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PubMed2005

Automatic processing of spoken dialogue in the home hemodialysis domain.

Spoken medical dialogue is a valuable source of information, and it forms a foundation for diagnosis, prevention and therapeutic management. However, understanding even a perfect transcript of spoken dialogue is challenging for humans because of the lack of structure and the verbosity of dialogues. This work presents a first step towards automatic analysis of spoken medical dialogue. The backbone of our approach is an abstraction of a dialogue into a sequence of semantic categories. This abstraction uncovers structure in informal, verbose conversation between a caregiver and a patient, thereby facilitating automatic processing of dialogue content. Our method induces this structure based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our model has a classification accuracy of 73%, compared to 33% achieved by a majority baseline (p<0.01). This work demonstrates the feasibility of automatically processing spoken medical dialogue.

Algorithms↗
PubMed2003

Metabolic fingerprinting of salt-stressed tomatoes.

The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.

Algorithms↗
PubMedDate not supplied

User-requirements driven learning.

This paper describes an approach for deriving classification knowledge from databases, taking into account user preferences. These preferences especially concern the trade-off between different kinds of costs and performance indicators of the classification scheme to be developed. We analyze what knowledge, provided by the user, can be used at various stages of the machine learning process to influences the development of the classifier. We restrict ourselves in this paper mainly to the generation of classification trees.

Algorithms↗
PubMedDate not supplied

Data mining the NCI cancer cell line compound GI(50) values: identifying quinone subtypes effective against melanoma and leukemia cell classes.

Using data mining techniques, we have studied a subset (1400) of compounds from the large public National Cancer Institute (NCI) compounds data repository. We first carried out a functional class identity assignment for the 60 NCI cancer testing cell lines via hierarchical clustering of gene expression data. Comprised of nine clinical tissue types, the 60 cell lines were placed into six classes-melanoma, leukemia, renal, lung, and colorectal, and the sixth class was comprised of mixed tissue cell lines not found in any of the other five classes. We then carried out supervised machine learning, using the GI(50) values tested on a panel of 60 NCI cancer cell lines. For separate 3-class and 2-class problem clustering, we successfully carried out clear cell line class separation at high stringency, p < 0.01 (Bonferroni corrected t-statistic), using feature reduction clustering algorithms embedded in RadViz, an integrated high dimensional analytic and visualization tool. We started with the 1400 compound GI(50) values as input and selected only those compounds, or features, significant in carrying out the classification. With this approach, we identified two small sets of compounds that were most effective in carrying out complete class separation of the melanoma, non-melanoma classes and leukemia, non-leukemia classes. To validate these results, we showed that these two compound sets' GI(50) values were highly accurate classifiers using five standard analytical algorithms. One compound set was most effective against the melanoma class cell lines (14 compounds), and the other set was most effective against the leukemia class cell lines (30 compounds). The two compound classes were both significantly enriched in two different types of substituted p-quinones. The melanoma cell line class of 14 compounds was comprised of 11 compounds that were internal substituted p-quinones, and the leukemia cell line class of 30 compounds was comprised of 6 compounds that were external substituted p-quinones. Attempts to subclassify melanoma or leukemia cell lines based upon their clinical cancer subtype met with limited success. For example, using GI(50) values for the 30 compounds we identified as effective against all leukemia cell lines, we could subclassify acute lymphoblastic leukemia (ALL) origin cell lines from non-ALL leukemia origin cell lines without significant overlap from non-leukemia cell lines. Based upon clustering using GI(50) values for the 60 cancer cell lines laid out by the RadViz algorithm, these two compound subsets did not overlap with clusters containing any of the NCI's 92 compounds of known mechanism of action, a few of which are quinones. Given their structural patterns, the two p-quinone subtypes we identified would clearly be expected to possess different redox potentials/substrate specificities for enzymatic reduction in vivo. These two p-quinone subtypes represent valuable information that may be used in the elucidation of pharmacophores for the design of compounds to treat these two cancer tissue types in the clinic.

Algorithms↗
PubMed2006

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Abscisic Acid↗
PubMed2004

Information geometry of U-Boost and Bregman divergence.

We aim at an extension of AdaBoost to U-Boost, in the paradigm to build a stronger classification machine from a set of weak learning machines. A geometric understanding of the Bregman divergence defined by a generic convex function U leads to the U-Boost method in the framework of information geometry extended to the space of the finite measures over a label set. We propose two versions of U-Boost learning algorithms by taking account of whether the domain is restricted to the space of probability functions. In the sequential step, we observe that the two adjacent and the initial classifiers are associated with a right triangle in the scale via the Bregman divergence, called the Pythagorean relation. This leads to a mild convergence property of the U-Boost algorithm as seen in the expectation-maximization algorithm. Statistical discussions for consistency and robustness elucidate the properties of the U-Boost methods based on a stochastic assumption for training data.

Algorithms↗
PubMed1999

Boosted mixture of experts: an ensemble learning scheme.

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classifical ensemble approaches.

Algorithms↗
PubMed2006

A fuzzy discrete event system approach to determining optimal HIV/AIDS treatment regimens.

Treatment decision-making is complex and involves many factors. A systematic decision-making and optimization technology capable of handling variations and uncertainties of patient characteristics and physician's subjectivity is currently unavailable. We recently developed a novel general-purpose fuzzy discrete event systems theory for optimal decision-making. We now apply it to develop an innovative system for medical treatment, specifically for the first round of highly active antiretroviral therapy of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) patients involving three historically widely used regimens. The objective is to develop such a system whose regimen choice for any given patient will exactly match expert AIDS physician's selection to produce the (anticipated) optimal treatment outcome. Our regimen selection system consists of a treatment objectives classifier, fuzzy finite state machine models for treatment regimens, and a genetic-algorithm-based optimizer. The optimizer enables the system to either emulate an individual doctor's decision-making or generate a regimen that simultaneously satisfies diverse treatment preferences of multiple physicians to the maximum extent. We used the optimizer to automatically learn the values of 26 parameters of the models. The learning was based on the consensus of AIDS specialists A and B on this project, whose exact agreement was only 35%. The performance of the resulting models was first assessed. We then carried out a retrospective study of the entire system using all the qualifying patients treated in our institution's AIDS Clinical Center in 2001. A total of 35 patients were treated by 13 specialists using the regimens (four and eight patients were treated by specialists A and B, respectively). We compared the actually prescribed regimens with those selected by the system using the same available information. The overall exact agreement was 82.9% (29 out of 35), with the exact agreement with specialists A and B both at 100%. The exact agreement for the remaining 11 physicians not involved in the system training was 73.9% (17 out of 23), an impressive result given the fact that expert opinion can be quite divergent for treatment decisions of such complexity. Our specialists also carefully examined the six mismatched cases and deemed that the system actually chose a more appropriate regimen for four of them. In the other two cases, either would be reasonable choices. Our approach has the capabilities of generalizing, learning, and representing knowledge even in the face of weak consensus, and being readily upgradeable to new medical knowledge. These are practically important features to medical applications in general, and HIV/AIDS treatment in particular, as national HIV/AIDS treatment guidelines are modified several times per year.

Anti-HIV Agents↗
PubMed2006

Predicting cancer drug response by proteomic profiling.

PURPOSE: Accurate prediction of an individual patient's drug response is an important prerequisite of personalized medicine. Recent pharmacogenomics research in chemosensitivity prediction has studied the gene-drug correlation based on transcriptional profiling. However, proteomic profiling will more directly solve the current functional and pharmacologic problems. We sought to determine whether proteomic signatures of untreated cells were sufficient for the prediction of drug response. EXPERIMENTAL DESIGN: In this study, a machine learning model system was developed to classify cell line chemosensitivity exclusively based on proteomic profiling. Using reverse-phase protein lysate microarrays, protein expression levels were measured by 52 antibodies in a panel of 60 human cancer cell (NCI-60) lines. The model system combined several well-known algorithms, including random forests, Relief, and the nearest neighbor methods, to construct the protein expression--based chemosensitivity classifiers. The classifiers were designed to be independent of the tissue origin of the cells. RESULTS: A total of 118 classifiers of the complete range of drug responses (sensitive, intermediate, and resistant) were generated for the evaluated anticancer drugs, one for each agent. The accuracy of chemosensitivity prediction of all the evaluated 118 agents was significantly higher (P < 0.02) than that of random prediction. Furthermore, our study found that the proteomic determinants for chemosensitivity of 5-fluorouracil were also potential diagnostic markers of colon cancer. CONCLUSIONS: The results showed that it was feasible to accurately predict chemosensitivity by proteomic approaches. This study provides a basis for the prediction of drug response based on protein markers in the untreated tumors.

Antineoplastic Agents↗
PubMed2006

Machine learning approaches for estimation of prediction interval for the model output.

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

Algorithms↗
PubMed1999

Decision tree-based formation of consensus protein secondary structure prediction.

MOTIVATION: Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. METHOD: We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data. RESULTS: The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively.

Algorithms↗
PubMed2000

Assessing rbf networks using DELVE.

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods.

Algorithms↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗
PubMed1999

Neurofuzzy adaptive controlling of selective stimulation for FES: a case study.

A controller was designed for the selective stimulation of the sciatic nerve with a multiple contact cuff electrode to generate a desired torque in the ankle joint of cat. The design integrates three approaches, artificial neural network (ANN) modeling, fuzzy logical adaptation, and geometrical mapping. The geometrical mapping refers to the vector transformation from the joint coordinates to the virtual muscle coordinates which have been conceptually developed to represent the major recruitment features of contact-based functional units in the physical plant. This method reduces the complexity of generating a data set for training the neural network in the feedforward path and implementing the on-line learning algorithm embedded in the feedback loop. The controller was evaluated by computer simulation with the experimental data obtained from the torque generation in five acute cats. The results show that the ANN-based feedforward is capable of predicting 65% of a given desired isometric torque, and the fuzzy logical machine is able to provide suitable gains for feedback modulation to reduce the error from 35 to 8.5% and produce a robust control.

Animals↗
PubMed1998

Medical diagnostic system using Fuzzy Coloured Petri Nets under uncertainty.

We propose a medical diagnostic system using Fuzzy Coloured Petri Nets (FCPN) in this paper. For complex real-world knowledge Fuzzy Petri Net (FPN) models have been proposed to perform fuzzy reasoning automatically. However, in the Petri Net we have to represent all kinds of processes by separate subnets even though the process has the same behavior of other one. Real-world knowledge often contains many parts which are similar, but not identical. This means that the total PTN becomes very large. The kind of problems may be annoying for a small system, and it may be catastrophic for the description of large-scale system. To avoid this kind of problems we propose a learning and reasoning method using FCPNs under uncertainty. On the other hand to correct the rules of knowledge-based system hand-built classifier and empirical learning method both based on domain theory have been proposed as machine learning methods, where there is a significant gap between the knowledge-intensive approach in the former and the virtually knowledge-free approach in the later. To resolve such problems simultaneously we propose a hybrid learning method which is built on the top of knowledge-based FCPN and Genetic Algorithms (GA). To verify the validity and the effectiveness of the proposed system, we have successfully applied it to the diagnosis of intervertebral diseases.

Algorithms↗
PubMed1999

Improving the efficiency of a user-driven learning system with reconfigurable hardware. Application to DNA splicing.

This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The first one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on reconfigurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show how efficient the interaction between the machine and the expert is when the concept modelling is based on reconfigurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.

Algorithms↗
PubMed2005

Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

PURPOSE: To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). METHODS: Seventy-two eyes of 72 healthy control subjects (average age = 64.3 +/- 8.8 years, visual field mean deviation = -0.71 +/- 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 +/- 8.9 years, visual field mean deviation = -5.32 +/- 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6 degrees each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Ten-fold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). RESULTS: The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. CONCLUSIONS: Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis.

Aged↗