934 research outputs found
Machine learning for automatic prediction of the quality of electrophysiological recordings
The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters
A Composite Interface for Bioinformatics Applications (CIBA)
Bioinformatics, and more importantly the general use of computers in the field of Biology, has become a mainstream exercise for today\u27s biologist. So mainstream, that Bioinformatics classes are commonly offered to students in the standard Biology and Computer Science degree programs. This poses a problem for educators since some of the core Bioinformatics programs are still command-line based, requiring a deeper knowledge of computers than a standard biology student is expected to possess. The Composite Interface for Bioinformatics Applications, or CIBA for short, was created to address this issue and allow educators to focus on teaching how the algorithms work as opposed to teaching how the command line works
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Sparse representation based classification (SRC) methods have achieved
remarkable results. SRC, however, still suffer from requiring enough training
samples, insufficient use of test samples and instability of representation. In
this paper, a stable inverse projection representation based classification
(IPRC) is presented to tackle these problems by effectively using test samples.
An IPR is firstly proposed and its feasibility and stability are analyzed. A
classification criterion named category contribution rate is constructed to
match the IPR and complete classification. Moreover, a statistical measure is
introduced to quantify the stability of representation-based classification
methods. Based on the IPRC technique, a robust tumor recognition framework is
presented by interpreting microarray gene expression data, where a two-stage
hybrid gene selection method is introduced to select informative genes.
Finally, the functional analysis of candidate's pathogenicity-related genes is
given. Extensive experiments on six public tumor microarray gene expression
datasets demonstrate the proposed technique is competitive with
state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table
Statistical Methods to Enhance Clinical Prediction with High-Dimensional Data and Ordinal Response
Der technologische Fortschritt ermöglicht es heute, die moleculare
Konfiguration einzelner Zellen oder ganzer Gewebeproben zu
untersuchen. Solche in großen Mengen produzierten
hochdimensionalen Omics-Daten aus der Molekularbiologie lassen sich
zu immer niedrigeren Kosten erzeugen und werden so immer
häufiger auch in klinischen Fragestellungen eingesetzt.
Personalisierte Diagnose oder auch die Vorhersage eines
Behandlungserfolges auf der Basis solcher Hochdurchsatzdaten stellen
eine moderne Anwendung von Techniken aus dem maschinellen Lernen dar.
In der Praxis werden klinische Parameter, wie etwa der
Gesundheitszustand oder die Nebenwirkungen einer Therapie, häufig auf
einer ordinalen Skala erhoben (beispielsweise gut, normal,
schlecht).
Es ist verbreitet, Klassifikationsproblme mit ordinal skaliertem
Endpunkt wie generelle Mehrklassenproblme zu behandeln und somit die
Information, die in der Ordnung zwischen den Klassen enthalten ist, zu
ignorieren. Allerdings kann das Vernachlässigen dieser Information zu
einer verminderten Klassifikationsgüte führen oder sogar eine
ungünstige ungeordnete Klassifikation erzeugen.
Klassische Ansätze, einen ordinal skalierten Endpunkt direkt zu
modellieren, wie beispielsweise mit einem kumulativen Linkmodell,
lassen sich typischerweise nicht auf hochdimensionale Daten anwenden.
Wir präsentieren in dieser Arbeit hierarchical twoing (hi2) als
einen Algorithmus für die Klassifikation hochdimensionler Daten in
ordinal Skalierte Kategorien. hi2 nutzt die Mächtigkeit der
sehr gut verstandenen binären Klassifikation, um auch in ordinale
Kategorien zu klassifizieren. Eine Opensource-Implementierung von
hi2 ist online verfügbar.
In einer Vergleichsstudie zur Klassifikation von echten wie von
simulierten Daten mit ordinalem Endpunkt produzieren etablierte
Methoden, die speziell für geordnete Kategorien entworfen wurden,
nicht generell bessere Ergebnisse als state-of-the-art
nicht-ordinale Klassifikatoren. Die Fähigkeit eines Algorithmus, mit
hochdimensionalen Daten umzugehen, dominiert die
Klassifikationsleisting. Wir zeigen, dass unser Algorithmus hi2
konsistent gute Ergebnisse erzielt und in vielen Fällen besser
abschneidet als die anderen Methoden
Incorporating Pathway Information into Feature Selection Towards Better Performed Gene Signatures
To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable
Stability and aggregation of ranked gene lists
Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector
RENT—Repeated Elastic Net Technique for Feature Selection
publishedVersio
Concept and application of a computational vaccinology workflow
BACKGROUND : The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS : We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION : Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges
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