138 research outputs found
Relevance Grounding for Planning in Relational Domains
Abstract. Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that ground this representation for all objects need to plan in exponentially large state spaces and large sets of stochastic actions. A key insight for computational efficiency is that successful planning typically involves only a small subset of relevant objects. In this paper, we introduce a probabilistic model to represent planning with subsets of objects and provide a definition of object relevance. Our definition is sufficient to prove consistency between repeated planning in partially grounded models restricted to relevant objects and planning in the fully grounded model. We propose an algorithm that exploits object relevance to plan efficiently in complex domains. Empirical results in a simulated 3D blocksworld with an articulated manipulator and realistic physics prove the effectiveness of our approach.
Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation
Therapies halting the progression of fibrosis are ineffective and limited. Activated myofibroblasts are emerging as important targets in the progression of fibrotic diseases. Previously, we performed a high-throughput screen on lung fibroblasts and subsequently demonstrated that the inhibition of myofibroblast activation is able to prevent lung fibrosis in bleomycin-treated mice. High-throughput screens are an ideal method of repurposing drugs, yet they contain an intrinsic limitation, which is the size of the library itself. Here, we exploited the data from our āwetā screen and used ādryā machine learning analysis to virtually screen millions of compounds, identifying novel anti-fibrotic hits which target myofibroblast differentiation, many of which were structurally related to dopamine. We synthesized and validated several compounds ex vivo (āwetā) and confirmed that both dopamine and its derivative TS1 are powerful inhibitors of myofibroblast activation. We further used RNAi-mediated knock-down and demonstrated that both molecules act through the dopamine receptor 3 and exert their anti-fibrotic effect by inhibiting the canonical transforming growth factor Ī² pathway. Furthermore, molecular modelling confirmed the capability of TS1 to bind both human and mouse dopamine receptor 3. The anti-fibrotic effect on human cells was confirmed using primary fibroblasts from idiopathic pulmonary fibrosis patients. Finally, TS1 prevented and reversed disease progression in a murine model of lung fibrosis. Both our interdisciplinary approach and our novel compound TS1 are promising tools for understanding and combating lung fibrosis
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer
The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5ā17.6), irregular mass shape (OR 10.0, CI 3.4ā29.5), spiculated mass margin (OR 20.4, CI 1.9ā222.8), and subject age (Ī²ā=ā0.09, pā<ā0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings
S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework
Proceeding of: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, CĆ³rdoba, Spain, June 1-4, 2010Determining the functions of genes is essential for understanding how the metabolisms work, and for trying to solve their malfunctions. Genes usually work in groups rather than isolated, so functions should be assigned to gene groups and not to individual genes. Moreover, the genetic knowledge has many relations and is very frequently changeable. Thus, a propositional ad-hoc approach is not appropriate to deal with the gene group function prediction domain. We propose the Modular Multi-Relational Framework (MMRF), which faces the problem from a relational and flexible point of view. The MMRF consists of several modules covering all involved domain tasks (grouping, representing and learning using computational prediction techniques). A specific application is described, including a relational representation language, where each module of MMRF is individually instantiated and refined for obtaining a prediction under specific given conditions.This research work has been supported by CICYT, TRA 2007-67374-C02-02 project and by the expert biological knowledge of the Structural Computational Biology Group in Spanish National Cancer Research Centre (CNIO). The authors would like to thank members of Tilde tool developer
group in K.U.Leuven for providing their help and many useful suggestions.Publicad
Combined chemical genetics and data-driven bioinformatics approach identifies receptor tyrosine kinase inhibitors as host-directed antimicrobials
Immunogenetics and cellular immunology of bacterial infectious disease
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Recent advances in machine learning and AI, including Generative AI and LLMs,
are disrupting technological innovation, product development, and society as a
whole. AI's contribution to technology can come from multiple approaches that
require access to large training data sets and clear performance evaluation
criteria, ranging from pattern recognition and classification to generative
models. Yet, AI has contributed less to fundamental science in part because
large data sets of high-quality data for scientific practice and model
discovery are more difficult to access. Generative AI, in general, and Large
Language Models in particular, may represent an opportunity to augment and
accelerate the scientific discovery of fundamental deep science with
quantitative models. Here we explore and investigate aspects of an AI-driven,
automated, closed-loop approach to scientific discovery, including self-driven
hypothesis generation and open-ended autonomous exploration of the hypothesis
space. Integrating AI-driven automation into the practice of science would
mitigate current problems, including the replication of findings, systematic
production of data, and ultimately democratisation of the scientific process.
Realising these possibilities requires a vision for augmented AI coupled with a
diversity of AI approaches able to deal with fundamental aspects of causality
analysis and model discovery while enabling unbiased search across the space of
putative explanations. These advances hold the promise to unleash AI's
potential for searching and discovering the fundamental structure of our world
beyond what human scientists have been able to achieve. Such a vision would
push the boundaries of new fundamental science rather than automatize current
workflows and instead open doors for technological innovation to tackle some of
the greatest challenges facing humanity today.Comment: 35 pages, first draft of the final report from the Alan Turing
Institute on AI for Scientific Discover
Enriching for correct prediction of biological processes using a combination of diverse classifiers
<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p
Studying the Functional Genomics of Stress Responses in Loblolly Pine With the Expresso Microarray Experiment Management System
Conception, design, and implementation of cDNA microarray experiments present a
variety of bioinformatics challenges for biologists and computational scientists. The multiple
stages of data acquisition and analysis have motivated the design of Expresso, a
system for microarray experiment management. Salient aspects of Expresso include
support for clone replication and randomized placement; automatic gridding, extraction of
expression data from each spot, and quality monitoring; flexible methods of combining
data from individual spots into information about clones and functional categories; and the
use of inductive logic programming for higher-level data analysis and mining. The
development of Expresso is occurring in parallel with several generations of microarray
experiments aimed at elucidating genomic responses to drought stress in loblolly pine
seedlings. The current experimental design incorporates 384 pine cDNAs replicated and
randomly placed in two specific microarray layouts. We describe the design of Expresso as
well as results of analysis with Expresso that suggest the importance of molecular
chaperones and membrane transport proteins in mechanisms conferring successful
adaptation to long-term drought stress
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