Morphologic aspects of the cervical (C3-C7) annular epiphysis: a skeletal study.

BACKGROUND CONTEXT: The annular epiphysis (AE) is a peripheral ring of cortical bone that forms a secondary ossification center in the superior and inferior surfaces of vertebral bodies (VBs). The AE is the last ossification site in the skeleton, typically forming at about the 25th year of life. The AE functions jointly with vertebral endplates to anchor the intervertebral discs to the VBs. PURPOSE: To establish accurate data on the sizes of the AE of the cervical spine (C3-C7); to compare the ratios between areas and the ratios of the AE to VBs; to compare the ratios between the superior and inferior VB surface areas; and to compare AE lengths between the posterior and anterior midsagittal areas. STUDY DESIGN: Measurement of 424 cervical spines (C3-C7) obtained from the skeletal collection of the Natural History Museum, Cleveland, Ohio (USA). METHODS: The sample was characterized by sex, age, and ethnic origin. The following measurements were recorded for each vertebra: (1) the surface area of the VBs and the AE, (2) the midsagittal anterior and posterior length of the AE, (3) the ratios between the AE and VB surface areas, and (4) the ratios between the superior and inferior disc surface areas. RESULTS: The study revealed that the AE and VBs in men were larger than in women. With age, the AE and VBs became larger; the ratio between the AE and VB surface was approximately 0.5 throughout the middle to lower cervical spine. The ratio of superior to inferior VBs was approximately 0.8. We found no differences between African Americans versus European Americans or between the anterior versus the posterior midsagittal length of the AE of the superior and inferior VBs. CONCLUSIONS: The ratios between the superior and inferior VBs are ≥0.8, and the ratio is the same for the entire middle to lower spine. Thus, the ratio between the superior and inferior VBs to the AE is ≥ 0.5. Men had larger AEs and VBs than women did, with both VBs and AEs becoming larger with age. Knowing these relationships are important so that orthopedic surgeons can best correct these issues in young patients (<25 years old) during spine surgery. The data reported here provide, for the first time, all the relevant sizes of the AE and VB. In future studies, AEs and VBs of living patients can be measured with computed tomography. CLINICAL SIGNIFICANCE: The ER location and function are clinically significant showing any changes during life that might lead to clinical issues related to intervertebral discs such as intervertebral disc asymmetry, disc herniation, nerve pressure, cervical osteophytes and neck pain.

Network-induced classification kernels for gene expression profile analysis.

Computational classification of gene expression profiles into distinct disease phenotypes has been highly successful to date. Still, robustness, accuracy, and biological interpretation of the results have been limited, and it was suggested that use of protein interaction information jointly with the expression profiles can improve the results. Here, we study three aspects of this problem. First, we show that interactions are indeed relevant by showing that co-expressed genes tend to be closer in the network of interactions. Second, we show that the improved performance of one extant method utilizing expression and interactions is not really due to the biological information in the network, while in another method this is not the case. Finally, we develop a new kernel method--called NICK--that integrates network and expression data for SVM classification, and demonstrate that overall it achieves better results than extant methods while running two orders of magnitude faster.

Analysis of the IJCNN 2011 UTL challenge.

We organized a challenge in "Unsupervised and Transfer Learning": the UTL challenge (http://clopinet.com/ul). We made available large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal was to learn data representations that capture regularities of an input space for re-use across tasks. The representations were evaluated on supervised learning "target tasks" unknown to the participants. The first phase of the challenge was dedicated to "unsupervised transfer learning" (the competitors were given only unlabeled data). The second phase was dedicated to "cross-task transfer learning" (the competitors were provided with a limited amount of labeled data from "source tasks", distinct from the "target tasks"). The analysis indicates that learned data representations yield significantly better results than those obtained with original data or data preprocessed with standard normalizations and functional transforms.

miRNAkey: a software for microRNA deep sequencing analysis.

MOTIVATION: MicroRNAs (miRNAs) are short abundant non-coding RNAs critical for many cellular processes. Deep sequencing (next-generation sequencing) technologies are being readily used to receive a more accurate depiction of miRNA expression profiles in living cells. This type of analysis is a key step towards improving our understanding of the complexity and mode of miRNA regulation. RESULTS: miRNAkey is a software package designed to be used as a base-station for the analysis of miRNA deep sequencing data. The package implements common steps taken in the analysis of such data, as well as adds unique features, such as data statistics and multiple read determination, generating a novel platform for the analysis of miRNA expression. A user-friendly graphical interface is applied to determine the analysis steps. The tabular and graphical output contains general and detailed reports on the sequence reads and provides an accurate picture of the differentially expressed miRNAs in paired samples. AVAILABILITY AND IMPLEMENTATION: See http://ibis.tau.ac.il/miRNAkey CONTACT: nshomron@post.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SlimPLS: a method for feature selection in gene expression-based disease classification.

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique.

Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge.

We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design, or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants in the "prior knowledge" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants in the "agnostic learning" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performance. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: http://www.agnostic.inf.ethz.ch/.

A machine learning predictor of facial attractiveness revealing human-like psychophysical biases.

Recent psychological studies have strongly suggested that humans share common visual preferences for facial attractiveness. Here, we present a learning model that automatically extracts measurements of facial features from raw images and obtains human-level performance in predicting facial attractiveness ratings. The machine's ratings are highly correlated with mean human ratings, markedly improving on recent machine learning studies of this task. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments that are remarkably similar to those of humans. Thus, a model trained explicitly to capture a specific operational performance criteria, implicitly captures basic human psychophysical characteristics.

Feature selection via coalitional game theory.

We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets.

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity.

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.

Assessing the number of ancestral alternatively spliced exons in the human genome.

BACKGROUND: It is estimated that between 35% and 74% of all human genes undergo alternative splicing. However, as a gene that undergoes alternative splicing can have between one and dozens of alternative exons, the number of alternatively spliced genes by itself is not informative enough. An additional parameter, which was not addressed so far, is therefore the number of human exons that undergo alternative splicing. We have previously described an accurate machine-learning method allowing the detection of conserved alternatively spliced exons without using ESTs, which relies on specific features of the exon and its genomic vicinity that distinguish alternatively spliced exons from constitutive ones. RESULTS: In this study we use the above-described approach to calculate that 7.2% (+/- 1.1%) of all human exons that are conserved in mouse are alternatively spliced in both species. CONCLUSION: This number is the first estimation for the extent of ancestral alternatively spliced exons in the human genome.

Facial attractiveness: beauty and the machine.

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

Accurate identification of alternatively spliced exons using support vector machine.

MOTIVATION: Alternative splicing is a major component of the regulatory action on mammalian transcriptomes. It is estimated that over half of all human genes have more than one splice variant. Previous studies have shown that alternatively spliced exons possess several features that distinguish them from constitutively spliced ones. Recently, we have demonstrated that such features can be used to distinguish alternative from constitutive exons. In the current study, we used advanced machine learning methods to generate robust classifier of alternative exons. RESULTS: We extracted several hundred local sequence features of constitutive as well as alternative exons. Using feature selection methods we find seven attributes that are dominant for the task of classification. Several less informative features help to slightly increase the performance of the classifier. The classifier achieves a true positive rate of 50% for a false positive rate of 0.5%. This result enables one to reliably identify alternatively spliced exons in exon databases that are believed to be dominated by constitutive exons.

Dynamic proximity of spatio-temporal sequences.

Recurrent networks can generate spatio-temporal neural sequences of very large cycles, having an apparent random behavior. Nonetheless a proximity measure between these sequences may be defined through comparison of the synaptic weight matrices that generate them. Following the dynamic neural filter (DNF) formalism we demonstrate this concept by comparing teacher and student recurrent networks of binary neurons. We show that large sequences, providing a training set well exceeding the Cover limit, allow for good determination of the synaptic matrices. Alternatively, assuming the matrices to be known, very fast determination of the biases can be achieved. Thus, a spatio-temporal sequence may be regarded as spatio-temporal encoding of the bias vector. We introduce a linear support vector machine (SVM) variant of the DNF in order to specify an optimal weight matrix. This approach allows us to deal with noise. Spatio-temporal sequences generated by different DNFs with the same number of neurons may be compared by calculating correlations of the synaptic matrices of the reconstructed DNFs. Other types of spatio-temporal sequences need the introduction of hidden neurons, and/or the use of a kernel variant of the SVM approach. The latter is being defined as a recurrent support vector network (RSVN).