nils-herrmann/clean_scirepeval huggingface dataset:
Margin Maximizing Loss Functions

Margin maximizing properties play an important role in the analysis of classification models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it facilitates generalization error analysis, and practically interesting because it presents a clear geometric interpretation of the models being built. We formulate and prove a sufficient condition for the solutions of regularized loss functions to converge to margin maximizing separators, as the regularization vanishes. This condition covers the hinge loss of SVM, the exponential loss of AdaBoost and logistic regression loss. We also generalize it to multi-class classification problems, and present margin maximizing multi-class versions of logistic regression and support vector machines.
Customer lifetime value modeling and its use for customer retention planning

We present and discuss the important business problem of estimating the effect of retention efforts on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We then describe how we build on this approach to estimate the effects of retention on Lifetime Value. Our solution has been successfully implemented in Amdocs' Business Insight (BI) platform, and we illustrate its usefulness in real-world scenarios.
Evaluation of prediction models for marketing campaigns

We consider prediction-model evaluation in the context of marketing-campaign planning. In order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. These should portray the model's ability to score accurately and to identify the relevant target population. In this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. We illustrate our results with a case study based on our experience from several projects.
KDD-cup 99: knowledge discovery in a charitable organization's donor database

1. I N T R O D U C T I O N 2. This report describes the results of our knowledge discovery and modeling on the data of the 1997 donation campaign of an 1. American charitable organization. The two data sets (training and evaluation) contained about 95000 customers each, with an average net donation of slightly over 11 cents per customer, hence a total net donation of around $10500 results from the "mail to all" policy. The main tool we utilized for the knowledge discovery task is 2. Amdocs' Information Analysis Environment, which allows standard 2-class knowledge discovery and modeling, but also Value Weighted Analysis (VWA). In VWA, the discovered segments and models attempt to optimize the value and class membership simultaneously. Thus, our modeling was based on a 1-stage model rather than a separate analysis for donation probability and expected donation (the approach taken by all of KDD-Cup 98's reported modeling efforts except our own). 3. We concentrate the first two parts of the report on introducing the knowledge and models we have discovered. The third part deals with the methods, algorithms and comments about the results. In doing the analysis and modeling we used only the training data set of KDD-Cup 98, reserving the evaluation data set for final unbiased model evaluation for our 5 suggested models only. If our goal had been only knowledge discovery, it might have been useful to utilize the evaluation data too, especially the donors. It is probably possible to find more interesting phenomena with almost 10000 donors than with under 5000.
KDD-Cup 2000: question 5 winnner's report Amdocs

Z. METHODOLOGY AND ALGORITHMS This is a knowledge discovery problem and it was important to use techniques that reveal highly interpretable phenomena. The primary tool we used is the Amdoes Business Insight (BI) Tool. It supports both Knowledge Discovery and Predictive Modeling where for this task the former was much more prominent. In addition we used the Splus tool for Exploratory Data Analysis (EDA) purposes.
KDD-Cup 2000: question 1 winner's report

1. THE KDD-CUP QUESTION The training database contained 234,954 sessions, some complete and some clipped. We were asked to build a prediction model to describe which sessions are going to continue and which are about to terminate, and use tMs model to predict the outcomes on a test dataset (where the information on whether the sessions were clipped or not was withheld). The prediction problem was particularly difficult for several reasons, most notably:
Discovery of fraud rules for telecommunications—challenges and solutions

Many fraud analysis systems have at their heart a rule-based engine for generating alerts about suspicious behaviors. The rules in the system are usually based on expert knowledge. Automatic rule discovery aims at using past examples of fraudulent and legitimate usage to find new patterns and rules to help distinguish between the two. Some aspects of the problem of finding rules suitable for fraud analysis make this problem unique. Among them are the following: the need to find rules combining both the properties of the customer (e.g., credit rating) and properties of the specific “behavior” which indicates fraud (e.g., number of international calls in one day); and the need for a new definition of accuracy: We need to find rules which do not necessarily classify correctly each individual “usage sample” as either fraudulent or not, but ensure the identification, with a minimum of wasted cost and effort, of most of the fraud “cases” (i.e., defrauded customers). These aspects require a special-purpose rule discovery system. We present as an example a two-stage system based on adaptation of the C4.5 rule generator, with an additional rule selection mechanism. Our experimental results indicate that this route is very promising.
Ranking - Methods for Flexible Evaluation and Efficient Comparison of Classification Performance

We present the notion of Ranking for evaluation of two-class classifiers. Ranking is based on using the ordering information contained in the output of a scoring model, rather than just setting a classification threshold. Using this ordering information, we can evaluate the model's performance with regard to complex goal functions, such as the cor -rect identification of the k most likely and/or least likely to be responders out of a group of potential customers. Using Ranking we can also obtain increased efficiency in comparing classifiers and selecting the better one even for the standard goal of achieving a minimal misclassification rate. This feature of Ranking is illustrated by simulation results. We also discuss it theoretically, showing the similarity in structure between the reducible (model dependent) parts of the Linear Ranking score and the standard Misclassification Rate score, and characterizing the situations when we expect Linear Ranking to outperform Misclassification Rate as a method for model discrimination.
Large margin non-linear embedding

It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, we "learn" the location of the data. This way we (i) do not need a metric (or even stronger structure) - pairwise dissimilarities suffice; and additionally (ii) produce low-dimensional embeddings that can be analyzed visually. We achieve this by combining an entropy-based embedding method with an entropy-based version of semi-supervised logistic regression. We present results for clustering and semi-supervised classification.
Phenotypic Characterization of Human Chondrocyte Cell Line C-20/A4: A Comparison between Monolayer and Alginate Suspension Culture

DNA microarray analysis was used to investigate the molecular phenotype of one of the first human chondrocyte cell lines, C-20/A4, derived from juvenile costal chondrocytes by immortalization with origin-defective simian virus 40 large T antigen. Clontech Human Cancer Arrays 1.2 and quantitative PCR were used to examine gene expression profiles of C-20/A4 cells cultured in the presence of serum in monolayer and alginate beads. In monolayer cultures, genes involved in cell proliferation were strongly upregulated compared to those expressed by human adult articular chondrocytes in primary culture. Of the cell cycle-regulated genes, only two, the CDK regulatory subunit and histone H4, were downregulated after culture in alginate beads, consistent with the ability of these cells to proliferate in suspension culture. In contrast, the expression of several genes that are involved in pericellular matrix formation, including MMP-14, COL6A1, fibronectin, biglycan and decorin, was upregulated when the C-20/A4 cells were transferred to suspension culture in alginate. Also, nexin-1, vimentin, and IGFBP-3, which are known to be expressed by primary chondrocytes, were differentially expressed in our study. Consistent with the proliferative phenotype of this cell line, few genes involved in matrix synthesis and turnover were highly expressed in the presence of serum. These results indicate that immortalized chondrocyte cell lines, rather than substituting for primary chondrocytes, may serve as models for extending findings on chondrocyte function not achievable by the use of primary chondrocytes.
[cDNA-microarrays in cartilage research - functional genomics of osteoarthritis].

Functional genomics represents a new challenging approach in order to analyze complex diseases such as osteoarthritis on a molecular level. The characterization of the molecular changes of the cartilage cells, the chondrocytes, enables a better understanding of the pathomechanisms of the disease. In particular, the identification and characterization of new target molecules for therapeutic intervention is of interest. Also, potential molecular markers for diagnosis and monitoring of osteoarthritis contribute to a more appropriate patient management. The DNA-microarray technology complements (but does not replace) biochemical and biological research in new disease-relevant genes. Large-scale functional genomics will identify molecular networks such as yet identified players in the anabolic-catabolic balance of articular cartilage as well as disease-relevant intracellular signaling cascades so far rather unknown in articular chondrocytes. However, at the moment it is also important to recognize the limitations of the microarray technology in order to avoid over-interpretation of the results. This might lead to misleading results and prevent to a significant extent a proper use of the potential of this technology in the field of osteoarthritis.
cDNA-Microarray Technology in Cartilage Research - Functional Genomics of Osteoarthritis [in German]

Functional genomics represents a new challenging approach in order to analyze complex diseases such as osteoarthritis on a molecular level. The characterization of the molecular changes of the cartilage cells, the chondrocytes, enables a better understanding of the pathomechanisms of the disease. In particular, the identification and characterization of new target molecules for therapeutic intervention is of interest. Also, potential molecular markers for diagnosis and monitoring of osteoarthritis contribute to a more appropriate patient management. The DNA-microarray technology complements (but does not replace) biochemical and biological research in new disease-relevant genes. Large-scale functional genomics will identify molecular networks such as yet identified players in the anabolic-catabolic balance of articular cartilage as well as disease-relevant intracellular signaling cascades so far rather unknown in articular chondrocytes. However, at the moment it is also important to recognize the limitations of the microarray technology in order to avoid over-interpretation of the results. This might lead to misleading results and prevent to a significant extent a proper use of the potential of this technology in the field of osteoarthritis.
Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis.

The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.
A primer on molecular biology

Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (“compartmentalization”); (3) the way cells produce proteins (“protein expression”); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined.
Microarrays: How Many Do You Need?

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.
Molecular phenotyping of human chondrocyte cell lines T/C-28a2, T/C-28a4, and C-28/I2.

Because the immortalized chondrocyte cell lines C-28/I2, T/C-28a2, and T/C-28a4 have become a common tool in cartilage research, permitting investigations in a largely unlimited and standardized manner, we investigated the molecular phenotype of these cell lines by gene expression profiling.



Complementary DNA-array analysis as well as online quantitative polymerase chain reaction were used to identify the gene expression profiles of the 3 cell lines cultured in monolayer and alginate beads, as compared with the expression profiles of cultured human adult primary chondrocytes.



A similar, but not identical, gene expression profile was established for all 3 cell lines. SOX9 was expressed at a significant level in all 3 cell lines. Extracellular matrix proteins and matrix-degrading proteases were rarely expressed. In contrast, genes involved in the cell cycle were strongly up-regulated, as compared with the expression levels in physiologic chondrocytes.



The expression of SOX9, the master gene of chondrocytic cell differentiation, reflects the basically chondrocytic phenotype of these cells. However, the major issue appears to be that these cell lines mainly proliferate and show less expression of genes involved in matrix synthesis and turnover. In this respect, C-28/I2 cells display the highest levels of matrix-anabolic and matrix-catabolic genes and thus are presumably preferable for use in investigating chondrocyte anabolic and catabolic activity and its regulation. None of the 3 cell lines appears to be a direct substitute for primary chondrocytes. A successful approach will have to validate the findings obtained with chondrocyte cell lines by using primary chondrocytes or cartilage-tissue cultures. This would permit the establishment of reproducible in vitro models and subsequently allow investigators to relate the findings to the physiologic situation.
YKL-39 (chitinase 3-like protein 2), but not YKL-40 (chitinase 3-like protein 1), is up regulated in osteoarthritic chondrocytes

To investigate quantitatively the mRNA expression levels of YKL-40, an established marker of rheumatoid and osteoarthritic cartilage degeneration in synovial fluid and serum, and a closely related molecule YKL-39, in articular chondrocytes.  cDNA array and online quantitative polymerase chain reaction (PCR) were used to measure mRNA expression levels of YKL-39 and YKL-40 in chondrocytes in normal, early degenerative, and late stage osteoarthritic cartilage samples.  Expression analysis showed high levels of both proteins in normal articular chondrocytes, with lower levels of YKL-39 than YKL-40. Whereas YKL-40 was significantly down regulated in late stage osteoarthritic chondrocytes, YKL-39 was significantly up regulated. In vitro both YKLs were down regulated by interleukin 1β.  The up regulation of YKL-39 in osteoarthritic cartilage suggests that YKL-39 may be a more accurate marker of chondrocyte activation than YKL-40, although it has yet to be established as a suitable marker in synovial fluid and serum. The decreased expression of YKL-40 by osteoarthritic chondrocytes is surprising as increased levels have been reported in rheumatoid and osteoarthritic synovial fluid, where it may derive from activated synovial cells or osteophytic tissue or by increased matrix destruction in the osteoarthritic joint. YKL-39 and YKL-40 are potentially interesting marker molecules for arthritic joint disease because they are abundantly expressed by both normal and osteoarthritic chondrocytes.
Correlated stage‐ and subfield‐associated hippocampal gene expression patterns in experimental and human temporal lobe epilepsy

Epileptic activity evokes profound alterations of hippocampal organization and function. Genomic responses may reflect immediate consequences of excitatory stimulation as well as sustained molecular processes related to neuronal plasticity and structural remodeling. Using oligonucleotide microarrays with 8799 sequences, we determined subregional gene expression profiles in rats subjected to pilocarpine‐induced epilepsy (U34A arrays, Affymetrix, Santa Clara, CA, USA; P < 0.05, twofold change, n = 3 per stage). Patterns of gene expression corresponded to distinct stages of epilepsy development. The highest number of differentially expressed genes (dentate gyrus, approx. 400 genes and CA1, approx. 700 genes) was observed 3 days after status epilepticus. The majority of up‐regulated genes was associated with mechanisms of cellular stress and injury – 14 days after status epilepticus, numerous transcription factors and genes linked to cytoskeletal and synaptic reorganization were differentially expressed and, in the stage of chronic spontaneous seizures, distinct changes were observed in the transcription of genes involved in various neurotransmission pathways and between animals with low vs. high seizure frequency. A number of genes (n = 18) differentially expressed during the chronic epileptic stage showed corresponding expression patterns in hippocampal subfields of patients with pharmacoresistant temporal lobe epilepsy (n = 5 temporal lobe epilepsy patients; U133A microarrays, Affymetrix; covering 22 284 human sequences). These data provide novel insights into the molecular mechanisms of epileptogenesis and seizure‐associated cellular and structural remodeling of the hippocampus.
Gene Expression in Chondrocytes Assessed with Use of Microarrays

Despite considerable limitations such as low sensitivity and insensitivity to alternative splicing, posttranscriptional regulation, and posttranslational modification, cDNA array technology provides a powerful tool with which to obtain an overview of gene expression patterns, hardly achievable with other techniques. This has been shown to be true for the analysis of known genes as well as the discovery of new genes of interest. Samples of normal and late-stage osteoarthritic cartilage of human knee joints were analyzed with use of the Human Cancer 1.2 cDNA-array and TaqMan analysis. In spite of a large variability of expression levels among different patients, significant expression patterns for many known genes of interest such as cartilage matrix proteins (e.g., collagen types II, VI, and XI; aggrecan; decorin; biglycan) and matrix-degrading proteases were detected. Of the latter, MMP-3 appeared to be strongly expressed in normal and early degenerative cartilage and downregulated in the late disease stages. This indicates that, in the late stages of cartilage degeneration, other degradation pathways might be more important, for example, those involving enzymes such as MMP-2 and MMP-13, both of which were upregulated in late-stage disease. Most results have to be considered to be preliminary to a certain degree, as technical tools and interpretation approaches are still emerging and need more validation. Clearly, there is a major challenge to distill information and knowledge out of the obtained mass of data. However, these data will be one basis of a new world of biological understanding. These new insights will be network-based and no longer molecule-centered. Today, molecules have a biochemical and physiological context; tomorrow, biological networks will have molecules as constituents.
Microarrays: how many do you need?

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. Current knowlegde seems to suffice for the construction of models that are realistic with respect to searches for individual differentially expressed genes. Such models allow to investigate the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class; the fold changes in expression; the detection sensitivity of the microarrays; and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at http://cartan.gmd.de/~zien/classsize/ for fine tuning simulations to their particular actualities. Since the situation can be assumed to be very similar for large scale proteomics and metabolomics studies, our methods and results might also apply there.