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Abstract Background Biomedical Relation Extraction (RE) is essential for uncovering complex relationships between biomedical entities within text. However, training RE classifiers is challenging in low-resource biomedical applications with few labeled examples. Methods We explore the potential of Shortest Dependency Paths (SDPs) to aid biomedical RE, especially in situations with limited labeled examples. In this study, we suggest various approaches to employ SDPs when creating word and sentence representations under supervised, semi-supervised, and in-context-learning settings. Results Through experiments on three benchmark biomedical text datasets, we find that incorporating SDP-based representations enhances the performance of RE classifiers. The improvement is especially notable when working with small amounts of labeled data. Conclusion SDPs offer valuable insights into the complex sentence structure found in many biomedical text passages. Our study introduces several straightforward techniques that, as demonstrated experimentally, effectively enhance the accuracy of RE classifiers.

Identification of procedures using International Classification of Diseases or Healthcare Common Procedure Coding System codes is challenging when conducting medical claims research. We demonstrate how Pointwise Mutual Information can be used to find associated codes. We apply the method to an investigation of racial differences in breast cancer outcomes. We used Surveillance Epidemiology and End Results (SEER) data linked to Medicare claims. We identified treatment using two methods. First, we used previously published definitions. Second, we augmented definitions using codes empirically identified by the Pointwise Mutual Information statistic. Similar to previous findings, we found that presentation differences between Black and White women closed much of the estimated survival curve gap. However, we found that survival disparities were completely eliminated with the augmented treatment definitions. We were able to control for a wider range of treatment patterns that might affect survival differences between Black and White women with breast cancer.

With the rise of large-scale data-driven innovation in AI, data annotation tasks found in digital work environments present an employment opportunity for neurodivergent individuals. Though work in data annotation can potentially ease the high unemployment rate of neurodivergent individuals, limited research focuses on the experience of neurodivergent workers in data annotation micro-tasks. To aid in understanding the experience of neurodivergent crowd workers, we conducted a user study with ten neurodivergent workers between the ages of 18–30. Participants completed three types of micro-tasks in a custom web-based data annotation platform. With the data collected from the platform, we examined individual responses within data annotations, work completion, the time to complete work, and calculated a potential “effective hourly wage,” for each participant based on their responses. Through a survey and semi-structured interview following each task, we learned about the experience of all participants regarding each of the data annotation tasks. Results of the study show: 1) our participants provide diverse annotations that are valuable for employers in digital data annotation work environments; 2) when calculating the “effective hourly wage” of all participants per task, some of our participants would earn less than minimum hourly wage on tasks; and 3) participant perceptions of the tasks matched their responses in the tasks presented.

Abstract Background Health providers create Electronic Health Records (EHRs) to describe the conditions and procedures used to treat their patients. Medical notes entered by medical staff in the form of free text are a particularly insightful component of EHRs. There is a great interest in applying machine learning tools on medical notes in numerous medical informatics applications. Learning vector representations, or embeddings, of terms in the notes, is an important pre-processing step in such applications. However, learning good embeddings is challenging because medical notes are rich in specialized terminology, and the number of available EHRs in practical applications is often very small. Methods In this paper, we propose a novel algorithm to learn embeddings of medical terms from a limited set of medical notes. The algorithm, called definition2vec, exploits external information in the form of medical term definitions. It is an extension of a skip-gram algorithm that incorporates textual definitions of medical terms provided by the Unified Medical Language System (UMLS) Metathesaurus. Results To evaluate the proposed approach, we used a publicly available Medical Information Mart for Intensive Care (MIMIC-III) EHR data set. We performed quantitative and qualitative experiments to measure the usefulness of the learned embeddings. The experimental results show that definition2vec keeps the semantically similar medical terms together in the embedding vector space even when they are rare or unobserved in the corpus. We also demonstrate that learned vector embeddings are helpful in downstream medical informatics applications. Conclusion This paper shows that medical term definitions can be helpful when learning embeddings of rare or previously unseen medical terms from a small corpus of specialized documents such as medical notes.

Abstract Background Additional evaluations, including second opinions, before breast cancer surgery may improve care, but may cause detrimental treatment delays that could allow disease progression. Aims We investigate the timing of surgical delays that are associated with survival benefits conferred by preoperative encounters versus the timing that are associated with potential harm. Methods and results We investigated survival outcomes of SEER Medicare patients with stage 1–3 breast cancer using propensity score‐based weighting. We examined interactions between the number of preoperative evaluation components and time from biopsy to definitive surgery. Components include new patient visits, unique surgeons, medical oncologists, or radiation oncologists consulted, established patient encounters, biopsies, and imaging studies. We identified 116 050 cases of whom 99% were female and had an average age of 75.0 (SD = 6.2). We found that new patient visits have a protective association with respect to breast cancer mortality if they occur quickly after diagnosis with breast cancer mortality subdistribution Hazard Ratios [sHRs] = 0.87 (95% Confidence Interval [CI] 0.76–1.00) for 2, 0.71 (CI 0.55–0.92) for 3, and 0.63 (CI 0.37–1.07) for 4+ visits at minimal delay. New patient visits predict worsened mortality compared with no visits if the surgical delay is greater than 33 days (CI 14–53) for 2, 33 days (CI 17–49) for 3, and 44 days (CI 12–75) for 4+. Medical oncologist visits predict worse outcomes if the surgical delay is greater than 29 days (CI 20–39) for 1 and 38 days (CI 12–65) for 2+ visits. Similarly, surgeon encounters switch from a positive to a negative association if the surgical delay exceeds 29 days (CI 17–41) for 1 visit, but the positive estimate persists over time for 3+ surgeon visits. Conclusion Preoperative visits that cause substantial delays may be associated with increased mortality in older patients with breast cancer.

Generative models have become increasingly popular in protein design, yet rigorous metrics that allow the comparison of these models are lacking. Here, the authors propose a set of such metrics and use them to compare three popular models.

Given the growing number of cancer survivors, it is important to better understand socio-spatial mobility patterns of cancer patients after diagnosis that could have public health implications regarding post-diagnostic access to care for treatment and follow-up surveillance. In this exploratory study, residential histories from LexisNexis were linked to New Jersey colon cancer cases diagnosed from 2006 to 2011 to examine differences in socio-spatial mobility patterns after diagnosis by stage at cancer diagnosis, sex, and race/ethnicity. For the colon cancer cases, we summarized and compared the number of residences and changes in the residential census tract and neighborhood poverty after the diagnosis. We found only minor changes in neighborhood poverty among the cases during the follow-up period after diagnosis. During the follow-up period of up to 10 years after diagnosis, 67% of the patients did not move to a different residential census tract, and 10.8% moved from New Jersey to another state. Cases that moved to a different census tract changed after diagnosis were generally less wealthy than non-movers, but the destination of relocation varied by race/ethnicity and socioeconomic status. We also found a significant association between residential mobility and stage at diagnosis, whereby patients diagnosed with colon cancer at an early stage were more likely to be movers. This study contributes to understanding of the socio-spatial mobility patterns in colon cancer patients and may help to inform cancer research by summarizing the extent to which colon cancer patients move after diagnosis.

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Abstract Background There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive modeling of EHR data to gain insights about the patterns of care and health outcomes. EHRs contain structured data such as diagnostic codes and laboratory tests, as well as unstructured free text data in form of clinical notes, which provide more detail about condition and treatment of patients. Methods In this work, we propose a method that jointly learns vector representations of medical concepts and words. This is achieved by a novel learning scheme based on the word2vec model. Our model learns those relationships by integrating clinical notes and sets of accompanying medical codes and by defining joint contexts for each observed word and medical code. Results In our experiments, we learned joint representations using MIMIC-III data. Using the learned representations of words and medical codes, we evaluated phenotypes for 6 diseases discovered by our and baseline method. The experimental results show that for each of the 6 diseases our method finds highly relevant words. We also show that our representations can be very useful when predicting the reason for the next visit. Conclusions The jointly learned representations of medical concepts and words capture not only similarity between codes or words themselves, but also similarity between codes and words. They can be used to extract phenotypes of different diseases. The representations learned by the joint model are also useful for construction of patient features.

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81-82%, while SecNet's accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.

Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I', II, II', VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., 'A' and 'D' for alpha regions on the left side of the Ramachandran map and 'a' and 'd' for equivalent regions on the right-hand side; classical Type I turns are 'AD' turns and Type I' turns are 'ad'). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.