BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking.

Here, we present an update of Breast Cancer Now Tissue Bank bioinformatics, a rich platform for the sharing, mining, integration and analysis of breast cancer data. Its modalities provide researchers with access to a centralised information gateway from which they can access a network of bioinformatic resources to query findings from publicly available, in-house and experimental data generated using samples supplied from the Breast Cancer Now Tissue Bank. This in silico environment aims to help researchers use breast cancer data to their full potential, irrespective of any bioinformatics barriers. For this new release, a complete overhaul of the IT and bioinformatic infrastructure underlying the portal has been conducted and a host of novel analytical modules established. We developed and adopted an automated data selection and prioritisation system, expanded the data content and included tissue and cell line data generated from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia, designed a host of novel analytical modalities and enhanced the query building process. Furthermore, the results are presented in an interactive format, providing researchers with greater control over the information on which they want to focus. Breast Cancer Now Tissue Bank bioinformatics can be accessed at http://bioinformatics.breastcancertissuebank.org/.Breast Cancer Campaign [TB2016BIF]; Pancreatic Cancer Research Fund (PCRFTB) [Tissue Bank grant, to J.M and A.Z.D.U.]. Funding for open access charge: Breast Cancer Campaign [TB2016BIF]

A hybrid approach to protein folding problem integrating constraint programming with local search

<p>Abstract</p> <p>Background</p> <p>The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.</p> <p>Results</p> <p>Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.</p> <p>Conclusion</p> <p>Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.</p

IW-Scoring: an Integrative Weighted Scoring framework for annotating and prioritizing genetic variations in the noncoding genome

Cancer Research UK Centre of Excellence Award to Barts Cancer Centre [C16420/A18066]; Barts and The London Charity Grant [MGU0344]; D.U. is funded by Pancreatic Cancer Research Fund Tissue Bank Grant. Funding for open access charge: Charity Open Access Fund (COAF) award

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic -omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.Pancreatic Cancer Research Fund [Tissue Bank grant]; Cancer Research UK [Grant A12008]; Breast Cancer Campaign [Tissue Bank Bioinformatics grant TB2016BIF]

Multimorbidity in patients living with and beyond cancer: protocol for a scoping review

INTRODUCTION: The number of people living with and beyond cancer is increasing rapidly. Many of them will experience ongoing physical or psychological sequelae as a result of their original cancer diagnosis or comorbidities arising from risk factors common to cancers and other long-term conditions. This poses the complex problem of managing cancer as a ‘chronic’ illness along with other existing comorbidities. This scoping review aims to map the literature available on multimorbidity in patients living with and beyond cancer, to explore, quantify and understand the impact of comorbid illnesses to inform work around cancer care in UK primary care settings. METHODS AND ANALYSIS: This review will be guided by Joanna Briggs Institute Reviewer’s manual for scoping reviews. A systematic literature search using Medical Subject Heading and text words related to cancer survivors and multimorbidity will be performed in MEDLINE, CINAHL, Embase and Web of Science, from 1990. Results will be described in a narrative style, reported in extraction tables and diagrams, and where appropriate in themes and text. ETHICS AND DISSEMINATION: The scoping review will undertake secondary analysis of published literature; therefore, ethics committee approval is not required. Results will be disseminated through a peer-reviewed scientific journal and presented in relevant conferences. The scoping review will inform understanding of the burden of multimorbidity for cancer survivors, thus allow families, practitioners, clinicians and researchers to take the steps necessary to improve patient-centred care

Derivative scores from site accessibility and ranking of miRNA target predictions

In the present study, we define derivative scoring functions from PITA and STarMir predictions. The scoring functions are evaluated for up to five selected miRNAs with a relatively large number of validated targets reported by TarBase and miRecords. The average ranking of validated targets returned by PITA and STarMir is compared to the average ranking produced by the new derivatives scores. We obtain an average improvement of 13.6% (STD∼5.7%) relative to the average ranking of validated targets produced by PITA and STarMir

Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study

The study aimed to develop a prediction model for differentiating suspected PDAC from benign conditions. We used a prospective cohort of patients with pancreatic disease (n = 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (n = 340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Age (over 55), weight loss in hypertensive patients, recent symptoms of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low red blood cell count and low serum sodium were identified as the most important features. These predictors were then used for training several machine-learning-based risk-prediction models on 75% of the cohort. Models were assessed on the remaining 25%. A logistic regression-based model had the best overall performance in the validation cohort (area-under-the-curve = 0.90; Spiegelhalter’s z = −1·82, p = 0.07). Setting a probability threshold of 0.15 guided by the maximum F2-score of 0.855, 96.8% sensitivity was reached in the full cohort, which could lead to earlier detection of 84.7% of the PDAC patients. The prediction model has the potential to be applied in primary, secondary and emergency care settings for the early distinction of suspected PDAC patients and expedited referral to specialist hepato-pancreatico-biliary services