nils-herrmann/clean_scirepeval huggingface dataset:
Functional genomics of osteoarthritis.

Functional genomics is a challenging new way to address a complex disease like osteoarthritis on a molecular level. Despite osteoarthritis being ultimately a biochemical problem, mainly characterized by an imbalanced cartilage matrix turnover, a deeper understanding of molecular events within the tissue cells (i.e., the chondrocytes) will provide not only a better understanding of pathogenetic mechanisms but also new diagnostic markers and cellular targets for therapeutic intervention. This innovative technology represents a challenging approach complementing (not replacing) classical research in previously described and new disease-relevant genes: large-scale functional genomics will open up new areas of so far unrecognized molecular networks. This will include as yet unidentified players in the anabolic-catabolic balance of matrix turnover of articular cartilage as well as disease-relevant intracellular signaling cascades so far hardly investigated in articular chondrocytes. However, care must be taken not to over or misinterpret results and some major challenges must be overcome in order to properly utilize the potential of this technology in the field of osteoarthritis.
Gene expression profiling by the cDNA array technology: Molecular portraying of chondrocytes

Many studies have investigated morphological and biochemical changes during osteoarthritic cartilage degeneration. These studies have never allowed to paint a picture of an expression profile in the chondrocytes during the disease process, based on a broad number of genes simultaneously investigated using the same material. cDNA-array technology is a new, very powerful tool for mRNA expression profiling of large numbers of genes at the same time. The major limitation in using this technology for gene expression profiling in diseased versus normal tissue is the inherent heterogeneity of cell populations present in most tissues. Thus, one is not able to distinguish expression signals derived from endothelial cells, neural tissue, inflammatory cell populations, fibroblasts and other cells physiologically or pathologically present in the tissues. In this respect, adult human articular cartilage offers the great advantage that it contains - both in the normal and in the diseased state - only one cell population, the chondrocytes. Therefore, the detected gene expression levels can be fully attributed to the chondrocytes.
Co-clustering of biological networks and gene expression data

Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.



We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.
Confidence measures for protein fold recognition

We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures.



As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity--sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable.


AVAILABILITY
The benchmark set is available upon request.
Centralization: a new method for the normalization of gene expression data

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.
Confidence Measures for Fold Recognition

It is a standard procedure to compare new amino acid sequences to databases of proteins that have been studied already in order to find similarities in structure and function. This comparison can be sequence–sequence or sequence– structure based. In order to compare, an alignment is performed of the target protein sequence (whose structure we are searching) with a template protein (whose structure we know). For a sequence–sequence alignment, the alignment algorithm optimizes a certain scoring function that quantifies the similarities of the amino acids at individual positions. For a sequence–structure alignment, also known as threading, usually the scoring function that is optimized is designed to capture the essence of structural similarity among proteins. These scores are supposed to be comparable between different proteins, since we want to select the template which achieves the highest alignment score to the target protein as our candidate for the structural model of a protein. The involved scoring functions are inaccurate, however. Thus, it is very helpful if the method can augment the generated alignment and its score with a statistical significance value which captures the confidence that we can put into the generated alignment. While important for protein alignment and threading as stand-alone tools, significance scores are even more essential if protein alignment is used in an automated cascade of tools for protein structure prediction. In this paper we analyze the performance of several variants of the 123D protein threading method Alexandrov, Nussinov, & Zimmer and compare it to several variants of optimal sequence alignment. Where theoretically available, we analyze the statistical significance of scores. For the other methods, we propose empirical approximations to pvalues and evaluate their validity.
Anabolic and catabolic gene expression pattern analysis in normal versus osteoarthritic cartilage using complementary DNA-array technology.

To understand changes in gene expression levels that occur during osteoarthritic (OA) cartilage degeneration, using complementary DNA (cDNA)-array technology.



Nine normal, 6 early degenerated, and 6 late-stage OA cartilage samples of human knee joints were analyzed using the Human Cancer 1.2 cDNA array and TaqMan analysis.



In addition to a large variability of expression levels between different patients, significant expression patterns were detectable for many genes. Cartilage types II and VI collagen were strongly expressed in late-stage specimens, reflecting the high matrix-remodeling activity of advanced OA cartilage. The increase in fibronectin expression in early degeneration suggests that fibronectin is a crucial regulator of matrix turnover activity of chondrocytes during early disease development. Of the matrix metalloproteinases (MMPs), MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and down-regulated in the late stages of disease. This indicates that other degradation pathways might be more important in late stages of cartilage degeneration, involving other enzymes, such as MMP-2 and MMP-11, both of which were up-regulated in late-stage disease. MMP-11 was up-regulated in OA chondrocytes and, interestingly, also in the early-stage samples. Neither MMP-1 nor MMP-8 was detectable, and MMP-13 and MMP-2 were significantly detectable only in late-stage specimens, suggesting that early stages are characterized more by degradation of other matrix components, such as aggrecan and other noncollagenous molecules, than by degradation of type II collagen fibers.



This investigation allowed us to identify gene expression profiles of the disease process and to get new insights into disease mechanisms, for example, to develop a picture of matrix proteinases that are differentially involved in different phases of the disease process.
A simple iterative approach to parameter optimization

Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a linear scoring function combines the values for different properties of possible sequence-to-structure alignments into a single score to allow for unambigous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, the implied partial ordering on optimal alignments may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a novel approach: iterating the computation of solutions (here: threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via a systematic calibration method. We show that this procedure converges to structurally meaningful weights, that also lead to significantly improved performance on comprehensive test data sets as measured in different ways. The latter indicates that the performance of threading can be improved in general.
Analysis of Gene Expression Data with Pathway Scores

We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. We assess the significance of the scores for the investigated pathways by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically significant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.
Application of Parameter Optimization to Molecular Comparison Problems

Various bioinformatics comparison problems require optimizing several different properties simultaneously. Often linear objective functions combine the values for different properties of solution candidates into a single score to allow for multivariate optimization. In this context, an essential question is how each property should be weighted. Frequently, no apparent measure is available to serve as a model for the score. However, if preferences of certain solution candidates over others in a training set are available, the implied partial ordering may be used to best possibly adjust the weights. We apply different strategies to optimize the parameterization of empirical scoring functions used for two molecular comparison problems, protein threading and small molecule superposition. Using well established evaluation methods, it can be shown that the results of both comparison methods are significantly improved by systematically choosing appropriate weights for the scoring function contributions.
Power-optimal simultaneous buffer insertion/sizing and uniform wire sizing for single long wires

This paper studies the problems of minimizing power dissipation of an interconnect wire by simultaneously considering buffer insertion/sizing and wire sizing. We obtain optimal solutions for the problems of optimizing power dissipation for simultaneous buffer insertion/sizing and uniform wire sizing (BISUWS) under the delay constraints. These solutions can be used to estimate the power dissipation in the interconnect designs.
A novel wavelet method for noise analysis of nonlinear circuits

In this paper, a novel wavelet method is proposed for noise analysis of nonlinear circuits. Compared with the existing algorithms capable of accessing circuit performance in the present of noise, the proposed method presents several merits. First, it fully accounts for nonlinearities. Second, it can handle signals with continuous frequency spectra. Third, by taking advantage of the properties of the wavelet bases, such as local compactness and multi-resolution, it holds high simulation speed and high accuracy. Furthermore, an adaptive scheme exists to automatically select the wavelet basis functions for a desired accuracy. All these merits make the novel wavelet method outperforms its previous techniques.
Design and Verification of High-Speed VLSI Physical Design

With the rapid development of deep submicron (DSM) VLSI circuit designs, many issues such as time closure and power consumption are making the physical designs more and more challenging. In this review paper we provide readers with some recent progress of the VLSI physical designs. The recent developments of floorplanning and placement, interconnect effects, modeling and delay, buffer insertion and wire sizing, circuit order reduction, power grid analysis, parasitic extraction, and clock signal distribution are briefly reviewed.
Power-optimal simultaneous buffer insertion/sizing and wire sizing for two-pin nets

This paper studies the problems of optimizing power dissipation for simultaneous buffer insertion/sizing and uniform wire sizing (BISUWS), and simultaneous buffer insertion/sizing and tapered wire sizing (BISTWS). For BISUWS, we analyze the optimal total power dissipation under the delay constraints as well as the power-delay tradeoff. For BISTWS, we study the problems of minimizing power dissipation with optimal delay constraints or with a given delay penalty. We derive optimal solutions for both cases. These solutions can be used to efficiently estimate the power dissipation for long single wires in the interconnect designs.
Block SAPOR: block second-order Arnoldi method for passive order reduction of multi-input multi-output RCS interconnect circuits

Recently model order reduction techniques for second-order systems have obtained many research interests for the simulation of RCS interconnect circuits employing susceptance elements. In this paper, we propose a Block SAPOR (Block Second-order Arnoldi method for Passive Order Reduction) for multi-input multi-output RCS circuits. The proposed Block SAPOR algorithm can simultaneously guarantee passivity and achieve higher accuracy than the first order reduction technique PRIMA. Most importantly, the reduced system matrices obtained by the proposed method can preserve the structure of the original system matrices. Such a nice property makes it possible to construct an equivalent RCS circuit for the reduced system.
Phase noise spectra analysis for LC oscillators

This paper presents an analytical solution of the phase noise spectra for LC oscillators. Modeling noisy input processes as current impulse injections to the circuit, a linearized phase noise model is obtained by the Taylor series expansion method. For this linearized model, an analytic expression of the impulse sensitivity function (ISF) is derived. This is a new method to obtain ISF for a class of LC oscillators. Then we analyze for oscillator's output power spectra density (PSD) and we show that the two widely publicized methods presented in Vanassche et al. (2003) and Demir (2002) produce equal results for the oscillator's phase noise spectrum with white noise source.
Direct nonlinear order reduction with variational analysis

The variational analysis has been employed in for order reduction of weakly nonlinear systems. For a relatively strong nonlinear system, this method will mostly lose efficiency because of the exponentially increased number of inputs in higher order variational equations caused by the individual reduction process of the variational systems. Moreover, the inexact inputs into the higher order variational equations indispensably introduce extra errors in the order reduction process. Inspired by the variational analysis, we propose a direct model order reduction method. The order of the approximate polynomial system of the original nonlinear system is directly reduced by one project space. The proposed direct reduction technique can easily avoid the errors brought by inexact inputs and the exponentially increased inputs. We show theoretically and experimentally that the proposed method can achieve much more accurate reduced system with smaller order size than the conventional variational equation order reduction method.
Analog circuit behavioral modeling via wavelet collocation method with auto-companding

We propose an auto-companding technique for the analog behavioral modeling via wavelet collocation method. The companding function is automatically constructed according to the singularities of the input-output function of the circuit block. Such a general-purpose technique can be applied for the automatic modeling of arbitrary building block of arbitrary input-output function. Moreover, compared with the published modeling approaches, this method works more efficiently in reducing both the modeling error and the number of basis functions in wavelet expansion.
Two-sided projection method in variational equation model order reduction of nonlinear circuits

For relatively strong nonlinear circuits, the conventional variational order reduction method loses efficiency due to the exponentially increased number of inputs and the one-sided projection technique. In this paper, we propose a two-sided projection approach which is capable of matching a much higher number of moments than the one-sided projection method. We show by theoretical analysis and experiments that the two-sided projection method is very efficient for the variational equation model order reduction of nonlinear circuits.
Frequency domain wavelet method with GMRES for large-scale linear circuit simulation

In this paper, a frequency domain fast wavelet collocation method with GMRES (FFWCM-G) is proposed for the simulation of high-speed large-scale linear VLSI circuits. Taking advantages of wavelet expansion and adaptive scheme, the number of frequency points for calculating frequency response is minimized. Moreover, due to the efficiency of GMRES as the internal iterative solver, the proposed method can achieve nearly linear time complexity, which undoubtedly would be more promising to simulate large-scale integrated circuits.
SAPOR: second-order Arnoldi method for passive order reduction of RCS circuits

The recently-introduced susceptance element exhibits many prominent features in modeling the on-chip magnetic couplings. For an RCS circuit, it is better to be formulated as a second-order system. Therefore, corresponding MOR (model-order reduction) techniques for second-order systems are desired to efficiently deal with the ever-increasing circuit scale and to preserve essential model properties. We first review the existing MOR methods for RCS circuits, such as ENOR and SMOR, and discuss several key issues related to numerical stability and accuracy of the methods. Then, a technique, SAPOR (second-order Arnoldi method for passive order reduction), is proposed to effectively address these issues. Based on an implementation of a generalized second-order Arnoldi method, SAPOR is numerically stable and efficient. Meanwhile, the reduced-order system also guarantees passivity.
Frequency driven repeater insertion for deep submicron

Repeaters now widely used to decrease delay and increase the performance of long interconnects. The maximum operating frequency is limited by the longest wire between two repeaters. The main goal of repeater insertion is to reduce the delay of the interconnects in a circuit. Increasing the number of repeaters on an interconnect increases the slew rate and the operating frequency but decreases the design performance in terms of area and power. This paper presents a new methodology of repeater insertion considering the circuit operating frequency to improve the slew rate and reduce the area overhead of the repeaters. We propose a mathematical treatment for finding the number and position of repeaters while keeping the same size of the optimal repeater size. Experimental results are shown for different frequencies and repeater sizes to indicate the improvements of slew rate and saving area overheard.
An error distribution based nonlinear companding method for analog behavioral modeling via wavelet approximation

In this paper, we develop a wavelet collocation method with nonlinear auto-companding for behavioral modeling of analog circuits. The companding function is automatically constructed according to the initial error distribution obtained through approximating the input-output function of the circuit blocks by conventional wavelet collocation method. Compared with the published modeling approaches, the proposed nonlinear auto-companding method works more efficiently not only in controlling the error distribution but also in reducing the modeling errors. A switched-current filter example is employed to demonstrate the promising features of the proposed method.
Power-optimal simultaneous buffer insertion/sizing and wire sizing

This paper studies the problems of minimizing power dissipation of an interconnect wire by simultaneously considering buffer insertion/sizing and wire sizing (BISWS). We consider two cases, namely minimizing power dissipation with optimal delay constraints, and minimizing power dissipation with a given delay penalty. We derive closed form optimal solutions for both cases. These closed form solutions can be used to efficiently estimate the power dissipation in the early stages of the VLSI designs. We observe that the power dissipation can be much different even with the same optimal delay.