nils-herrmann/clean_scirepeval huggingface dataset:
Finding Feasible Pathways in Metabolic Networks

Recent small-world studies of the global structure of metabolic networks have been based on the shortest-path distance. In this paper, we propose new distance measures that are based on the structure of feasible metabolic pathways between metabolites. We argue that these distances capture the complexity of the underlying biochemical processes more accurately than the shortest-path distance. To test our approach in practice, we calculated our distances and shortest-path distances in two microbial organisms, S. cerevisiae and E. coli. The results show that metabolite interconversion is significantly more complex than was suggested in previous small-world studies. We also studied the effect of reaction removals (gene knock-outs) on the connectivity of the S. cerevisiae network and found out that the network is not particularly robust against such mutations.
Planning isotopomer measurements for estimation of metabolic fluxes

Flux estimation by using isotopomer information of metabolites is currently the only method that can give quantitative estimates of the activity of metabolic pathways. However, the measurement of isotopomer distributions of intermediate metabolites is costly and tedious with current technologies. In this paper we study the question of finding the smallest subset of metabolites to measure that ensure an adequate level of the isotopomer information. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give exact and fast heuristic solutions and evaluate empirically the efficacy of the proposed methods.
Flow analysis of metabolite fragments for ux estimation

Flux estimation using isotopic tracers is currently the only method that can give quantitative estimated of the activity of metabolic pathways. The success of these methods, however, is intimately dependent on the quality and amount of data on isotopomer distributions of intermediate metabolites. In this paper we present a novel method for discovering sets of metabolite fragments that always have identical isotopomer distributions, regardless of the velocities of the reactions in the metabolic network. Our preliminary computational experiments indicate that the sets can help in the propagation of isotopomer data in the network and thus ease the requirements posed to measurement technology. The sets have potential of being useful in other tasks of flux estimation, too.
Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes

Many learning situations involve separation of labeled training instances by hyperplanes. Consistent separation is of theoretical interest, but the real goal is rather to minimize the number of errors using a bounded number of hyperplanes. Exact minimization of empirical error in a high-dimensional grid induced into the feature space by axis-parallel hyperplanes is NP-hard. We develop two approximation schemes with performance guarantees, a greedy set covering scheme for producing a consistently labeled grid, and integer programming rounding scheme for finding the minimum error grid with bounded number of hyperplanes.
Isotopomer distribution computation from tandem mass spectrometric data with overlapping fragment spectra

We present a method for determination of the isotopomer distributions of metabolites from the data generated by a tandem mass spectrometer. The method is an improvement over existing method as it is able to deal with overlapping fragments in the spectra. Our experiments indicate that the new method surpasses its predecessors in separating isotopomers from each other. When using the daughter ion scanning (collision induced dissociation) mode, the method was shown to be able to constrain the isotopomer distribution of different amino acids better than two existing methods. In particular, the isotopomer distributions of three amino acids, glycine, alanine and serine, can be fully uncovered with the method. However, due to the imperfect fragmentation of molecules in the tandem mass spectrometer, isotopomer distributions of larger amino acids still cannot be fully uncovered. In tests with isotope-labelled alanine, most accurate results were obtained using multiple reaction monitoring and 15 eV collision energy. The meausured isotopomer frequecies were in the range 99-106% of the theoretical value and the deviation between repetitions was in the range 1-10%.
General and Efficient Multisplitting of Numerical Attributes

Often in supervised learning numerical attributes require special treatment and do not fit the learning scheme as well as one could hope. Nevertheless, they are common in practical tasks and, therefore, need to be taken into account. We characterize the well-behavedness of an evaluation function, a property that guarantees the optimal multi-partition of an arbitrary numerical domain to be defined on boundary points. Well-behavedness reduces the number of candidate cut points that need to be examined in multisplitting numerical attributes. Many commonly used attribute evaluation functions possess this property; we demonstrate that the cumulative functions Information Gain and Training Set Error as well as the non-cumulative functions Gain Ratio and Normalized Distance Measure are all well-behaved. We also devise a method of finding optimal multisplits efficiently by examining the minimum number of boundary point combinations that is required to produce partitions which are optimal with respect to a cumulative and well-behaved evaluation function. Our empirical experiments validate the utility of optimal multisplitting: it produces constantly better partitions than alternative approaches do and it only requires comparable time. In top-down induction of decision trees the choice of evaluation function has a more decisive effect on the result than the choice of partitioning strategy; optimizing the value of most common attribute evaluation functions does not raise the accuracy of the produced decision trees. In our tests the construction time using optimal multisplitting was, on the average, twice that required by greedy multisplitting, which in its part required on the average twice the time of binary splitting.
APPLYING MACHINE LEARNING METHODS IN STUDYING RELATIONSHIPS BETWEEN MOUTHFEEL AND MICROSTRUCTURE OF OAT BREAD

The aim was to study whether machine learning can be applied in analysing microstructure of food from microscopy images and whether the state of macromolecules, protein and starch, can be related to sensory mouthfeel. Addition of gluten and transglutaminase modified the microstructure and mouthfeel of five oat bread samples. Light microscopy, instrumental texture profile analysis and descriptive sensory analysis were used to analyse the test breads. Digital image analysis was used to obtain numerical data on starch and protein phases in the breads. Degree of starch gelatinization and protein network properties were extracted from microscopy images by expert ratings. Many of the bread sensory properties could be predicted using instrumental texture and image analysis features. Machine learning of degree of starch gelatinization and protein network structure properties from expert classified microscope images was studied. Color histograms and co-occurrence matrix statistics were suitable preprocessing techniques for the images. In both starch gelatinization and protein network structure classification, the prediction error of an induced model tree was lower than the standard deviation between independent predictions given by experts.
Linear-Time Preprocessing in Optimal Numerical Range Partitioning

Only a subset of the boundary points—the segment borders—have to be taken into account in searching for the optimal multisplit of a numerical value range with respect to the most commonly used attribute evaluation functions of classification learning algorithms. Segments and their borders can be found efficiently in a linear-time preprocessing step.In this paper we expand the applicability of segment borders by showing that inspecting them alone suffices in optimizing any convex evaluation function. For strictly convex evaluation functions inspecting all segment borders is also necessary. These results are derived directly from Jensen's inequality.We also study the evaluation function Training Set Error which is not strictly convex. With that function the data can be preprocessed into an even smaller number of cut point candidates, called alternations, when striving for optimal partition. Examining all alternations also seems necessary, since—analogously to strictly convex functions—the placement of neighboring cut points affects the optimality of an alternation. We test empirically the reduction of the number of cut point candidates that can be obtained for Training Set Error on real-world data.
Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates

We consider multisplitting of numerical value ranges, a task that is encountered as a discretization step preceding induction and also embedded into learning algorithms. We are interested in finding the partition that optimizes the value of a given attribute evaluation function. For most commonly used evaluation functions this task takes quadratic time in the number of potential cut points in the numerical range. Hence, it is a potential bottleneck in data mining algorithms.We present two techniques that speed up the optimal multisplitting task. The first one aims at discarding cut point candidates in a quick linear-time preprocessing scan before embarking on the actual search. We generalize the definition of boundary points by Fayyad and Irani to allow us to merge adjacent example blocks that have the same relative class distribution. We prove for several commonly used evaluation functions that this processing removes only suboptimal cut points. Hence, the algorithm does not lose optimality.Our second technique tackles the quadratic-time dynamic programming algorithm, which is the best schema for optimizing many well-known evaluation functions. We present a technique that dynamically—i.e., during the search—prunes partitions of prefixes of the sorted data from the search space of the algorithm. The method works for all convex and cumulative evaluation functions.Together the use of these two techniques speeds up the multisplitting process considerably. Compared to the baseline dynamic programming algorithm the speed-up is around 50 percent on the average and up to 90 percent in some cases. We conclude that optimal multisplitting is fully feasible on all benchmark data sets we have encountered.
A Method for Estimating Metabolic Fluxes from Incomplete Isotopomer Information

Metabolic flux estimation--the problem of finding out the rates of reactions in metabolic pathways--is an important problem area in the study of metabolism. The most accurate technique for this task today is the use of isotopic tracer experiments, where a mixture of differently isotope-labeled substrates is fed to a cell culture and the propagation of the labels is observed from the products and intermediate metabolites, where possible. We present a generic methodology for solving the fluxes of a metabolic network in a steady state. The method differs from most previous approaches by not making prior assumptions about the topology of the metabolic network. Also, only very mild assumptions are made about the available measurement data, for example, both positional enrichment and mass isotopomer data can be used.
Necessary and Sufficient Pre-processing in
Numerical Range Discretization

The time complexities of class-driven numerical range discretization algorithms
depend on the number of cut point candidates. Previous analysis has shown that only a
subset of all cut points - the segment borders - have to be taken into account in optimal
discretization with respect to many goodness criteria. In this paper we show that inspecting
segment borders alone suffices in optimizing any convex evaluation function. For strictly
convex evaluation functions inspecting all of them also is necessary, since the placement
of neighboring cut points affects the optimality of a segment border. With the training set
error function, which is not strictly convex, it suffices to inspect an even smaller set of
cut point candidates, called alternations, when striving for optimal partition. On the other
hand, we prove that failing to check an alternation may lead to suboptimal discretization.
We present a linear-time algorithm for finding all alternation points. The number of
alternation points is typically much lower than the total number of cut points. In our
experiments running the discretization algorithm over the sequence of alternation points
led to a significant speed-up.
Computing positional isotopomer distributions from tandem mass spectrometric data.

The isotopomer distributions of metabolites are invaluable pieces of information in the computation of the flux distribution in a metabolic network. We describe the use of tandem mass spectrometry with the daughter ion scanning technique in the discovery of positional isotopomer distributions (PID). This technique increases the possibilities of mass spectrometry since given the same fragment ions, it uncovers more information than the full scanning mode. The mathematics of the new technique is slightly more complicated than the techniques needed by full scanning mode methods. Our experiments, however, show that in practice the inadequacy of the fragmentation of amino acids in the tandem mass spectrometer does not allow uncovering the PID exactly even if the daughter ion scanning is used. The computational techniques have been implemented in a MATLAB application called PIDC (Positional Isotopomer Distribution Calculator).
Model-Based Reasoning About Cases

Our bioprocess recipe planner Sophist (Aarts & Rousu, 1996) uses a qualitative model to represent a large amount of domain knowledge. This knowledge is used to analyse differences between cases, to construct adaptations, to evaluate suggested plan changes and to explain outcomes. The module for Qualitative Modeling in a CBR system (QMC) was especially developed to support these CaseBased Reasoning tasks. Hence, QMC is tightly integrated in the Sophist framework. Both Sophist (Aarts & Rousu, 1996, Rousu & Aarts, 1996) as well as QMC (Aarts & Rousu, 1997) have been described earlier so this paper briefly describes planning in Sophist and then focuses on some interesting issues that arose during development and deployment of this multimodal reasoner.
A Neural Network Tool for Brewery Fermentations

Fermentation is an important, if not the most impor tant process step in the production of beer. It is subject to alterations stemming from the variation in the yeas t, living organism, and due to the complex raw ma teri ls of biological origin. The variability may manifest in changes in the fermentation speed or cause off-flav ors in beer. Thus, there is a need for tools, both analytical an d computational, to help in monitoring the process and keeping it in a desired course. In this paper we describe a prediction tool to assist production management in the brewery. The system relies on a neural network that predicts the course of the fermentation based on yeast hist ory, fermentation recipe and raw material variables. Th e system is able to predict the fermentation time w ith an average error of 10 percent of the remaining fermen tation time, which enables the brewery to spot prob lematic batches days in advance and plan the correct yeast harvesting time with an increased accuracy. The sys tem has been in daily trial use in Hartwall’s brewery in La hti since March 2000 and the user experiences have been positive.
Preprocessing opportunities in optimal numerical range partitioning

We show that only segment borders have to be taken into account as cut point candidates when searching for the optimal multisplit of a numerical value range with respect to convex attribute evaluation functions. Segment borders can be found efficiently in a linear-time preprocessing step. With training set error, which is not strictly convex, the data can be preprocessed into an even smaller number of cut point candidates, called alternations, when striving for the optimal partition. We show that no segment borders (resp. alternations) can be overlooked with strictly convex functions (resp. training set error) without risking the loss of optimality. Our experiments show that while in real-world domains a significant reduction in the number of cut point candidates can be obtained for training set error, the number of segment borders is usually not much lower than that of boundary points.
An integrated approach to bioprocess recipe design

Biotechnical and food process industries have a need for tools that support the implementation of modern manufacturing practice. Flexible production strategies and total quality management require efficient design of the process recipes. 
 
This paper presents the approach used in Sophist, an adaptive recipe planning system. The system integrates case-based reasoning and a semi-qualitative process model for the design and optimisation of bioprocess recipes. In particular, we introduce a novel revision selection algorithm that improve the system's adaptivity. The behavior of the planner is exemplified by a batch fermentation planning experiment.
Efficient Range Partitioning in Classification Learning

Partitioning of data is the essence of many machine learning and data mining methods. It is particularly important in classiication learning tasks, where the aim is to induce rules, decision trees or network structures that separate instances of diierent classes. This thesis examines the problem of partitioning ordered value ranges into two or more subsets, optimally with respect to an evaluation function. This task is encountered in the induction of multisplitting decision trees and, in many learning paradigms, as a data preprocessing stage preceding the actual learning phase. The goal of the partitioning in preprocessing is to transform the data into a form that better suits the learning algorithm or to decrease the resource-demands of the algorithm; the handling of numerical values during learning is often the bottleneck in time consumption. No polynomial-time algorithm is known for the range partitioning task in the general case, which has led to a number of heuristic approaches. These methods are fast and some of them produce good|but sub-optimal|partitions in practice. We study ways to make optimal partitioning more feasible in terms of time-complexity. The approach taken in this study is to take advantage of the general properties of the evaluation functions to decrease the computational demands. We show that many commonly used evaluation functions obtain their minima on a well-deened subset of all cut point combinations. This subset can be identiied in a linear-time preprocessing step. The size of the subset is not directly dependent on the size of the dataset but on the diversity of the class distributions along the numerical range. Restricting the class of evaluation functions enables quadratic-or cubic-time evaluation over the preprocessed sequence by dynamic programming. We i introduce a pruning technique that let us speed up the algorithms further. In our tests on a large number of publicly available datasets, the average speed up from these improvements was over 50% of the running-time. As an application, we consider the induction of multisplitting decision trees. We present a comprehensive experimental comparison between the binary splitting, optimal multisplitting and heuristic multisplitting strategies using two well-known evaluation functions. We examine ways to postpone the evaluation of seemingly irrelevant attributes to a later stage, in order to further improve the eeciency of the tree induction. Our main conclusion from these studies is that generating optimal multisplits during tree induction is feasible. However, the predictive accuracy of decision trees only marginally depends on …
On the Computational Complexity of Optimal Multisplitting

The need to partition or discretize numeric value ranges arises in machine learning and data mining algorithms. This subtask is a potential time-consumption bottleneck, since the number of candidate partitions is exponential in the number of possible cut points in the range. Thus, many heuristic algorithms have been proposed for this task. Recently, the efficiency of optimal multisplitting has improved dramatically, due to the introduction of linear-time algorithms for training error minimization and quadratic-time generic algorithms. Whether these efficient algorithms are the best obtainable, is not yet known. In this paper, we probe the inherent complexity of the multisplitting problem. We reflect results obtained for similar problems in computational geometry and string matching to the multisplitting task. Subquadratic optimization algorithms in computational geometry rely on the monotonicity of the optimized function. We show by counterexamples that the widely used evaluation functions Training Set Error and Average Class Entropy do not satisfy the kind of monotonicity that facilitates subquadratic-time optimization. However, we also show that the Training Set Error function can be decomposed into monotonic subproblems, one per class, which explains its linear time optimization. Finally, we review recently developed techniques for speeding up optimal multisplitting.
Uses of Convexity in Numerical Domain Partitioning

We study the diierent manifestations of convexity in classiier-constructing machine learning algorithms. Two principal ways to apply the consequences of the convexity of the evaluation function to enhance numerical domain partitioning exist: it enables static and dynamic pruning of partition candidates. Numerical attribute handling is a potential time-consumption bottleneck in classiier learning. Therefore, speeding it up is important for the practical utility of machine learning algorithms. In extensive empirical evaluation we review the utility of static and dynamic pruning of partition candidates. We test for 28 UCI test domains the speed-up gained by both approaches separately and their combined eeect. All pruning methods are able to enhance the eeciency of optimal numerical attribute partitioning. Best results are obtained by combining static and dynamic pruning.
Generalizing Boundary Points

The complexity of numerical domain partitioning depends on the number of potential cut points. In multiway partitioning this dependency is often quadratic, even exponential. Therefore, reducing the number of candidate cut points is important. For a large family of attribute evaluation functions only boundary points need to be considered as candidates. We prove that an even more general property holds for many commonly-used functions. Their optima are located on the borders of example segments in which the relative class frequency distribution is static. These borders are a subset of boundary points. Thus, even less cut points need to be examined for these functions. The results shed a new light on the splitting properties of common attribute evaluation functions and they have practical value as well. The functions that are examined also include non-convex ones. Hence, the property introduced is not just another consequence of the convexity of a function.