Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

Using Epigenetic Networks for the Analysis of Movement Associated with Levodopa Therapy for Parkinson's Disease

© 2016 The Author(s) Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

Bayesian Centroid Estimation for Motif Discovery

Biological sequences may contain patterns that are signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We present a Bayesian model that is an extended version of the model adopted by the Gibbs motif sampler, and propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the maximum a posteriori estimator.Comment: 24 pages, 9 figure

A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson’s Dyskinesia

Parkinson’s disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient’s movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia

Genomic characterization of pediatric B‐lymphoblastic lymphoma and B‐lymphoblastic leukemia using formalin‐fixed tissues

BackgroundRecurrent genomic changes in B‐lymphoblastic leukemia (B‐ALL) identified by genome‐wide single‐nucleotide polymorphism (SNP) microarray analysis provide important prognostic information, but gene copy number analysis of its rare lymphoma counterpart, B‐lymphoblastic lymphoma (B‐LBL), is limited by the low incidence and lack of fresh tissue for genomic testing.ProcedureWe used molecular inversion probe (MIP) technology to analyze and compare copy number alterations (CNAs) in archival formalin‐fixed paraffin‐embedded pediatric B‐LBL (n = 23) and B‐ALL (n = 55).ResultsSimilar to B‐ALL, CDKN2A/B deletions were the most common alteration identified in 6/23 (26%) B‐LBL cases. Eleven of 23 (48%) B‐LBL patients were hyperdiploid, but none showed triple trisomies (chromosomes 4, 10, and 17) characteristic of B‐ALL. IKZF1 and PAX5 deletions were observed in 13 and 17% of B‐LBL, respectively, which was similar to the reported frequency in B‐ALL. Immunoglobulin light chain lambda (IGL) locus deletions consistent with normal light chain rearrangement were observed in 5/23 (22%) B‐LBL cases, compared with only 1% in B‐ALL samples. None of the B‐LBL cases showed abnormal, isolated VPREB1 deletion adjacent to IGL locus, which we identified in 25% of B‐ALL.ConclusionsOur study demonstrates that the copy number profile of B‐LBL is distinct from B‐ALL, suggesting possible differences in pathogenesis between these closely related diseases.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137353/1/pbc26363.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137353/2/pbc26363_am.pd

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility

Libri novi

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43281/1/11046_2005_Article_BF02052587.pd

Raman spectroscopy in head and neck cancer

In recent years there has been much interest in the use of optical diagnostics in cancer detection. Early diagnosis of cancer affords early intervention and greatest chance of cure. Raman spectroscopy is based on the interaction of photons with the target material producing a highly detailed biochemical 'fingerprint' of the sample. It can be appreciated that such a sensitive biochemical detection system could confer diagnostic benefit in a clinical setting. Raman has been used successfully in key health areas such as cardiovascular diseases, and dental care but there is a paucity of literature on Raman spectroscopy in Head and Neck cancer. Following the introduction of health care targets for cancer, and with an ever-aging population the need for rapid cancer detection has never been greater. Raman spectroscopy could confer great patient benefit with early, rapid and accurate diagnosis. This technique is almost labour free without the need for sample preparation. It could reduce the need for whole pathological specimen examination, in theatre it could help to determine margin status, and finally peripheral blood diagnosis may be an achievable target