AI News Today — Part 4
Aug 9, 2026 · Sourced from 500+ daily AI sources, scored by relevance.
- Kantos AI
AI-Powered CRM for growing businesses
- kiwasofttechnologies
@socialmediamarketing@digitalmarketing@socialmediamanagement
- PolymarketExplained
Understand Polymarket before you trade
- Visibite
Visibite: A free food, calorie, and habit tracking app.
- Voya
Review cities you have visited. Plan where you go next.
- cs.AI (ChopsticksAI)
No API key. No hallucinations. Just honest answers.
- A-Case it's my case
Soft silicone Apple Watch band with titanium buckle
- Mom Baby Care Tips
Free online parenting tools and baby care guides.
- Labour Job Card System
KURBAN ALI
- SqueezeNative
Ultra-fast, native media compressor with optimized quality
- LootLust
A barter marketplace for gamers and creators
- Lumière — Digital Bookstore
Discover and buy premium digital books instantly
- AI Flow Builder veya MicroSaaS
Launch your micro-SaaS content with AI.
- Laptop on Rent PAN India
Laptop on Rent PAN India
- VooStream
Fast & Reliable IPTV Service
- HyperMeme AI
Create memes that actually get the joke.
- QuoteQuick
Price freelance projects and create polished proposals
- GotaOfferr
Find the jobs. Tailor the resume. Apply in one click
- Turbyte Bilişim Hizmetleri status page
hosting status page
- FactoringCalculatorOnline.com
Free step-by-step math tool
- PhotoEPhotoEditor Proditor Pro
AI-powered photo editing made simple
- ReplyFlow AI
AI-powered replies for customer inquiries
- Sparrowgate
Open-source AI API router to prevent cloud billing leaks
- The Best AI Writer
The Best AI Writer for Effortless Blog Writing
- StageOnce
AI virtual staging for real estate, one-time payment
- Lia AI
Origination intelligence for mortgage banking
- FloorAI
Sketch to floor plan in seconds.
- Shyne
Design anything, with your team and AI.
- Ottermind
Where thinking meets doing.
- Explainable machine learning relates histological to genomic pathology
Background & Aims: Haematoxylin and eosin (H&E) staining remains the diagnostic gold standard for solid cancers, including hepatocellular carcinoma, and is increasingly complemented by genomic profiling for precision medicine. Inferring genomic alterations directly from H&E images could streamline testing, but heterogeneity and biases in human training data limit interpretation of genotype-phenotype associations. Here, we aimed to relate histologic to genomic pathology to provide biological explainability for mutation prediction models and assess the impact of germline variation on model performance. Methods: We analysed 597 murine liver tumours with matched whole-genome sequencing and histopathology (163,835 image tiles; 22.9 million nuclei). Our controlled in vivo design accounted for germline variation, biological sex, and causal mutagen (N-diethylnitrosamine), removing confounding factors present in human cohorts. We trained and evaluated deep learning and supervised machine learning models to predict germline variation and cancer driver alterations from H&E. Results: Modelling accurately predicted germline and somatic alterations from histology, at both locus-specific and genome-wide scales. Quantitative image analysis revealed an unexpected association between Egfr driver mutations and hepatic steatosis, linking genotype to an interpretable morphological phenotype. While model performance declined when applied to tumours from unrepresented genetic backgrounds, this limitation was biologically informative, revealing strain-dependent differences in tumour evolution, notably the prevalence of whole-genome duplication. Conclusions: Machine learning integration of histological and genomic pathology enables accurate, interpretable inference of genetic alterations from H&E, potentially reducing reliance on costly ancillary molecular assays. Our predictions are supported by human-interpretable biological features, addressing concerns around 'black-box' technologies. However, caution is required when applying such methods to samples with a genetic background that, even if closely related, is beyond the genetic horizon of training data.
- Assessing Computational Models for Pharmacogenomic Variant Interpretation
Accurately predicting the effects of pharmacogenomic variants is essential for the development of personalized therapeutic strategies, as genetic variability can influence drug response differently across patients. Here, we assessed several computational approaches using a dataset of pharmacogenomic variants with either clinical annotations or functional characterization by deep mutational scanning, compiled from the literature, with an additional focus on CYP2C9, a clinically relevant drug-metabolizing enzyme. Our results show that, despite recent methodological advances, substantial room for improvement remains. In particular, current methods struggle to distinguish gain-of-function variants associated with increased drug clearance and fast-metabolizer phenotypes from neutral variants, whereas loss-of-function variants that reduce drug clearance are predicted more accurately. The integration of structural and evolutionary information appears to be a key strategy for improving performance, with the coevolution-based StructureDCA method achieving the highest accuracy compared with classical genetic variant-effect predictors and recent deep learning approaches, including the pathogenic-variant predictor AlphaMissense and general protein language model-based methods. Finally, our results indicate that computational models can complement in vitro experiments in clinical variant interpretation, as StructureDCA predictions showed better agreement with clinically annotated phenotypes than large-scale deep mutational scanning data in several cases.
- Uncovering High-Order Epistatic Interactions in GWAS via a Machine Learning-Based Feature Engineering Framework
Background: Genome wide association studies (GWAS) often fail to identify higher-order epistatic interactions that contribute to complex inheritance patterns of traits and diseases. While machine learning (ML) can capture nonlinear relationships, extracting interpretable insights from these models remains a challenge. We propose a novel tree-based feature engineering framework that uses Classification and Regression Trees (CART) to explicitly encode high order interaction decision paths as dummy variables. We investigate three path-based encoding strategies: (i) all decision paths, (ii) leaf node paths only, and (iii) internal-node paths only. This approach aims to transform complex decision boundaries into discrete features that capture nonlinear interactions that are not readily captured by traditional association models. Results: The framework was evaluated using genetic data for ANCA associated vasculitis (AAV). To manage the high dimensionality of the engineered feature space, we applied a comprehensive suite of ML methods across three tasks: (1) Ensemble Learning (Random Forest, XGBoost, and Gradient Boosting Machine); (2) Decision Tree Analysis (CART); and (3) Regression and Classification Tasks (Regularized Linear Regression/LASSO, Support Vector Machine, and Logistic Regression). Stepwise feature selection and regularization were employed to isolate the most informative interaction patterns. Results indicate that incorporating CART-derived interaction paths, particularly those from high-impact regions of the tree, significantly improves classification accuracy and model interpretability compared to using the original feature space alone. Conclusions: The proposed framework provides a robust, scalable methodology for identifying high-order genetic interactions. By bridging the gap between the predictive power of ensemble ML and the necessity for mechanistic insight, this approach offers a clearer mapping of the combinatorial genetic processes underlying complex diseases. While applied here to AAV, the method is highly adaptable for exploring the genetic architecture of diverse populations and complex traits.
- Whole-brain modeling of dynamic causal circuits in human cognition using amortized variational inference
Understanding dynamic mechanisms underlying cognition remains a major challenge in human neuroscience. Here, we develop, validate, and apply Multivariate Dynamical Systems Identification with Amortized Variational Inference (MDSI-AVI), a novel computational framework designed to address critical challenges in capturing asymmetric, context-dependent, whole-brain directed interactions while accounting for regional hemodynamic response variability in fMRI data. MDSI-AVI leverages simulation-based inference through forward and reverse variational inference to address the limitations of conventional variational methods in high-dimensional settings. By averaging over uncertainty in hemodynamic response parameters using forward simulation, MDSI-AVI provides well-calibrated posteriors of directed connectivity that scale efficiently to networks with hundreds of nodes. Applied to Human Connectome Project data (N=728), MDSI-AVI reveals new insights into working memory mechanisms, identifying the dorsal anterior insula as a critical hub influencing activity at the whole-brain level. We demonstrate task-dependent modulation of causal influences, where the salience network drives frontoparietal network activity, which differentially influences the default mode and sensorimotor networks depending on working memory load. These whole-brain causal interactions distinguish task conditions with high accuracy and predict working memory performance. Our framework demonstrates reproducible results across whole-brain parcellations, establishing MDSI-AVI as a robust tool for advancing our understanding of circuit dynamics in cognition and disease.
- Maya AI
AI, generative AI,
- Yeta AI / YouTube Dubbing
Real-time AI dubbing for any YouTube video
- Acelan AI Development Services
Build Smarter Products with Practical AI Solutions
- Lians v0.5
Reconstruct what your AI knew when it acted
- Budgeting and API Costs Explained (2026)
AI API pricing, tokens, budgeting, and cost optimization
- Paper Lab
Write Smarter. Format Automatically.
- Awesome Forward Deployment Engineering
The definitive guide to becoming an AI FDE
- OpenAI Slows Down Astra AI Model Development Over Cybersecurity Concerns
OpenAI revealed over the weekend that it had stopped work on some aspects of its upcoming frontier AI model called Astra because an internal review found that its agentic coding capability and cybersecurity prowess were causing concerns. Is this another point that Sam Altman has scored in his battle with his friend-turned-bitter-foe Dario Amodei after […] The post OpenAI Slows Down Astra AI Model Development Over Cybersecurity Concerns appeared first on CXOToday.com .
- OpenAI flags possible critical cybersecurity risk in upcoming model
UPDATE 1-OpenAI flags possible critical cybersecurity risk in upcoming model, tightens controls
- AdTown
The Airbnb for Ad Spaces
- OpenAI is pressing pause on its AI model after it displayed dangerous out-of-control tendencies
OpenAI is pausing some work on Astra after testing found the AI could identify and exploit software vulnerabilities without human intervention.
- Dubai to issue villa construction permits in minutes using new AI system
Dubai to issue villa construction permits in minutes using new AI system
- Dubai to cut building permit processing times from days to minutes with AI
Dubai to cut building permit processing times from days to minutes with AI Gulf News
- Amazons new Texas AI data center will include massive amounts of air pollution
Amazon is building a natural gas power plant for a big Texas data center that could be the most pollutant one in the country.
- Omniwork
The Creative Agent OS — create better with desktop AI agents
- DocsAlot CLI
Let Claude or Codex create and maintain good looking docs
- AgentConnect
Tag any agent, wherever work happens.