AlterNet: Alternative splicing-aware gene regulatory networkinference biorxiv.org/content/10.1101/… #biorxiv_bioinfo

Optimizes accuracy Via Core Mechanism of #Context Aware Synthesis - Enabling #Workers to generate dual outputs: #Inferences for target variables and forecasted #Losses of peer models under current conditions. Dual Output Structure: #Workers produce an #Inference based on the target like a price prediction and a forecast of how accurate other models will be, considering factors like market states. - Weighting Process: These forecasted #Losses are transformed into a measure of relative performance, which is then used to assign weights, emphasizing models expected to perform better in the given context. - Final Inference: The #NetworkInference combines these weighted #Inferences using a method called #ForecastImpliedInference, ensuring the output reflects the most relevant contextual insights. 🔹 Self Improvement via Recursive Forecasting #AlloraNetwork’s self improving nature stems from #ContextAwareSynthesis’s recursive loop, refining model weights over time. Learning Loop: The network compares actual #Losses to the forecasts, adjusting weights to prioritize models that perform better under specific conditions. Reputer Role: #Reputers stake #ALLO tokens to evaluate #Inference quality, using an entropy based consensus mechanism to maintain trust and accuracy. Simulation Insight: Whitepaper Section 3.3 highlights that #ForecastImpliedInferences cut error rates significantly compared to basic aggregation methods. 🔹 Contextual Adaptability in Dynamic Domains #ContextAwareSynthesis excels in dynamic environments, making #AlloraNetwork ideal for #DeFi, #Healthcare, and beyond. Use Case Example: For #BTC price feeds, #Workers might prioritize models that perform better when #USMarkets are closed, ensuring more accurate #PriceFeeds. Cross Domain Impact: From tailoring diagnostics in #Healthcare to adjusting for real time weather in climate modeling, #Topics with customized #LossFunctions boost precision. 🔹 Economic Security and Scalability The mechanism scales across #AlloraNetwork’s modular #Topics while maintaining integrity through economic incentives. Reputer Staking: #ALLO staking by #Reputers aligns incentives, rewarding accurate forecasting and penalizing poor performance. Modular Efficiency: #Topic specific #LossFunctions ensure scalability without compromising #Inference quality. Unlike static aggregation in networks like #Numerai, #ContextAwareSynthesis leverages real time context awareness, optimizing #InferenceSynthesis for dynamic settings. #AlloraNetwork #gAllora #Allora $Allo

Unified knowledge-driven network inference from omics data • This paper introduces CORNETO, a novel framework for knowledge-driven network inference that integrates omics data with prior knowledge through constrained optimization. • CORNETO offers a unified mathematical approach, supporting multi-sample inference across different network types, including undirected, directed, signed graphs, and hypergraphs. • The framework bridges the gap between data-driven and knowledge-based methods, addressing the challenges of sparse data and noisy networks by leveraging shared patterns across samples. • CORNETO improves performance in protein-protein interaction networks, intracellular signaling, and metabolic flux modeling, outperforming traditional single-sample inference methods. • The multi-sample extensions reduce variability and overfitting, producing more interpretable and biologically consistent networks, as demonstrated through simulated and real-world datasets. • CORNETO is implemented as an open-source Python package, offering flexibility for customization, reuse, and compatibility with multiple mathematical solvers, enhancing accessibility for the research community. • Applications include multi-condition Flux Balance Analysis (FBA), metabolic network reconstruction from omics data, and intracellular signaling inference from cancer datasets. @saezlab @JulioSaezRod @attila_gbr @PabloRMier 💻Code: github.com/saezlab/corneto 📜Paper: biorxiv.org/content/10.1101/… #Bioinformatics #NetworkInference #OmicsData #SystemsBiology #MachineLearning #AIforBiology #NetworkModeling

Reconstructing networks from simple and complex contagions, Nicholas W. Landry, William Thompson, Laurent Hébert-Dufresne, and Jean-Gabriel Young #NetworkInference @nwlandry @Will_H_Thompson @LHDnets @_jgyou go.aps.org/4ev0BUv

🌟#BITS2024 conference was interesting and highly engaging! We are proud that our researchers @MariottoPiero and Gaia successfully presented their remarkable work on #networkinference and #singlecell. Check out on our GitLab gitlab.com/sysbiobig Stay tuned!! 🚀

Inference of dynamic hypergraph representations in temporal interaction data, Alec Kirkley #ComplexNetworks #NetworkInference @captainkirk1041 @hkudatascience go.aps.org/4b6BXrA

Read #NewPaper "Algal Bloom Ties: Spreading Network Inference and Extreme Eco-Environmental Feedback" from Haojiong Wang et al. mdpi.com/1099-4300/25/4/636 #spatial #networkinference #biogeochemicalnetworks #predictivecausality #bloomprediction #FloridaBay

syntenet: an R/Bioconductor package for the inference and analysis of synteny networks. #SyntenyDetection #NetworkInference #PhylogenomicProfiling #Bioinformatics academic.oup.com/bioinformat…

Our work on 'Network reconstruction for trans acting genetic loci using multi‑omics data and prior information' is finally out! Huge thanks to everyone involved! doi.org/10.1186/s13073-022-0… #networkinference #multiomics @SpringerNature @GenomeMedicine @heinig_matthias

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation. #scRNAseq #ImputationMethods #NetworkInference #Benchmarking #Bioinformatics bmcbioinformatics.biomedcent… @BMCBioinfo

NetworkInference - This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stab... #rstats github.com/desmarais-lab/net…

corto: a lightweight R package for Gene NetworkInference and Master Regulator Analysis biorxiv.org/cgi/content/shor… #bioRxiv

Nice overview of state of the art #networkinference methods, well done!

Interested in #networkinference in #multiomics data? Might want to check out our (@fabian_theis , @heinig_matthias) recent review! Link: doi.org/10.3389/fgene.2019.0… #omics #dataintegration #epigenetics #generegulation

Work from @VingronLab on a new take on graphical models allowing for non-linear associations between nodes using empirical distance covariance presented by Martin Vingron @recomb2019 - going from N to N^2 - exciting work for all us #networkinference guys!

Top story: @MickMenager: 'What a great user group meeting #10xUGM19. Thanks @10xGenomics for having invited me to present our latest data on #singlecell #networkinference to better understand rare autoinflammatory disea… x.com/MickMenager/status/111…, see more tweetedtimes.com/v/1937?s=tn…

What a great user group meeting #10xUGM19. Thanks @10xGenomics for having invited me to present our latest data on #singlecell #networkinference to better understand rare autoinflammatory diseases @InstitutImagine

NetworkInference 1.2.0 now available on CRAN! Faster inference and new features, including automatic parameter initialization and edge selection with Vuong-style test. With @brucedesmarais cran.r-project.org/web/packa…

Version 1.1.2 of NetworkInference package now available on CRAN. Faster and with new features: github.com/desmarais-lab/Net…. @brucedesmarais

R package NetworkInference 1.0.0 is released. Infer latent diffusion networks, now also in R. goo.gl/mRjh2J @brucedesmarais @jure