Optimization of the Architecture of Neural Networks Using a Deletion/Substitution/Addition Algorithm

Neural networks are a popular machine learning tool, particularly in applications such as the prediction of protein secondary structure. However, overfitting poses an obstacle to their effective use for this and other problems. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data yet fails to generalize to other data sets. One way of reducing the size of the parameter space is to alter the network topology so that some edges are removed; however, it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using the Deletion/Substitution/Addition algorithm introduced in Sinisi and van der Laan (2004) and Molinaro and van der Laan (2004). Results of this approach in the regression case are presented on two simulated data sets and the diabetes data of Efron et al. (2002)

Histologic Correlation With Magnetic Resonance Imaging for Benign and Malignant Lipomatous Masses

Purpose/results. We evaluated the diagnostic accuracy of magnetic resonance imaging (MRI) for 46 consecutive patients with lipomatous soft tissue tumors prior to biopsy and resection. Twenty-eight patients had benign lipomas and 18 had liposarcomas. Clinical differences between thdse patients with benign disease and those with malignant lesions were average age at the time of presentation (49 years for benign vs 62 years for malignant, p < 0.001) and average length of symptoms prior to resection (64 months for benign versus 38 months for malignant, p = 0.01). MRI characteristics associated with benign disease included: smaller tumor size (9.4 cm average greatest dimension for benign lesions vs 13.4 cm for malignant masses, p = 0.022); a mass with a uniformly homogeneous signal (p = 0.0003); a mass with homogeneous high T1 and T2 signals and a low short-time-inversion-recovery (STIR) signal comparable to normal fat (p < 0.0001). This last signal pattern was not seen in malignant lesions (0/18) and was present in almost all benign lipomas (25/28). The usual MRI descriptions of soft tissue masses such as infiltrating vs encapsulating, deep vs subcutaneous and septated vs non-septated were not helpful predictors of malignancy in this series. Needle biopsies of lipomatous masses with heterogeneous signals on MRI resulted in inaccurate diagnoses due to sampling error in 5/9 patients

The Sequence Ontology: a tool for the unification of genome annotations.

The Sequence Ontology (SO) is a structured controlled vocabulary for the parts of a genomic annotation. SO provides a common set of terms and definitions that will facilitate the exchange, analysis and management of genomic data. Because SO treats part-whole relationships rigorously, data described with it can become substrates for automated reasoning, and instances of sequence features described by the SO can be subjected to a group of logical operations termed extensional mereology operators.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Global Burden of Neuroendocrine Tumors and Changing Incidence in Kentucky

Background: Neuroendocrine tumors (NETs) have a low incidence but relatively high prevalence. Over the last three decades, the incidence of NETs has risen 6-fold in the United States. We conducted an observational study to compare the incidence of NETs reported to the Kentucky Cancer Registry (KCR) versus that reported to Surveillance, Epidemiology, and End Results Program (SEER). We also provide a systematic review of the state of neuroendocrine tumors worldwide, and compare the available global and local published data. Methods: KCR and SEER databases were queried for NET cases between 1995 and 2015. A detailed literature review of epidemiological data for various nations worldwide summarize epidemiological data from various countries. Results: KCR recorded 6179 individuals with newly diagnosed NETs between 1995 and 2015. Between 1995-2012, the incidence of NETs in KCR increased from 3.1 to 7.1 per 100,000 cases, while it increased from 3.96 to 6.61 in the SEER database. The incidence rates in both KCR and SEER databases were linear. 90.57% were Caucasians with 54.74% females. 27.67% of the Kentucky population was from the Appalachian region. Patients aged 50-64 years had the highest prevalence (38%). Lung NET (30.60%) formed the bulk of cases, followed by small intestine (16.82%), rectum/anus (11.35%) and colon (9.71%). Conclusions: NETs incidence between 1995 and 2015 show a linear increase in both KCR and SEER databases. Because of this increased incidence it is imperative for community oncologists to familiarize themselves with this entity, which until recently was under-studied and with few viable treatment options