456 research outputs found
A First Course in Causal Inference
I developed the lecture notes based on my ``Causal Inference'' course at the
University of California Berkeley over the past seven years. Since half of the
students were undergraduates, my lecture notes only require basic knowledge of
probability theory, statistical inference, and linear and logistic regressions
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
Precise influence evaluation in complex networks
Evaluating node influence is fundamental for identifying key nodes in complex
networks. Existing methods typically rely on generic indicators to rank node
influence across diverse networks, thereby ignoring the individualized features
of each network itself. Actually, node influence stems not only from general
features but the multi-scale individualized information encompassing specific
network structure and task. Here we design an active learning architecture to
predict node influence quantitively and precisely, which samples representative
nodes based on graph entropy correlation matrix integrating multi-scale
individualized information. This brings two intuitive advantages: (1)
discovering potential high-influence but weak-connected nodes that are usually
ignored in existing methods, (2) improving the influence maximization strategy
by deducing influence interference. Significantly, our architecture
demonstrates exceptional transfer learning capabilities across multiple types
of networks, which can identify those key nodes with large disputation across
different existing methods. Additionally, our approach, combined with a simple
greedy algorithm, exhibits dominant performance in solving the influence
maximization problem. This architecture holds great potential for applications
in graph mining and prediction tasks
Machine Learning Methods for Autonomous Classification and Decision Making
This thesis focuses on developing machine learning methods for autonomous classification and decision making, especially on two case studies: traffic speed prediction and cancer bone segmentation. For traffic speed prediction, the convolutional neural network (CNN) achieves state-of-the-art results in complex traffic networks. However, the pooling layers cause the loss of information within the data. This thesis proposes an efficient capsule network for traffic speed prediction. The proposed capsule network replaces the pooling layer with capsules connected by dynamic routing and encodes the features and probability of those features showing on the local region. The proposed capsule network provides outperformed results compared to state-of-the-art CNNs. However, the CNN and capsule network (CapsNet) are parametric models and the uncertainty is, thus, not analysed. Two Gaussian process (GP) frameworks are proposed for traffic speed prediction, equipping the CNN with the ability to quantify uncertainty. The first framework proposes to equate a state-of-the-art CNN with a shallow GP. The proposed approach is evaluated and the uncertainty is analysed by applying the confidence interval. In addition, the impact of the noise is investigated by adding a different level of noise. The second framework is a novel deep kernel CNN-GP framework with spatio-temporal kernels, allowing it to abstract high-level features and consider both time and space. The proposed CNN-GP framework is validated and evaluated using CO2 concentration and traffic prediction for the short-term and long-term. An efficient uniform error bound is proposed and evaluated with simulated and real data. For cancer bone segmentation, machine learning methods are proposed to segment bone lesions in cancer-induced bone disease from Micro Computed Tomography (µCT) images, which brings a new perspective of dealing with bone caner segmentation. The performances are evaluated and their effectiveness is compared. Due to the limited number of datasets and the lack of labelled lesions within the dataset, an approach to generate simulated data is proposed. With an enhanced dataset, a generative adversarial network is proposed to reconstruct the bone with a lesion to a healthy bone. Consequently, the location of the lesion can be obtained by subtracting the original image from the reconstructed image
Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases
Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI
In Silico Strategies for Prospective Drug Repositionings
The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions
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