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PubMed2002

Building an asynchronous web-based tool for machine learning classification.

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not yet been used for this purpose, hence proper comparisons between classification methods have not been conducted. We developed free software that implements logistic regression with stepwise variable selection as a quick and simple method for initial exploration of important genetic markers in disease classification. To implement the algorithm and allow our collaborators in remote locations to evaluate and compare its results against those of other methods, we developed a user-friendly asynchronous web-based application with a minimal amount of programming using free, downloadable software tools. With this program, we show that classification using logistic regression can perform as well as other more sophisticated algorithms, and it has the advantages of being easy to interpret and reproduce. By making the tool freely and easily available, we hope to promote the comparison of classification methods. In addition, we believe our web application can be used as a model for other bioinformatics laboratories that need to develop web-based analysis tools in a short amount of time and on a limited budget.

Algorithms↗
PubMed2002

Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners.

MOTIVATION: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. RESULTS: We develop a new set of flexible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network implemantations of a class of Bayesian networks we call generalized input-output HMMs (GIOHMMs). For the specific case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of 8 A and 45% of distant contacts at 10 A, for proteins of length up to 300.

Algorithms↗
PubMed2005

Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks.

We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or ORFs (Open Reading Frames) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore many functional links would be missing when only one or two source of data is used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗
PubMed2004

Does computation provide a model for creativity? An epistemological perspective in neuroscience.

In 1939 Alan Turing, a major scholar in the field of mechanical computation, described a system whose computational power was beyond that of a discrete, finite state machine (Turing Machine). The composition of this system was likely the first example of what is now called an hybrid computational system. Since then, development of neural networks and brain automata has made aware that forms of computation might exist that are likely to go beyond Turing's limits. Natural systems, like the central nervous system in Mammals and man, are likely to use such a type of computation, especially to perform highly integrating activities, like feedback controls and mental creative processes. The latter are usually understood as processes that involve infinitary procedures, ending up in a complex information network, the computational maps, in which both digital, Turing-like computation and continuous, analog forms of calculus are expected to occur. Pictorial representation may be a fruitful example, mostly metaphorical, to analyze the use of this hybrid forms of computation by higher order computational maps, and the possible role of these types of computational processes in painting creativity is briefly analyzed in comparing 15th vs 16th century Renaissance Art. An open challenge for neuroscience in the 21st century is to clarify whether a hybrid neural learning network might represent a reasonable clue to scientifically interpret the theme of "creativity".

Art↗
PubMed2005

Challenges in real-life emotion annotation and machine learning based detection.

Since the early studies of human behavior, emotion has attracted the interest of researchers in many disciplines of Neurosciences and Psychology. More recently, it is a growing field of research in computer science and machine learning. We are exploring how the expression of emotion is perceived by listeners and how to represent and automatically detect a subject's emotional state in speech. In contrast with most previous studies, conducted on artificial data with archetypal emotions, this paper addresses some of the challenges faced when studying real-life non-basic emotions. We present a new annotation scheme allowing the annotation of emotion mixtures. Our studies of real-life spoken dialogs from two call center services reveal the presence of many blended emotions, dependent on the dialog context. Several classification methods (SVM, decision trees) are compared to identify relevant emotional states from prosodic, disfluency and lexical cues extracted from the real-life spoken human-human interactions.

Artificial Intelligence↗
PubMed2003

A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment.

In the present study, an attempt has been made to develop a method for predicting gamma-turns in proteins. First, we have implemented the commonly used statistical and machine-learning techniques in the field of protein structure prediction, for the prediction of gamma-turns. All the methods have been trained and tested on a set of 320 nonhomologous protein chains by a fivefold cross-validation technique. It has been observed that the performance of all methods is very poor, having a Matthew's Correlation Coefficient (MCC) </= 0.06. Second, predicted secondary structure obtained from PSIPRED is used in gamma-turn prediction. It has been found that machine-learning methods outperform statistical methods and achieve an MCC of 0.11 when secondary structure information is used. The performance of gamma-turn prediction is further improved when multiple sequence alignment is used as the input instead of a single sequence. Based on this study, we have developed a method, GammaPred, for gamma-turn prediction (MCC = 0.17). The GammaPred is a neural-network-based method, which predicts gamma-turns in two steps. In the first step, a sequence-to-structure network is used to predict the gamma-turns from multiple alignment of protein sequence. In the second step, it uses a structure-to-structure network in which input consists of predicted gamma-turns obtained from the first step and predicted secondary structure obtained from PSIPRED.

Databases, Protein↗
PubMed2006

Machine learning in soil classification.

In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper, an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is developed and applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained.

Algorithms↗
PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗
PubMed2007

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data.

Algorithms↗
PubMed2006

A wiring of the human nucleolus.

Recent proteomic efforts have created an extensive inventory of the human nucleolar proteome. However, approximately 30% of the identified proteins lack functional annotation. We present an approach of assigning function to uncharacterized nucleolar proteins by data integration coupled to a machine-learning method. By assembling protein complexes, we present a first draft of the human ribosome biogenesis pathway encompassing 74 proteins and hereby assign function to 49 previously uncharacterized proteins. Moreover, the functional diversity of the nucleolus is underlined by the identification of a number of protein complexes with functions beyond ribosome biogenesis. Finally, we were able to obtain experimental evidence of nucleolar localization of 11 proteins, which were predicted by our platform to be associates of nucleolar complexes. We believe other biological organelles or systems could be "wired" in a similar fashion, integrating different types of data with high-throughput proteomics, followed by a detailed biological analysis and experimental validation.

Artificial Intelligence↗
PubMedDate not supplied

Associative clustering for exploring dependencies between functional genomics data sets.

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.

Algorithms↗
PubMed2025

DescribePROT Database of Residue-Level Protein Structure and Function Annotations.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8&#xa0;billion predictions for 1.4&#xa0;million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Databases, Protein↗
PubMed1994

Aspect graphs for visual recognition of three-dimensional objects.

Visual representation of three-dimensional (3-D) objects in our environment is a crucial question, for human as well as for machine vision. Some basics are reviewed of a viewer-centred model of 3-D objects, aspect graphs, which represents a 3-D object by all its topologically stable visible image contours (its aspects) and by the transitions between stable image contours (the visual events). This representation takes only geometrical information about discontinuities in depth and in surface orientation into account, and other clues, such as shadows, markings, texture, etc, are disregarded. Mathematical results give some insight into the relationships between the geometry of a 3-D object and the aspect of its image contours, the techniques used to compute an aspect graph effectively, and the state of the art of this type of model in computer vision. Current research is reviewed on viewer-centred representation in cognitive science that seems to indicate that aspect graphs could also have some relevance for human vision.

Attention↗
PubMed2003

A grounded theory of abstraction in artificial intelligence.

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.

Artificial Intelligence↗
PubMed2025

Gene Specific Pathogenicity Predictor for Chromatin-Remodeling BAF Complex-Associated Neurodevelopmental Disorders.

Advancements in whole genome sequencing have increased the number of variants of uncertain significance (VUS) identified in patient genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BAF complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and AUROC than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.

Journal Article↗
PubMed2006

Cortical substrates for exploratory decisions in humans.

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

Choice Behavior↗
PubMed2026

Treemble: a graphical tool to generate Newick strings from phylogenetic tree images.

SUMMARY: Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable Newick format. There are, thus, thousands of published trees in the scientific literature that are unavailable for follow-up analyses, comparisons, and supertree construction. Experts can easily read such diagrams, but the manual construction of a Newick string from a diagram is laborious, error-prone, and time-consuming. Previous attempts to semi-automate the reading of tree images relied on image processing techniques. These often encounter difficulties as typical published tree diagrams contain various graphical elements and annotations that overlap the branches, such as error bars on internal nodes. Here we introduce Treemble, a user-friendly desktop application for generating Newick strings from tree images. The user simply clicks to mark node locations, assisted by a deep learning-based node detection tool, and Treemble algorithmically assembles the tree from the node coordinates alone. Treemble also facilitates the automatic reading of tip name labels and can be used for both rectangular and circular trees. AVAILABILITY AND IMPLEMENTATION: Treemble is a native desktop application for macOS and Windows and is freely available, with documentation, at treemble.org. Source code is available at github.com/John-Allard/Treemble. The trained node detection model is available at huggingface.co/John-Allard/treemble-1.

Phylogeny↗
PubMedDate not supplied

Logistic-based patient grouping for multi-disciplinary treatment.

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospital's units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.

Databases, Factual↗