Aging Research Advances with Python Analysis

Exploring the complexities of aging is essential in a world increasingly focused on achieving longer and healthier lives. Recent advancements in Python programming provide robust tools for analyzing biological data, facilitating a deeper understanding of the mechanisms underlying longevity and immortality. Researchers can utilize various Python libraries to model aging processes, simulate potential interventions, and identify biomarkers associated with lifespan. By integrating these technological resources, we can develop innovative strategies to combat the aging process and enhance our comprehension of longevity. For further insights into this research, please refer to the following link: https://lnkd.in/eCakp9c7 #AgingResearch #Longevity #BiologicalData #PythonProgramming #HealthInnovation

A PhD milestone 💁♀️ My work on comparing neighbor preference methods for spatial data analysis is now out in Nature Communications. We compared 9+ existing neighbor preference methods for analyzing spatial omics data on a cell type level across synthetic & real datasets. We highlight method result differences, strengths & pitfalls, and provide guidance on what to use when. Additionally, we propose COZI (conditional z-score), a novel approach that combines the most relevant analysis features. Check out the paper and use it as a guide to interpret your analysis results: https://lnkd.in/d5DKdzXS 🫵 If you want to try out COZI, it is available as a package in Python (https://lnkd.in/d7pC7fDe), R (https://lnkd.in/dj_gWk7e) and now also as a part of IMCRtools (https://lnkd.in/dgxApjtH)! Big thanks to Denis Schapiro, Miguel A. Ibarra-Arellano, Krešimir Beštak, and Jovan Tanevski - and to everyone else who helped along the way 🤝 . This project took its time, and I couldn’t have brought it this far without you. Nature Portfolio #spatial #ComputationalBiology #SpatialProteomics

Today, we released Cognitiv, an open-source data & RAG architecture in Python for emulating human-like memory and behavior for LLMs. Instead of the common filing cabinet implementation of memory, Cognitiv implements four subsystems (perception, appraisal, emotion, memory) which share a cognitive blackboard. When a stimulus is received, it is processed through these subsystems and and the experience is stored as a memory with associated emotions impulses which in turn affect overall emotional reactions. We developed this with the goal of creating a model of cognition that can emulate imperfections in human memory and emotion. Memories are lumped into different confidence levels, which are used to them inform subsequent LLM responses. To read more about our implementation, check out our white paper here: https://lnkd.in/gGfTyVC4 Download Cognitiv for free from our github: https://lnkd.in/gZ8X-irG #LLM #Neuroscience #Python

Excited to share a successful outreach initiative from the Oliveira Lab 🚀 The Python for Bioengineers Workshop 2.0 at the Joint School of Nanoscience & Nanoengineering brought together students and researchers to explore how computational tools are shaping modern bioengineering. This one day hybrid workshop introduced Python for biological data analysis with no prior coding experience required. Participants gained practical skills, engaged with research, and explored real world applications at the interface of biology and computation. As part of Oliveira Outreach, this initiative reflects a strong commitment to making computational biology accessible and empowering the next generation of bioengineers. Looking forward to growing this community and continuing to bridge biology and computation. Read more: https://lnkd.in/eN9dUdez #Python #Bioengineering #STEM #ComputationalBiology #JSNN #Outreach #FutureScientists #OliveiraLab

Most DNA sequences look random… but hidden patterns tell powerful biological stories 🧬 So I built a **DNA Sequence Alignment Tool** using the Needleman–Wunsch algorithm. This tool: ✔ Aligns two DNA sequences ✔ Detects matches, mismatches & gaps ✔ Uses dynamic programming to find optimal alignment What makes this interesting? Even a small change in sequence can reveal evolutionary relationships or mutations. Built using Python 🐍 — combining coding with real biology concepts. I’m currently exploring bioinformatics step by step, and this is one of the most exciting builds so far. Would love your thoughts 👇 What should I build next? #Python #Bioinformatics #Genetics #Coding #MachineLearning #DataScience #100DaysOfCode

Most DNA sequences look random… but hidden patterns tell important biological stories 🧬 So I built a Python script to uncover them 👨💻 This program scans a DNA sequence and identifies: • Exact motif matches • All positions where it appears • Even overlapping occurrences This concept is fundamental in bioinformatics and sequence analysis. 💡 How it works: 1️⃣ Input DNA sequence 2️⃣ Input motif 3️⃣ Scan using sliding window 4️⃣ Return all matching positions Built using pure Python logic (no external libraries). 🔗 GitHub Repository: https://lnkd.in/gv7BxEEp Next step → scaling this for large genomic datasets 🚀 How would you optimize this for massive sequences? 🧠👇 #Bioinformatics #Python #Genomics #ComputationalBiology #Biotech #Programming #PythonProjects #LearnInPublic #DataScience #CodingJourney

This research pushes the Lorenz-63 system into an extreme turbulence regime (ρ=99.96) with 20% Gaussian noise. We demonstrate that RNN, GRU, and LSTM architectures act as robust non-linear observers, successfully decoupling deterministic physics from stochastic noise while maintaining topological integrity. Full technical details, analysis, and results are available in the attached paper. Links and Repositories: Source Code: https://lnkd.in/dMZ69YNT. Official DOI: https://lnkd.in/dBMYCgFJ #DeepLearning #DynamicSystems #ChaosTheory #MachineLearning #PhysicsInformed #QuantymaResearch #Python #Lorenz63 #ResearchLab

Happy to share my latest research at Quantyma! Check out the full study on the resilience of recurrent networks in high-turbulence chaotic regimes below. 👇 #DeepLearning #ChaosTheory #PhysicsInformed #MachineLearning #DynamicalSystems #QuantymaResearch #ResearchLab

Excited to share our new preprint: "Discovering Quantum phenomena with Interpretable Machine Learning" https://lnkd.in/dnsaSjkr We introduce QDisc, a pipeline that combines variational autoencoders with symbolic regression to automatically extract interpretable physical descriptions directly from raw quantum data. Important: no prior knowledge required!! ⚛️ We tested it on three very different types of quantum data: experimental Rydberg-atom snapshots, classical shadows, and hybrid discrete-continuous fermionic measurements. In each case, the pipeline not only recovered the known physics but also revealed previously unreported features, including a corner-ordering pattern in the Rydberg platform. 💻 Importantly, we also release qdisc, an open-source Python library that makes the full pipeline accessible and easy to use: https://lnkd.in/d3xZ7AA9 🔎 This is the next step in our ongoing effort to let interpretable ML guide the discovery of new quantum phenomena and we believe there's much more to find as quantum simulators continue to scale. This work has been speareheaded by Paulin de Schoulepnikoff, and with the amazing help of Hendrik Poulsen Nautrup and Hans Briegel.

A few years ago I started asking a simple question: what if zero wasn't the end of a computation, but the beginning of a trace? That question became Zero Domain Algebra: a formal framework where dividing by zero produces a labeled object instead of an exception, and where those objects can be combined, reduced, and under the right conditions, brought back into something meaningful. It's part mathematics, part philosophy, part engineering. The "Grand Absorber" (the absolute zero ○ that swallows everything) is very much intentional. The paper is now on Zenodo, with full proofs, a Python prototype, and deliberately open applications. Some bridges are left to the reader. 📄 https://lnkd.in/eD82fets Curious to hear from anyone working on formal verification, SIEM pipelines, numerical analysis, or algebraic structures — this might resonate. #Mathematics #AbstractAlgebra #ErrorProvenance #Observability #Research