A combined approach to data mining of textual and structured data to identify cancer-related targets

BACKGROUND: We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein databases (UniProt, InterPro, NCBI Entrez), and state-of-the-art pathway knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas. RESULTS: The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, ~35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1. CONCLUSION: The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

The Power of the Web in Cancer Drug Discovery and Clinical Trial Design: Research without a Laboratory?

The discovery of effective cancer treatments is a key goal for pharmaceutical companies. However, the current costs of bringing a cancer drug to the market in the USA is now estimated at $1 billion per FDA approved drug, with many months of research at the bench and costly clinical trials. A growing number of papers highlight the use of data mining tools to determine associations between drugs, genes or protein targets, and possible mechanism of actions or therapeutic efficacy which could be harnessed to provide information that can refine or direct new clinical cancer studies and lower costs. This report reviews the paper by R.J. Epstein, which illustrates the potential of text mining using Boolean parameters in cancer drug discovery, and other studies which use alternative data mining approaches to aid cancer research

Cell line name recognition in support of the identification of synthetic lethality in cancer from text

Motivation: The recognition and normalization of cell line names in text is an important task in biomedical text mining research, facilitating for instance the identification of synthetically lethal genes from the literature. While several tools have previously been developed to address cell line recognition, it is unclear whether available systems can perform sufficiently well in realistic and broad-coverage applications such as extracting synthetically lethal genes from the cancer literature. In this study, we revisit the cell line name recognition task, evaluating both available systems and newly introduced methods on various resources to obtain a reliable tagger not tied to any specific subdomain. In support of this task, we introduce two text collections manually annotated for cell line names: the broad-coverage corpus Gellus and CLL, a focused target domain corpus. Results: We find that the best performance is achieved using NERsuite, a machine learning system based on Conditional Random Fields, trained on the Gellus corpus and supported with a dictionary of cell line names. The system achieves an F-score of 88.46% on the test set of Gellus and 85.98% on the independently annotated CLL corpus. It was further applied at large scale to 24 302 102 unannotated articles, resulting in the identification of 5 181 342 cell line mentions, normalized to 11 755 unique cell line database identifiers

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Drug Discov Today

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide an overview of the drug development studies that use both AI and RWD. We found that the most popular applications were adverse event detection, trial recruitment, and drug repurposing. Here, we also discuss current research gaps and future opportunities.U18 DP006512/DP/NCCDPHP CDC HHSUnited States/R21 ES032762/ES/NIEHS NIH HHSUnited States/R21 CA245858/CA/NCI NIH HHSUnited States/UL1 TR001427/TR/NCATS NIH HHSUnited States/R01 CA246418/CA/NCI NIH HHSUnited States/U18DP006512/ACL/ACL HHSUnited States/R21 AG068717/AG/NIA NIH HHSUnited States/2021-11-27T00:00:00Z33358699PMC862686410635vault:4053

A comparative analysis of 21 literature search engines

With increasing number of bibliographic software, scientists and health professionals either make a subjective choice of tool(s) that could suit their needs or face a challenge of analyzing multiple features of a plethora of search programs. There is an urgent need for a thorough comparative analysis of the available bio-literature scanning tools, from the user’s perspective. We report results of the first time semi-quantitative comparison of 21 programs, which can search published (partial or full text) documents in life science areas. The observations can assist life science researchers and medical professionals to make an informed selection among the programs, depending on their search objectives. 
Some of the important findings are: 
1. Most of the hits obtained from Scopus, ReleMed, EBImed, CiteXplore, and HighWire Press were usually relevant (i.e. these tools show a better precision than other tools). 
2. But a very high number of relevant citations were retrieved by HighWire Press, Google Scholar, CiteXplore and Pubmed Central (they had better recall). 
3. HWP and CiteXplore seemed to have a good balance of precision and recall efficiencies. 
4. PubMed Central, PubMed and Scopus provided the most useful query systems. 
5. GoPubMed, BioAsk, EBIMed, ClusterMed could be more useful among the tools that can automatically process the retrieved citations for further scanning of bio-entities such as proteins, diseases, tissues, molecular interactions, etc. 
The authors suggest the use of PubMed, Scopus, Google Scholar and HighWire Press - for better coverage, and GoPubMed - to view the hits categorized based on the MeSH and gene ontology terms. The article is relavant to all life science subjects.
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