nils-herrmann/clean_scirepeval huggingface dataset:
Extensions of marginalized graph kernels

Positive definite kernels between labeled graphs have recently been proposed. They enable the application of kernel methods, such as support vector machines, to the analysis and classification of graphs, for example, chemical compounds. These graph kernels are obtained by marginalizing a kernel between paths with respect to a random walk model on the graph vertices along the edges. We propose two extensions of these graph kernels, with the double goal to reduce their computation time and increase their relevance as measure of similarity between graphs. First, we propose to modify the label of each vertex by automatically adding information about its environment with the use of the Morgan algorithm. Second, we suggest a modification of the random walk model to prevent the walk from coming back to a vertex that was just visited. These extensions are then tested on benchmark experiments of chemical compounds classification, with promising results.
A mutual information kernel for sequences

We propose a new kernel for strings which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular support vector machines for protein classification. By incorporating prior assumptions on the properties of the alphabet and using a Bayesian averaging framework, we compute the value of this kernel in linear time and space, benefiting from previous achievements proposed in the field of universal coding. Encouraging classification results are reported on a standard protein homology detection experiment.
Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis

A major issue in computational biology is the reconstruction of pathways from several genomic datasets, such as expression data, protein interaction data and phylogenetic profiles. As a first step toward this goal, it is important to investigate the amount of correlation which exists between these data.



These methods are successfully tested on their ability to recognize operons in the Escherichia coli genome, from the comparison of three datasets corresponding to functional relationships between genes in metabolic pathways, geometrical relationships along the chromosome, and co-expression relationships as observed by gene expression data.
A covariance kernel for proteins

We propose a new kernel for biological sequences which borrows ideas and techniques from information theory and data compression. This kernel can be used in combination with any kernel method, in particular Support Vector Machines for protein classification. By incorporating prior biological assumptions on the properties of amino-acid sequences and using a Bayesian averaging framework, we compute the value of this kernel in linear time and space, benefiting from previous achievements proposed in the field of universal coding. Encouraging classification results are reported on a standard protein homology detection experiment.
Extracting active pathways from gene expression data

A promising way to make sense out of gene expression profiles is to relate them to the activity of metabolic and signalling pathways. Each pathway usually involves many genes, such as enzymes, which can themselves participate in many pathways. The set of all known pathways can therefore be represented by a complex network of genes. Searching for regularities in the set of gene expression profiles with respect to the topology of this gene network is a way to automatically extract active pathways and their associated patterns of activity.



We present a method to perform this task, which consists in encoding both the gene network and the set of profiles into two kernel functions, and performing a regularized form of canonical correlation analysis between the two kernels.



When applied to publicly available expression data the method is able to extract biologically relevant expression patterns, as well as pathways with related activity.
Comparison of SVM-Based Methods for Remote Homology Detection

Remote homology detection for protein sequences is one of the important and well-studied problems in Bioinformatics. Many algorithms have been developed for this purpose. The Smith-Waterman (SW) dynamic programming algorithm was developed in early 1980’s , and is still used widely today. In 1990’s, many methods were developed based on profiles  and hidden Markov models [2, 4]. In 2000’s, methods using SVMs (support vector machines) were developed such as the SVM-Fisher method . Recently, Liao and Noble proposed the SVM-pairwise method , which uses a vector of pairwise similarities with all proteins in the training set. Quite recently, we proposed a new SVM based method (SVM-SW), which uses the SW algorithm as a kernel function [7]. Though we do not yet succeed to prove that the SW score is always a valid kernel, SVM-SW worked successfully in all cases we tested. In this poster abstract, we briefly show the results of comparison of algorithms for remote homology detection using the SCOP database .
A tree kernel to analyze phylog enetic profi les

The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often “similar” or at least “related” to each other. The question we address in this paper is the following: how to measure the “similarity” between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods?  We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles. As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations.  All data and softwares used are freely and publicly available upon request.
Support Vector Machine Prediction of Signal Peptide Cleavage Site Using a New Class of Kernels for Strings

A new class of kernels for strings is introduced. These kernels can be used by any kernel-based data analysis method, including support vector machines (SVM). They are derived from probabilistic models to integrate biologically relevant information. We show how to compute the kernels corresponding to several classical probabilistic models, and illustrate their use by building a SVM for the problem of predicting the cleavage site of signal peptides from the amino-acid sequence of a protein. At a given rate of false positive this method retrieves up to 47% more true positives than the classical weight matrix method.
Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA

We present an algorithm to extract features from high-dimensional gene expression profiles, based on the knowledge of a graph which links together genes known to participate to successive reactions in metabolic pathways. Motivated by the intuition that biologically relevant features are likely to exhibit smoothness with respect to the graph topology, the algorithm involves encoding the graph and the set of expression profiles into kernel functions, and performing a generalized form of canonical correlation analysis in the corresponding reproducible kernel Hilbert spaces. 
 
Function prediction experiments for the genes of the yeast S. Cerevisiae validate this approach by showing a consistent increase in performance when a state-of-the-art classifier uses the vector of features instead of the original expression profile to predict the functional class of a gene.
Graph-driven features extraction from microarray data

Gene function prediction from microarray data is a rst step toward better understanding the machinery of the cell from relatively cheap and easy-to-produce data. In this paper we investigate whether the knowledge of many metabolic pathways and their catalyzing enzymes accumulated over the years can help improve the performance of classiers for this problem. The complex network of known biochemical reactions in the cell results in a representation where genes are nodes of a graph. Formulating the problem as a graph-driven features extraction problem, based on the simple idea that relevant features are likely to exhibit correlation with respect to the topology of the graph, we end up with an algorithm which involves encoding the network and the set of expression proles into kernel functions, and performing a regularized form of canonical correlation analysis in the corresponding reproducible kernel Hilbert spaces. Function prediction experiments for the genes of the yeast S. Cerevisiae validate this approach by showing a consistent increase in performance when a state-of-the-art classier uses the vector of features instead of the original expression prole to predict the functional class of a gene.
A tree kernel to analyse phylogenetic profiles

The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often 'similar' or at least 'related' to each other. The question we address in this paper is the following: how to measure the 'similarity' between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods?



We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles. As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations.
Adaptive context trees and text clustering

In the finite-alphabet context we propose four alternatives to fixed-order Markov models to estimate a conditional distribution. They consist in working with a large class of variable-length Markov models represented by context trees, and building an estimator of the conditional distribution with a risk of the same order as the risk of the best estimator for every model simultaneously, in a conditional Kullback-Leibler sense. Such estimators can be used to model complex objects like texts written in natural language and define a notion of similarity between them. This idea is illustrated by experimental results of unsupervised text clustering.
Deriving kernels from probabilistic models on discrete objects

Several learning methods using kernel functions () have been developed and shown to be highly efficient for tasks such as classification, regression, density estimation or clustering. One important feature of these methods is there modularity: they can be applied to any kind of object (e.g., images, sound, microarray data, protein sequences...) as soon as a kernel function has been defined for these objects. Kernels for real-valued vectors, such as polynomial or radial basis function kernels, have been known for a long time and successfully used in many applications where objects are naturally represented as vectors, e.g., images or microarray data. More recently new kernels have been developed for discrete objects such as sequences or trees, which naturally arise in computational biology or in natural language processing ([2, 3, 4]). These kernels open new analysis opportunities in these data-rich fields and are promised a bright future if they are confirmed to outperform “classical” analysis methods. We have developed a general method to derive a kernel K(x, y) from a probabilistic distribution p(x) for discrete objects (x, y) ∈ X 2 such as fixed-length strings or trees. This kernel interpolates between two natural kernels (see ), the diagonal kernel :
Detecting Transcriptional cis-Regulation from Gene Expression Data

The regulation of gene expression in eukaryotes is a complex process still to be elucidated. It has been known for a long time that the coordinated actions of particular transcription factors play a fundamental role in this process, but discovering such cis-regulatory actions remains a challenging problem, both experimentally and computationally. We have developed a systematic approach to discover combinations of regulatory motifs which potentially regulate certain genes under certain conditions, using gene expression data obtained from microarray experiments. Given a list of motifs and a table of microarray data, our method returns a ranked list of motif combinations which seem to play a particular role in the regulation of gene expression. We applied this method to track cis-regulatory factors in the yeast S. cerevisiae, using a compilation of public microarray data in combination with a list of known or putative transcription factor binding site motifs.
Approximate Inference for Infinite Contingent Bayesian Networks

In many practical problems—from tracking aircraft based on radar data to building a bibliographic database based on citation lists—we want to reason about an unbounded number of unseen objects with unknown relations among them. Bayesian networks, which define a fixed dependency structure on a finite set of variables, are not the ideal representation language for this task. This paper introduces contingent Bayesian networks (CBNs), which represent uncertainty about dependencies by labeling each edge with a condition under which it is active. A CBN may contain cycles and have infinitely many variables. Nevertheless, we give general conditions under which such a CBN defines a unique joint distribution over its variables. We also present a likelihood weighting algorithm that performs approximate inference in finite time per sampling step on any CBN that satisfies these conditions.
BLOG: Probabilistic Models with Unknown Objects

This paper introduces and illustrates BLOG, a formal language for defining probability models over worlds with unknown objects and identity uncertainty. BLOG unifies and extends several existing approaches. Subject to certain acyclicity constraints, every BLOG model specifies a unique probability distribution over first-order model structures that can contain varying and unbounded numbers of objects. Furthermore, complete inference algorithms exist for a large fragment of the language. We also introduce a probabilistic form of Skolemization for handling evidence.
BLOG: Relational Modeling with Unknown Objects

In many real-world probabilistic reasoning problems, one of the questions we want to answer is: how many objects are out there? Examples of such problems range from multitarget tracking to extracting information from text documents. However, most probabilistic modeling formalisms — even firstorder ones — assume a fixed, known set of objects. We introduce a language called Blog for specifying probability distributions over relational structures that include varying sets of objects. In this paper we present Blog informally, by means of example models for multi-target tracking and citation matching. We discuss some attractive features of Blog models and some avenues of future work.
CS 281 B Project Report : Marginalized Kernels for Object Recognition

It is relatively easy to obtain sets of images that are known to contain objects of a certain category, such as “face”, or “bicycle”. There has been promising work on using such data sets — without any manual labeling of object features — to learn generative models for object categories [4, 1]. These models can then be used to detect instances of the categories in other images. However, these generative models may not be the best tools for discriminating between, say, bicycles and motorcycles. In this report, we investigate a discriminative approach that builds on the generative models. Specifically, we use a marginalized kernel that sums over possible matchings of image regions to object parts. We find that the marginalized kernel performs much better than the generative model alone on distinguishing between object categories, improving accuracy by up to 14 percentage points.
Query-free news search

Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84%-91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics.
First-Order Probabilistic Models for Information Extraction

Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In this paper, we argue that firstorder probabilistic models (FOPMs) are a promising framework for IE, for two main reasons. First, FOPMs allow us to reason explicitly about entites that are mentioned in multiple documents, and compute the probability that two strings refer to the same entity — thus addressing the problem of coreference or record linkage in a principled way. Second, FOPMs allow us to resolve ambiguities in a text passage using information from the whole corpus, rather than disambiguating based on local cues alone and then trying to merge the results into a coherent knowledge base. This paper presents a comprehensive FOPM for a bibliographic database, and explains how the desired inference patterns emerge from the model.
Query-Free News Search

Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast.

We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84–91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics.
Identity Uncertainty and Citation Matching

Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identifiers or when those identifiers may not be perceived perfectly. In such cases, two observations may or may not correspond to the same object. In this paper, we consider the problem in the context of citation matching—the problem of deciding which citations correspond to the same publication. Our approach is based on the use of a relational probability model to define a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar. Identity uncertainty is handled by extending standard models to incorporate probabilities over the possible mappings between terms in the language and objects in the domain. Inference is based on Markov chain Monte Carlo, augmented with specific methods for generating efficient proposals when the domain contains many objects. Results on several citation data sets show that the method outperforms current algorithms for citation matching. The declarative, relational nature of the model also means that our algorithm can determine object characteristics such as author names by combining multiple citations of multiple papers.
The AMI System for the Transcription of Speech in Meetings

This paper describes the AMI transcription system for speech in meetings developed in collaboration by five research groups. The system includes generic techniques such as discriminative and speaker adaptive training, vocal tract length normalisation, heteroscedastic linear discriminant analysis, maximum likelihood linear regression, and phone posterior based features, as well as techniques specifically designed for meeting data. These include segmentation and cross-talk suppression, beam-forming, domain adaptation, Web-data collection, and channel adaptive training. The system was improved by more than 20% relative in word error rate compared to our previous system and was used in the NIST RT106 evaluations where it was found to yield competitive performance.
A hybrid Maxent/HMM based ASR system

The aim of this work is to develop a practical framework, which extends the classical Hidden Markov Models (HMM) for continuous speech recognition based on the Maximum Entropy (MaxEnt) principle. The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems. In particular, a new acoustic modelling based on discriminative MaxEnt models is formulated and is being developed to replace the generative Gaussian Mixture Models (GMM) commonly used to model acoustic variability. Initial experimental results using the TIMIT phone task are reported.
Speech and crosstalk detection in multichannel audio

The analysis of scenarios in which a number of microphones record the activity of speakers, such as in a round-table meeting, presents a number of computational challenges. For example, if each participant wears a microphone, speech from both the microphone's wearer (local speech) and from other participants (crosstalk) is received. The recorded audio can be broadly classified in four ways: local speech, crosstalk plus local speech, crosstalk alone and silence. We describe two experiments related to the automatic classification of audio into these four classes. The first experiment attempted to optimize a set of acoustic features for use with a Gaussian mixture model (GMM) classifier. A large set of potential acoustic features were considered, some of which have been employed in previous studies. The best-performing features were found to be kurtosis, "fundamentalness," and cross-correlation metrics. The second experiment used these features to train an ergodic hidden Markov model classifier. Tests performed on a large corpus of recorded meetings show classification accuracies of up to 96%, and automatic speech recognition performance close to that obtained using ground truth segmentation.