Mining protein function from text using term-based support vector machines

<p>Abstract</p> <p>Background</p> <p>Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents.</p> <p>Results</p> <p>The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.</p> <p>Conclusion</p> <p>A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2.</p

Comparison of cancer diagnostic intervals before and after implementation of NICE guidelines: analysis of data from the UK General Practice Research Database

This is the final version of the article. Available from Nature Publishing Group via the DOI in this record.BACKGROUND: The primary aim was to use routine data to compare cancer diagnostic intervals before and after implementation of the 2005 NICE Referral Guidelines for Suspected Cancer. The secondary aim was to compare change in diagnostic intervals across different categories of presenting symptoms. METHODS: Using data from the General Practice Research Database, we analysed patients with one of 15 cancers diagnosed in either 2001-2002 or 2007-2008. Putative symptom lists for each cancer were classified into whether or not they qualified for urgent referral under NICE guidelines. Diagnostic interval (duration from first presented symptom to date of diagnosis in primary care records) was compared between the two cohorts. RESULTS: In total, 37,588 patients had a new diagnosis of cancer and of these 20,535 (54.6%) had a recorded symptom in the year prior to diagnosis and were included in the analysis. The overall mean diagnostic interval fell by 5.4 days (95% CI: 2.4-8.5; P<0.001) between 2001-2002 and 2007-2008. There was evidence of significant reductions for the following cancers: (mean, 95% confidence interval) kidney (20.4 days, -0.5 to 41.5; P=0.05), head and neck (21.2 days, 0.2-41.6; P=0.04), bladder (16.4 days, 6.6-26.5; P≤0.001), colorectal (9.0 days, 3.2-14.8; P=0.002), oesophageal (13.1 days, 3.0-24.1; P=0.006) and pancreatic (12.6 days, 0.2-24.6; P=0.04). Patients who presented with NICE-qualifying symptoms had shorter diagnostic intervals than those who did not (all cancers in both cohorts). For the 2007-2008 cohort, the cancers with the shortest median diagnostic intervals were breast (26 days) and testicular (44 days); the highest were myeloma (156 days) and lung (112 days). The values for the 90th centiles of the distributions remain very high for some cancers. Tests of interaction provided little evidence of differences in change in mean diagnostic intervals between those who did and did not present with symptoms specifically cited in the NICE Guideline as requiring urgent referral. CONCLUSION: We suggest that the implementation of the 2005 NICE Guidelines may have contributed to this reduction in diagnostic intervals between 2001-2002 and 2007-2008. There remains considerable scope to achieve more timely cancer diagnosis, with the ultimate aim of improving cancer outcomes.This research was funded by the National Cancer Action Team and the Department of Health Cancer Policy Team. The views contained in it are those of the authors and do not represent Department of Health policy. We can confirm that the corresponding author has had full access to the data and final responsibility for the decision to submit for publication. We would like to thank Rosemary Tate for early input into the protocol, staff of the GPRD for help in understanding the data. OCU is supported by the Peninsula Collaboration for Leadership in Applied Health Research and Care. Ethical approval: Independent Scientific Advisory Committee, numbers 09_0110 and 09_0111

Comparison of cancer diagnostic intervals before and after implementation of NICE guidelines: analysis of data from the UK General Practice Research Database

Background: The primary aim was to use routine data to compare cancer diagnostic intervals before and after implementation of the 2005 NICE Referral Guidelines for Suspected Cancer. The secondary aim was to compare change in diagnostic intervals across different categories of presenting symptoms. Methods: Using data from the General Practice Research Database, we analysed patients with one of 15 cancers diagnosed in either 2001–2002 or 2007–2008. Putative symptom lists for each cancer were classified into whether or not they qualified for urgent referral under NICE guidelines. Diagnostic interval (duration from first presented symptom to date of diagnosis in primary care records) was compared between the two cohorts. Results: In total, 37 588 patients had a new diagnosis of cancer and of these 20 535 (54.6%) had a recorded symptom in the year prior to diagnosis and were included in the analysis. The overall mean diagnostic interval fell by 5.4 days (95% CI: 2.4–8.5; Po0.001) between 2001–2002 and 2007–2008. There was evidence of significant reductions for the following cancers: (mean, 95% confidence interval) kidney (20.4 days, 0.5 to 41.5; P ¼ 0.05), head and neck (21.2 days, 0.2–41.6; P ¼ 0.04), bladder (16.4 days, 6.6–26.5; Pp0.001), colorectal (9.0 days, 3.2–14.8; P ¼ 0.002), oesophageal (13.1 days, 3.0–24.1; P ¼ 0.006) and pancreatic (12.6 days, 0.2–24.6; P ¼ 0.04). Patients who presented with NICE-qualifying symptoms had shorter diagnostic intervals than those who did not (all cancers in both cohorts). For the 2007–2008 cohort, the cancers with the shortest median diagnostic intervals were breast (26 days) and testicular (44 days); the highest were myeloma (156 days) and lung (112 days). The values for the 90th centiles of the distributions remain very high for some cancers. Tests of interaction provided little evidence of differences in change in mean diagnostic intervals between those who did and did not present with symptoms specifically cited in the NICE Guideline as requiring urgent referral. Conclusion: We suggest that the implementation of the 2005 NICE Guidelines may have contributed to this reduction in diagnostic intervals between 2001–2002 and 2007–2008. There remains considerable scope to achieve more timely cancer diagnosis, with the ultimate aim of improving cancer outcomes

Symptom lead times in lung and colorectal cancers: What are the benefits of symptom-based approaches to early diagnosis?

This is the final version of the article. Available from Cancer Research UK via the DOI in this record.Background: Individuals with undiagnosed lung and colorectal cancers present with non-specific symptoms in primary care more often than matched controls. Increased access to diagnostic services for patients with symptoms generates more early-stage diagnoses, but the mechanisms for this are only partially understood. Methods: We re-analysed a UK-based case-control study to estimate the Symptom Lead Time (SLT) distribution for a range of potential symptom criteria for investigation. Symptom Lead Time is the time between symptoms caused by cancer and eventual diagnosis, and is analogous to Lead Time in a screening programme. We also estimated the proportion of symptoms in lung and colorectal cancer cases that are actually caused by the cancer. Results: Mean Symptom Lead Times were between 4.1 and 6.0 months, with medians between 2.0 and 3.2 months. Symptom Lead Time did not depend on stage at diagnosis, nor which criteria for investigation are adopted. Depending on the criteria, an estimated 27-48% of symptoms in individuals with as yet undiagnosed lung cancer, and 12-32% with undiagnosed colorectal cancer are not caused by the cancer. Conclusions: In most cancer cases detected by a symptom-based programme, the symptoms are caused by cancer. These cases have a short lead time and benefit relatively little. However, in a significant minority of cases cancer detection is serendipitous. This group experiences the benefits of a standard screening programme, a substantial mean lead time and a higher probability of early-stage diagnosis.This work was supported by the National Institute for Health Research (NIHR) Programme Grants for Applied Research Programme, RP-PG-0608-10045

‘Just like talking to someone about like shit in your life and stuff, and they help you’: hopes and expectations for therapy among depressed adolescents

Objective: To explore hopes and expectations for therapy among a clinical population of depressed adolescents. Method: As part of a randomised clinical trial, 77 adolescents aged 11 to 17, with moderate to severe depression, were interviewed using a semi-structured interview schedule. The interviews were analysed qualitatively, using Framework Analysis. Results: The findings are reported around five themes: “The difficulty of imagining what will happen in therapy”, "the 'talking cure'"; “the therapist as doctor”, “therapy as a relationship” and “regaining the old self or developing new capacities”. Conclusions: Differing expectations are likely to have implications for the way young people engage with treatment, and failure to identify these expectations may lead to a risk of treatment breakdown

Promoting mental health and well-being in schools: examining mindfulness, relaxation and strategies for safety and well-being in English primary and secondary schools—study protocol for a multi-school, cluster randomised controlled trial (INSPIRE)

There are increasing rates of internalising difficulties, particularly anxiety and depression, being reported in children and young people in England. School-based universal prevention programmes are thought to be one way of helping tackle such difficulties. This paper describes an update to a four-arm cluster randomised controlled trial (http://www.isrctn.com/ISRCTN16386254), investigating the effectiveness of three different interventions when compared to usual provision, in English primary and secondary pupils. Due to the COVID-19 pandemic, the trial was put on hold and subsequently prolonged. Data collection will now run until 2024. The key changes to the trial outlined here include clarification of the inclusion and exclusion criteria, an amended timeline reflecting changes to the recruitment period of the trial due to the COVID-19 pandemic and clarification of the data that will be included in the statistical analysis, since the second wave of the trial was disrupted due to COVID-19. Trial registration ISRCTN Registry ISRCTN16386254. Registered on 30 August 2018

Unboxing mutations: Connecting mutation types with evolutionary consequences

A key step in understanding the genetic basis of different evolutionary outcomes (e.g., adaptation) is to determine the roles played by different mutation types (e.g., SNPs, translocations and inversions). To do this we must simultaneously consider different mutation types in an evolutionary framework. Here, we propose a research framework that directly utilizes the most important characteristics of mutations, their population genetic effects, to determine their relative evolutionary significance in a given scenario. We review known population genetic effects of different mutation types and show how these may be connected to different evolutionary outcomes. We provide examples of how to implement this framework and pinpoint areas where more data, theory and synthesis are needed. Linking experimental and theoretical approaches to examine different mutation types simultaneously is a critical step towards understanding their evolutionary significance