public, or are not comprehensive, or focus exclusively on acronym-type abbreviations. We have created another abbreviation database, ADAM, which covers commonly used abbreviations and their definitions (or long-forms) within MEDLINE titles and abstracts, including both acronym and non-acronym abbreviations. RESULTS: A model of recognizing abbreviations and their long-forms from titles and abstracts of MEDLINE (2006 baseline) was employed. After grouping morphological variants, 59 405 abbreviation/long-form pairs were identified. ADAM shows high precision (97.4%) and includes most of the frequently used abbreviations contained in the Unified Medical Language System (UMLS) Lexicon and the Stanford Abbreviation Database. Conversely, one-third of abbreviations in ADAM are novel insofar as they are not included in either database. About 19% of the novel abbreviations are non-acronym-type and these cover at least seven different types of short-form/long-form pairs. AVAILABILITY: A free, public query interface to ADAM is available at http://arrowsmith.psych.uic.edu, and the entire database can be downloaded as a text file.

Abstract Arrowsmith, a computer‐assisted process for literature‐based discovery, takes as input two disjoint sets of records (A, C) from the Medline database. It produces a list of title words and phrases, B, that are common to A and C, and displays the title context in which each B‐term occurs within A and within C. Subject experts then can try to find A–B and B–C title‐pairs that together may suggest novel and plausible indirect A–C relationships (via B‐terms) that are of particular interest in the absence of any known direct A–C relationship. The list of B‐terms typically is so large that it is difficult to find the relatively few that contribute to scientifically interesting connections. The purpose of the present article is to propose and test several techniques for improving the quality of the B‐list. These techniques exploit the Medical Subject Headings (MeSH) that are assigned to each input record. A MesH‐based concept of literature cohesiveness is defined and plays a key role. The proposed techniques are tested on a published example of indirect connections between migraine and magnesium deficiency. The tests demonstrate how the earlier results can be replicated with a more efficient and more systematic computer‐aided process.

Abstract We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service (“Author‐ity”: http://arrowsmith.psych.uic.edu ) that takes as input an author's name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.

BACKGROUND: MicroRNAs are approximately 17-24 nt. noncoding RNAs found in all eukaryotes that degrade messenger RNAs via RNA interference (if they bind in a perfect or near-perfect complementarity to the target mRNA), or arrest translation (if the binding is imperfect). Several microRNA targets have been identified in lower organisms, but only one mammalian microRNA target has yet been validated experimentally. RESULTS: We carried out a population-wide statistical analysis of how human microRNAs interact complementarily with human mRNAs, looking for characteristics that differ significantly as compared with scrambled control sequences. These characteristics were used to identify a set of 71 outlier mRNAs unlikely to have been hit by chance. Unlike the case in C. elegans and Drosophila, many human microRNAs exhibited long exact matches (10 or more bases in a row), up to and including perfect target complementarity. Human microRNAs hit outlier mRNAs within the protein coding region about 2/3 of the time. And, the stretches of perfect complementarity within microRNA hits onto outlier mRNAs were not biased near the 5'-end of the microRNA. In several cases, an individual microRNA hit multiple mRNAs that belonged to the same functional class. CONCLUSIONS: The analysis supports the notion that sequence complementarity is the basis by which microRNAs recognize their biological targets, but raises the possibility that human microRNA-mRNA target interactions follow different rules than have been previously characterized in Drosophila and C. elegans.

This paper addresses the problem of completely reconstructing deterministic monotone Boolean functions via membership queries. The minimum average query complexity is guaranteed via recursion, where partially ordered sets (posets) make up the overlapping subproblems. For problems with up to 4 variables, the posets' optimality conditions are summarized in the form of an evaluative criterion. The evaluative criterion extends then computational feasibility to problems involving up to about 20 variables. A frameworkfor unbiased average case comparison of monotone Boolean function inference algorithms is developed using unequal probability sampling. The unbiased empirical results show that an implementation of the subroutine considered as a standard in the literature performs almost twice as many queries as the evaluative criterion on the average. It should also be noted that the first algorithm ever designed for this problem performed consistently within two percentage points of the evaluative criterion. As such, it prevails, by far, as the most efficient of the many preexisting algorithms.