Walking on a user similarity network towards personalized recommendations.
Ontology highlight
ABSTRACT:
SUBMITTER: Gan M
PROVIDER: S-EPMC4260921 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Deciphering gene regulatory networks (GRNs) is a key for understanding gene expression regulations in living systems. Here, we describe the investigation of the ABSCISIC ACID INSENSITIVE 3 (ABI3) plant transcription factor GRN vicinity by a technique called Network Walking. The method involves transient transformation of protoplasts and inducible nuclear re-localization of transcription factors along with transcriptomic analysis. This genome-wide approach allowed the de novo recovery of i) direct and indirect ABI3 target genes, ii) cis-binding site preference, and iii) biological processes regulated by this canonical abscisic acid response factor. This work improves our knowledge of ABI3 action by inferring network motifs (such as Feed Forwar Loops) under its influence. The novel high-throughput-oriented technique will help accelerate GRN systems investigations in plants, as well as in other organisms. This work studies ABI3 direct and indirect targets by a technique named Network Walking. Root/protoplasts were treated with or without dexamethasone (DEX) and cycloheximide (CHX). 3 reps each.
2013-01-01 | E-GEOD-33344 | biostudies-arrayexpress
Project description:Myelodysplastic syndromes (MDS) are characterized by ineffective hematopoiesis, dysplasia and peripheral cytopenias. Nowadays, MDS therapy is selected based on risk. The goals of therapy are different in low-risk and high-risk patients. In low-risk MDS, the goal is to decrease transfusion needs and to increase the quality of life. Currently, available drugs for newly diagnosed low-risk MDS include growth factor support, lenalidomide and immunosuppressive therapy. Additionally, luspatercept has recently been added to treat patients with MDS with ring sideroblasts, who are not candidates or have lost the response to erythropoiesis-stimulating agents. Treatment of high-risk patients is aimed to improve survival. To date, the only currently approved treatments are hypomethylating agents and allogeneic stem cell transplantation. However, the future for MDS patients is promising. In recent years, we are witnessing the emergence of multiple treatment combinations based on hypomethylating agents (pevonedistat, magrolimab, eprenetapopt, venetoclax) that have proven to be effective in MDS, even those with high-risk factors. Furthermore, the approval in the US of an oral hypomethylating agent opens the door to exclusively oral combinations for these patients and their consequent impact on the quality of life of these patients. Relapsed and refractory patients remain an unmet clinical need. We need more drugs and clinical trials for this profile of patients who have a dismal prognosis.
| S-EPMC8153316 | biostudies-literature
Project description:BackgroundRegular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process.MethodsWe used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting different phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user's context, specifying user requirements, designing and developing an initial low-fidelity eCoach prototype. In design workshop 2, we focused on maturing the low-fidelity solution design and development for the visualization of continuous and discrete data, artificial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals.ResultsThe iterative design process helped to develop a working prototype of eCoach system that meets end-user's requirements and expectations towards an effective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the "Google Material Design" guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users.ConclusionsThe adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working research prototype to improve the fit between technology, end-user, and researchers. Furthermore, we performed a technological readiness study of ProHealth eCoach against standard levels set by European Union (EU).
| S-EPMC9440769 | biostudies-literature
Project description:The cancer biomarker discovery pipeline is progressing slowly. The difficulties of finding novel and effective biomarkers for diagnosis and management of cancer patients are well-known. We speculate that it is unlikely to discover new serological biomarkers characterized by high sensitivity and specificity. This projection is supported by recent findings that cancers are genetically highly heterogeneous. Here, we propose a new way of improving the landscape of cancer biomarker research. There are currently hundreds, if not thousands, of described biomarkers which perform at high specificity (>?90%), but at relatively low sensitivity (<?30%). We call these "rare tumor markers." Borrowing from the principles of precision medicine, we advocate that among these low sensitivity markers, some may be useful to specific patients. We suggest screening new patients for hundreds to thousands of cancer biomarkers to identify a few that are informative, and then use them clinically. This is similar to what we currently do with genomics to identify personalized therapies. We further suggest that this approach may explain as to why some biomarkers are elevated in only a small group of patients. It is likely that these differences in expression are linked to specific genomic alterations, which could then be found with genomic sequencing.
| S-EPMC5871887 | biostudies-other
Project description:Identifying protein complexes from protein-protein interaction networks (PPINs) is important to understand the science of cellular organization and function. However, PPINs produced by high-throughput studies have high false discovery rate and only represent snapshot interaction information. Reconstructing higher quality PPINs is essential for protein complex identification. Here we present a Multi-Level PPINs reconstruction (MLPR) method for protein complexes detection. From existing PPINs, we generated full combinations of every two proteins. These protein pairs are represented as a vector which includes six different sources. Then the protein pairs with same vector are mapped to the same fingerprint ID. A fingerprint similarity network is constructed next, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top 10 similar fingerprints by edges. After random walking on the fingerprints similarity network, each vertex got a score at the steady state. According to the score of protein pairs, we considered the top ranked ones as reliable PPI and the score as the weight of edge between two distinct proteins. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs. Applying our method on the yeast PPINs, our algorithm achieved higher F-value in protein complexes detection than the-state-of-the-art methods. The interactions in our reconstructed PPI network have more significant biological relevance than the exiting PPI datasets, assessed by gene ontology. In addition, the performance of existing popular protein complexes detection methods are significantly improved on our reconstructed network.
| S-EPMC6067004 | biostudies-literature
Project description:Over the past several years, the success of genome-wide association studies (GWAS) and pharmacogenomics has gradually begun to enable personalized medicine in some fields. In the field of liver diseases, host genetic factors are now very useful in clinical practice for predicting treatment outcome and adverse reactions for pegylated interferon plus ribavirin combination therapy against chronic hepatitis C virus (HCV) infection. Recently, three virus-related hepatocellular carcinoma (HCC) GWAS were reported from Asia. One study examined hepatitis B virus-related HCC in China, where hepatitis B is very prevalent, and the other two examined HCV-related HCC in Japan. We identified a common variant in the DEPDC5 locus associated with HCV-related HCC, and another group identified an association involving the MICA locus. In this review, we compare the results of these GWAS and earlier candidate gene studies. Further research is needed to determine the role of these single nucleotide polymorphisms on HCC risk, but identification of these markers could make it possible to assess the magnitude of the risk of cancer based on each patient's genetic background. Consideration of the genetic background of the patients will likely play a role in personalized medicine for HCC, and understanding the mechanism underlying the association could suggest novel promising therapeutic targets in the future.
| S-EPMC7659374 | biostudies-literature
Project description:Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.
| S-EPMC5802799 | biostudies-literature
Project description:BackgroundIn Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents.ResultsWe introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human).ConclusionsInteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.
| S-EPMC4015993 | biostudies-literature
Project description:BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users' social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.
| S-EPMC6352015 | biostudies-literature
Project description:BackgroundFor the development of digital solutions, different aspects of user interface design must be taken into consideration. Different technologies, interaction paradigms, user characteristics and needs, and interface design components are some of the aspects that designers and developers should pay attention to when designing a solution. Many user interface design recommendations for different digital solutions and user profiles are found in the literature, but these recommendations have numerous similarities, contradictions, and different levels of detail. A detailed critical analysis is needed that compares, evaluates, and validates existing recommendations and allows the definition of a practical set of recommendations.ObjectiveThis study aimed to analyze and synthesize existing user interface design recommendations and propose a practical set of recommendations that guide the development of different technologies.MethodsBased on previous studies, a set of recommendations on user interface design was generated following 4 steps: (1) interview with user interface design experts; (2) analysis of the experts' feedback and drafting of a set of recommendations; (3) reanalysis of the shorter list of recommendations by a group of experts; and (4) refining and finalizing the list.ResultsThe findings allowed us to define a set of 174 recommendations divided into 12 categories, according to usability principles, and organized into 2 levels of hierarchy: generic (69 recommendations) and specific (105 recommendations).ConclusionsThis study shows that user interface design recommendations can be divided according to usability principles and organized into levels of detail. Moreover, this study reveals that some recommendations, as they address different technologies and interaction paradigms, need further work.
| S-EPMC9568819 | biostudies-literature