Best AI tools for< Analyze Biomedical Data >

IXICO is a precision analytics company specializing in intelligent insights in neuroscience. They offer a range of services for drug development analytics, imaging operations, and post-marketing consultancy. With a focus on technology and innovation, IXICO provides expertise in imaging biomarkers, radiological reads, volumetric MRI, PET & SPECT, and advanced MRI. Their TrialTracker platform and Assessa tool utilize innovation and AI for disease modeling and analysis. IXICO supports biopharmaceutical companies in CNS clinical research with cutting-edge neuroimaging techniques and AI technology.

Bionl is a no-code bioinformatics platform designed to streamline biomedical research for researchers and scientists. It offers a full workspace with features such as bioinformatics pipelines customization, GenAI for data analysis, AI-powered literature search, PDF analysis, and access to public datasets. Bionl aims to automate cloud, file system, data, and workflow management for efficient and precise analyses. The platform caters to Pharma and Biotech companies, academic researchers, and bioinformatics CROs, providing powerful tools for genetic analysis and speeding up research processes.

Genie TechBio is the world's first AI bioinformatician, offering an LLM-powered omics analysis software that operates entirely in natural language, eliminating the need for coding. Researchers can effortlessly analyze extensive datasets by engaging in a conversation with Genie, receiving recommendations for analysis pipelines, and obtaining results. The tool aims to accelerate biomedical research and empower scientists with newfound data analysis capabilities.

CEBRA is a machine-learning method that compresses time series data to reveal hidden structures in the variability of the data. It excels in analyzing behavioral and neural data simultaneously, allowing for the decoding of activity from the visual cortex of the mouse brain to reconstruct viewed videos. CEBRA is a novel encoding method that leverages both behavioral and neural data to produce consistent and high-performance latent spaces, enabling the mapping of space, uncovering complex kinematic features, and providing rapid, high-accuracy decoding of natural movies from the visual cortex.

The National Cancer Institute's Center for Biomedical Informatics and Information Technology (CBIIT) provides a comprehensive suite of tools, resources, and training to support cancer data science research. These resources include data repositories, analytical tools, data standards, and training materials. CBIIT also develops and maintains the NCI Thesaurus, a comprehensive vocabulary of cancer-related terms, and the Cancer Data Standards Registry and Repository (caDSR), a repository of cancer data standards. CBIIT's mission is to accelerate the pace of cancer research by providing researchers with the tools and resources they need to access, analyze, and share cancer data.

Decode Health is an AI and analytics platform that accelerates precision healthcare by supporting healthcare teams in launching machine learning and advanced analytics projects. The platform collaborates with pharmaceutical companies to enhance patient selection, biomarker identification, diagnostics development, data asset creation, and analysis. Decode Health offers modules for biomarker discovery, patient recruitment, next-generation sequencing, data analysis, and clinical decision support. The platform aims to provide fast, accurate, and actionable insights for acute and chronic disease management. Decode Health's custom-built modules are designed to work together to solve complex data problems efficiently.

Ignota Labs is a technology company focused on rescuing failing drugs and bringing new life to abandoned projects, ultimately providing hope to patients. The company utilizes a proprietary AI model, SAFEPATH, which applies deep learning to bioinformatics and cheminformatics datasets to solve drug safety issues. Ignota Labs aims to identify promising drug targets, address safety problems in clinical trials, and accelerate the delivery of therapeutically effective drugs to patients.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform collates and analyzes medical research articles, extracting important concepts and identifying causal and associative relationships between them. It organizes this information into a specialized database forming a knowledge graph. Researchers can retrieve causal chains, access specific research articles, and perform tasks like concept analysis, drug repurposing, and target discovery. SOMA enhances literature review by finding relevant articles based on the mechanism of action, saving time on organizing reviews and allowing researchers to focus on their own research. The platform offers freemium access with basic functionality for free indefinitely, with the option to subscribe to advanced features after a 14-day trial period.

CellProfiler is an AI tool designed for biologists to analyze and process images automatically. It allows users to load image-processing modules, adjust settings, measure phenotypes, export data, and classify phenotypes using machine learning. The application is user-friendly and provides a seamless experience for biologists to analyze complex or subtle phenotypes in their images.

RejuveAI is a decentralized token-based system that aims to democratize longevity globally. The Longevity App allows users to monitor essential health metrics, enhance lifespan, and earn RJV tokens. The application leverages revolutionary AI technology to analyze human body functions in-depth, providing insights for aging combat. RejuveAI collaborates with researchers, clinics, and data enthusiasts to ensure innovative outcomes are affordable and accessible. The platform also offers exclusive discounts on travel, supplements, medical tests, and longevity therapies.

Mind-Video is an AI tool that focuses on high-quality video reconstruction from brain activity data. It bridges the gap between image and video brain decoding by utilizing masked brain modeling, multimodal contrastive learning, spatiotemporal attention, and co-training with an augmented Stable Diffusion model. The tool aims to recover accurate semantic information from fMRI signals, enabling the generation of realistic videos based on brain activities.

Modality.AI is an AI application that has developed an automated, clinically validated system to assess neurological and psychiatric states both in clinic and remotely. The platform utilizes conversational AI to monitor conditions accurately and consistently, allowing researchers and clinicians to review data in near real-time and monitor treatment response over time. Modality.AI collaborates with world-class AI/Machine Learning experts and leading institutions to provide a HIPAA-compliant system for assessing various indications such as ALS, Parkinson's, depression, autism, Huntington's Disease, schizophrenia, and mild cognitive impairment. The platform enables convenient monitoring at home through streaming and analysis of speech and facial responses, without the need for special software or apps. Modality.AI is accessible on various devices with a browser, webcam, and microphone, offering a new approach to efficient and cost-effective clinical trials.

neurons.bio is an AI application that offers a unique collection of over 100 AI agents designed for drug development, medicine, and life science research. These agents perform specific tasks efficiently, retrieve data from various sources, and provide insights to accelerate research processes. The platform aims to revolutionize drug discovery and development by integrating cutting-edge LLM technology with domain-specific agents, reducing research costs and time to clinic.

Activeloop is an AI tool that offers Deep Lake, a database for AI solutions across various industries such as agriculture, audio processing, autonomous vehicles, robotics, biomedical and healthcare, generative AI, multimedia, safety, and security. The platform provides features like fast AI search, faster data preparation, serverless DB for code assistant, and more. Activeloop aims to streamline data processing and enhance AI development for businesses and researchers.

Undermind is an AI-powered scientific research assistant that revolutionizes the way researchers access and analyze academic papers. By utilizing intelligent language models, Undermind reads and synthesizes information from hundreds of papers to provide accurate and comprehensive results. Researchers can describe their queries in natural language, and Undermind assists in finding relevant papers, brainstorming questions, and discovering crucial insights. Trusted by researchers across various fields, Undermind offers a unique approach to literature search, surpassing traditional search engines in accuracy and efficiency.

Nucleai is an AI-driven spatial biomarker analysis tool that leverages military intelligence-grade geospatial AI methods to analyze complex cellular interactions in a patient's biopsy. The platform offers a first-of-its-kind multimodal solution by ingesting images from various modalities and delivering actionable insights to optimize biomarker scoring, predict response to therapy, and revolutionize disease diagnosis and treatment.

Kuano is an AI tool that focuses on redefining drug discovery using Quantum and AI technologies. The platform offers world-class scientific expertise in quantum physics, AI, and medicinal chemistry to revolutionize the drug design process. Kuano aims to leverage cutting-edge technologies to accelerate the discovery of new drugs and improve healthcare outcomes.

Elicit is a research tool that uses artificial intelligence to help researchers analyze research papers more efficiently. It can summarize papers, extract data, and synthesize findings, saving researchers time and effort. Elicit is used by over 800,000 researchers worldwide and has been featured in publications such as Nature and Science. It is a powerful tool that can help researchers stay up-to-date on the latest research and make new discoveries.

Plerdy is a comprehensive suite of conversion rate optimization tools that helps businesses track, analyze, and convert their website visitors into buyers. With a range of features including website heatmaps, session replay software, pop-up software, website feedback tools, and more, Plerdy provides businesses with the insights they need to improve their website's usability and conversion rates.

TimeComplexity.ai is an AI tool that allows users to analyze the runtime complexity of their code. It works seamlessly across different programming languages without the need for headers, imports, or a main statement. Users can input their code and get insights into its performance. However, it is important to note that the results may not always be accurate, so caution is advised when using the tool.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

AI-Drug-Discovery-Design is a repository focused on Artificial Intelligence-assisted Drug Discovery and Design. It explores the use of AI technology to accelerate and optimize the drug development process. The advantages of AI in drug design include speeding up research cycles, improving accuracy through data-driven models, reducing costs by minimizing experimental redundancies, and enabling personalized drug design for specific patients or disease characteristics.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

MAVIS (Math Visual Intelligent System) is an AI-driven application that allows users to analyze visual data such as images and generate interactive answers based on them. It can perform complex mathematical calculations, solve programming tasks, and create professional graphics. MAVIS supports Python for coding and frameworks like Matplotlib, Plotly, Seaborn, Altair, NumPy, Math, SymPy, and Pandas. It is designed to make projects more efficient and professional.

ProLLM is a framework that leverages Large Language Models to interpret and analyze protein sequences and interactions through natural language processing. It introduces the Protein Chain of Thought (ProCoT) method to transform complex protein interaction data into intuitive prompts, enhancing predictive accuracy by incorporating protein-specific embeddings and fine-tuning on domain-specific datasets.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

RankLLM is a suite of prompt-decoders compatible with open source LLMs like Vicuna and Zephyr. It allows users to create custom ranking models for various NLP tasks, such as document reranking, question answering, and summarization. The tool offers a variety of features, including the ability to fine-tune models on custom datasets, use different retrieval methods, and control the context size and variable passages. RankLLM is easy to use and can be integrated into existing NLP pipelines.

Awesome-Graph-LLM is a curated collection of research papers exploring the intersection of graph-based techniques with Large Language Models (LLMs). The repository aims to bridge the gap between LLMs and graph structures prevalent in real-world applications by providing a comprehensive list of papers covering various aspects of graph reasoning, node classification, graph classification/regression, knowledge graphs, multimodal models, applications, and tools. It serves as a valuable resource for researchers and practitioners interested in leveraging LLMs for graph-related tasks.

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

An expert in bioengineering, skilled in logical problem-solving

Enhanced with biomedical document knowledge for in-depth blood test analysis.

I assist with gene data queries and enable file downloads.

Your personal biomedical engineer. Create anything related to BME.

I assist biological image analysis, including ImageJ macro and Python coding.

A biophysics research specialist for academic discussions.

Assists with research on neuromuscular responses to passive heating.

Expert in all aspects of spectral flow cytometry.

A biophysicist assistant offering insights into the physics of living systems.

Excellence in Biotech Research - Powered by OpenAI

Find recent research for your area of biological interest.

A Specialized Biochemistry and Pharmaceutical Research Assistant

Expert in Orthopaedic Bioengineering

In-depth CRISPR research and analysis expert, ensuring comprehensive and step-by-step coverage of topics.

AI protein design assistant

I analyze cognitive biases in scenarios and thoughts, providing neutral, educational insights.

Analyze and reverse engineer images. Receive style descriptions and image re-creation prompts.

I'll help you analyze market news and charts for trading/investing

I read and analyze annual reports of companies. Just upload the annual report PDF and start asking me questions!