KEBLM: Knowledge-Enhanced Biomedical Language Models.

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP.

Exploring collaborative caption editing to augment video-based learning.

Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively.

Estimating the influence of Twitter on pre-exposure prophylaxis use and HIV testing as a function of rates of men who have sex with men in the United States.

Objectives: Acceptance of pre-exposure prophylaxis (PrEP) and testing for HIV is likely to vary as a function of the norms and communications within a geographic area. This study examined associations involving county tweets, in person communications, and HIV prevention and testing in regions with higher (vs. lower) estimated rates of men who have sex with men (MSM).

Biosystems Design by Machine Learning.

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality.

Who is Saying What on Twitter: An Analysis of Messages with References to HIV and HIV Risk Behavior.

This research aimed to determine the nature of social media discussions about HIV. With the goal of conducting a descriptive analysis, we collected almost 1,000 tweets posted February to September 2015. The sample of tweets included keywords related to HIV or behavioral risk factors (e.

HIV messaging on Twitter: an analysis of current practice and data-driven recommendations.

Objectives: Social media messages have been increasingly used in health campaigns about prevention, testing, and treatment of HIV. We identified factors leading to the retransmission of messages from expert social media accounts to create data-driven recommendations for online HIV messaging.

Design And Methods: We sampled 20 201 HIV-related tweets (posted between 2010 and 2017) from 37 HIV experts.

Framing Electronic Medical Records as Polylingual Documents in Query Expansion.

We present a study of electronic medical record (EMR) retrieval that emulates situations in which a doctor treats a new patient. Given a query consisting of a new patient's symptoms, the retrieval system returns the set of most relevant records of previously treated patients. However, due to semantic, functional, and treatment synonyms in medical terminology, queries are often incomplete and thus require enhancement.

An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years.

The present study evaluated the potential use of Twitter data for providing risk indices of STIs. We developed online risk indices (ORIs) based on tweets to predict new HIV, gonorrhea, and chlamydia diagnoses, across U.S.

VisAGE: Integrating external knowledge into electronic medical record visualization.

In this paper, we present VisAGE, a method that visualizes electronic medical records (EMRs) in a low-dimensional space. Effective visualization of new patients allows doctors to view similar, previously treated patients and to identify the new patients' disease subtypes, reducing the chance of misdiagnosis. However, EMRs are typically incomplete or fragmented, resulting in patients who are missing many available features being placed near unrelated patients in the visualized space.

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.

Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"--it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis.

Exploiting ontology graph for predicting sparsely annotated gene function.

Motivation: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (<10) annotated genes, which constitute about half of the GO terms in yeast, mouse and human, pose a unique challenge in that any prediction algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this 'overfitting' issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog.

MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence.

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation.

Understanding user intents in online health forums.

Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents.

Integer Linear Programming for Constrained Multi-Aspect Committee Review Assignment.

Automatic review assignment can significantly improve the productivity of many people such as conference organizers, journal editors and grant administrators. A general setup of the review assignment problem involves assigning a set of reviewers on a committee to a set of documents to be reviewed under the constraint of review quota so that the reviewers assigned to a document can collectively cover multiple topic aspects of the document. No previous work has addressed such a setup of committee review assignments while also considering matching multiple aspects of topics and expertise.

Leveraging medical thesauri and physician feedback for improving medical literature retrieval for case queries.

Objective: This paper presents a study of methods for medical literature retrieval for case queries, in which the goal is to retrieve literature articles similar to a given patient case. In particular, it focuses on analyzing the performance of state-of-the-art general retrieval methods and improving them by the use of medical thesauri and physician feedback.

Materials And Methods: The Kullback-Leibler divergence retrieval model with Dirichlet smoothing is used as the state-of-the-art general retrieval method.

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature.

Discovery of gene network variability across samples representing multiple classes.

Gene networks have been predicted using the expression profiles from microarray experiments that include multiple samples representing each of several classes or states (e.g., treatments, developmental stages, health status).

BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects.

Text mining is one promising way of extracting information automatically from the vast biological literature. To maximize its potential, the knowledge encoded in the text should be translated to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare.

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model.

Background: Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO).

Inference of gene pathways using mixture Bayesian networks.

Background: Inference of gene networks typically relies on measurements across a wide range of conditions or treatments. Although one network structure is predicted, the relationship between genes could vary across conditions. A comprehensive approach to infer general and condition-dependent gene networks was evaluated.

Multi-label literature classification based on the Gene Ontology graph.

Background: The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification.

Automatically generating gene summaries from biomedical literature.

Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature.

Genome scan for cis-regulatory DNA motifs associated with social behavior in honey bees.

Honey bees (Apis mellifera) undergo an age-related, socially regulated transition from working in the hive to foraging, which is associated with changes in the expression of thousands of genes in the brain. To begin to study the cis-regulatory code underlying this massive social regulation of gene expression, we used the newly sequenced honey bee genome to scan the promoter regions of eight sets of behaviorally related genes differentially expressed in the brain in the context of division of labor among worker bees, for 41 cis-regulatory motifs previously characterized in Drosophila melanogaster. Binding sites for the transcription factors Hairy, GAGA, Adf1, Cf1, Snail, and Dri, known to function in nervous system development, olfactory learning, or hormone binding in Drosophila, were significantly associated with one or more gene sets.