Best AI tools for< Research Bioinformatics >

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.

Synthace is a software and expertise platform designed for Discovery Biology Teams to streamline and optimize their experiments in assay development, media optimization, and purification process development. The platform offers software solutions, training, and on-site support from specialists to help scientists conduct experiments more efficiently and effectively. By leveraging multifactorial methods and automation, Synthace aims to accelerate drug discovery processes and deliver faster, definitive results.

Insitro is a drug discovery and development company that uses machine learning and data to identify and develop new medicines. The company's platform integrates in vitro cellular data produced in its labs with human clinical data to help redefine disease. Insitro's pipeline includes wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience.

HoloEye.AI is a transformative AI biological intelligence company that leverages cutting-edge artificial intelligence technology to revolutionize the field of biological research. The platform offers advanced AI solutions for analyzing complex biological data, enabling researchers to gain deeper insights and accelerate scientific discoveries.

JADBio is an automated machine learning (AutoML) platform designed to accelerate biomarker discovery and drug development processes. It offers a no-code solution that automates the discovery of biomarkers and interprets their role based on research needs. JADBio can parse multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, enabling users to efficiently discover insights for various conditions such as cancer, immune system disorders, chronic diseases, infectious diseases, and mental health. The platform is trusted by partners in precision health and medicine and is continuously evolving to disrupt drug discovery times and costs at all stages.

PubCompare is a powerful AI-powered tool that helps scientists search, compare, and evaluate experimental protocols. With over 40 million protocols in its database, PubCompare is the largest repository of trusted experimental protocols. PubCompare's AI-powered search features allow users to find similar protocols, highlight critical steps, and evaluate the reproducibility of protocols based on in-protocol citations. PubCompare is available from any computer and requires no download.

Chemprop is a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. Originally developed for research purposes, Chemprop offers a comprehensive set of tools and features for training models and analyzing molecular representations. The package underwent a recent major release (v2.0.0) with significant improvements and updates.

Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

Google Research is a team of scientists and engineers working on a wide range of topics in computer science, including artificial intelligence, machine learning, and quantum computing. Our mission is to advance the state of the art in these fields and to develop new technologies that can benefit society. We publish hundreds of research papers each year and collaborate with researchers from around the world. Our work has led to the development of many new products and services, including Google Search, Google Translate, and Google Maps.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

The Research Center Trustworthy Data Science and Security is a hub for interdisciplinary research focusing on building trust in artificial intelligence, machine learning, and cyber security. The center aims to develop trustworthy intelligent systems through research in trustworthy data analytics, explainable machine learning, and privacy-aware algorithms. By addressing the intersection of technological progress and social acceptance, the center seeks to enable private citizens to understand and trust technology in safety-critical applications.

Research Studio is a next-level UX research tool that helps you streamline your user research with AI-enhanced analysis. Whether you're a freelance UX designer, user researcher, or agency, Research Studio can help you get the insights you need to make better decisions about your products and services.

RapidAI Research Institute is an academic institution under the RapidAI open-source organization, a non-enterprise academic institution. It serves as a platform for academic research and collaboration, providing opportunities for aspiring researchers to publish papers and engage in scholarly activities. The institute offers mentorship programs and benefits for members, including access to resources such as internet connectivity, GPU configurations, and storage space. The management team consists of esteemed professionals in the field, ensuring a conducive environment for academic growth and development.

MIRI (Machine Intelligence Research Institute) is a non-profit research organization dedicated to ensuring that artificial intelligence has a positive impact on humanity. MIRI conducts foundational mathematical research on topics such as decision theory, game theory, and reinforcement learning, with the goal of developing new insights into how to build safe and beneficial AI systems.

Branded Research, acquired by Dynata, provides access to AI-verified audience insights. It offers a range of research methods, including surveys, webcam studies, and emotional AI. With its advanced algorithms and extensive profiling, Branded helps businesses connect with their target audience and gain valuable insights to drive innovation. The company serves various industries, including tech, consumer goods, healthcare, and research agencies.

The Berkeley Artificial Intelligence Research (BAIR) Lab is a renowned research lab at UC Berkeley focusing on computer vision, machine learning, natural language processing, planning, control, and robotics. With over 50 faculty members and 300 graduate students, BAIR conducts research on fundamental advances in AI and interdisciplinary themes like multi-modal deep learning and human-compatible AI.

AIM Research is a leading platform providing insights and analysis on the Artificial Intelligence industry. The website offers a comprehensive range of resources, including research reports, event coverage, news articles, and expert opinions. AIM Research focuses on highlighting the latest trends, innovations, and key players in the AI sector, catering to professionals, researchers, and enthusiasts seeking in-depth knowledge and understanding of AI technologies and applications.

Opus Research is a leading provider of market research, consulting, and advisory services to the global digital communications and collaboration sectors. The company's research focuses on the convergence of emerging technologies, including artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), with the communications and collaboration industries.

Competitor Research is an AI-powered tool that helps businesses analyze and understand their competitors. It provides a comprehensive research report on direct, indirect, substitute, and potential competitors, including insights on search traffic, keywords, backlinks, target audience, pricing strategy, website performance, and customer engagement. The tool uses AI to save time and deliver actionable insights to help businesses grow and stay ahead of the competition.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

hongbomiao.com is a personal research and development (R&D) lab that facilitates the sharing of knowledge. The repository covers a wide range of topics including web development, mobile development, desktop applications, API servers, cloud native technologies, data processing, machine learning, computer vision, embedded systems, simulation, database management, data cleaning, data orchestration, testing, ops, authentication, authorization, security, system tools, reverse engineering, Ethereum, hardware, network, guidelines, design, bots, and more. It provides detailed information on various tools, frameworks, libraries, and platforms used in these domains.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

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.

VerifAI is a document-based question-answering system that addresses hallucinations in generative large language models and search engines. It retrieves relevant documents, generates answers with references, and verifies answers for accuracy. The engine uses generative search technology and a verification model to ensure no misinformation. VerifAI supports various document formats and offers user registration with a React.js interface. It is open-source and designed to be user-friendly, making it accessible for anyone to use.

OntoGPT is a Python package for extracting structured information from text using large language models, instruction prompts, and ontology-based grounding. It provides a command line interface and a minimal web app for easy usage. The tool has been evaluated on test data and is used in related projects like TALISMAN for gene set analysis. OntoGPT enables users to extract information from text by specifying relevant terms and provides the extracted objects as output.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

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.

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.

Slideflow is a deep learning library for digital pathology, offering a user-friendly interface for model development. It is designed for medical researchers and AI enthusiasts, providing an accessible platform for developing state-of-the-art pathology models. Slideflow offers customizable training pipelines, robust slide processing and stain normalization toolkit, support for weakly-supervised or strongly-supervised labels, built-in foundation models, multiple-instance learning, self-supervised learning, generative adversarial networks, explainability tools, layer activation analysis tools, uncertainty quantification, interactive user interface for model deployment, and more. It supports both PyTorch and Tensorflow, with optional support for Libvips for slide reading. Slideflow can be installed via pip, Docker container, or from source, and includes non-commercial add-ons for additional tools and pretrained models. It allows users to create projects, extract tiles from slides, train models, and provides evaluation tools like heatmaps and mosaic maps.

AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

Skilled in Python, Linux, R, and more.

I assist with gene data queries and enable file downloads.

Compile instructions from the web and github for bioinformatics applications. Receive line-by-line instructions and commands to get started

Unlock the secrets of DNA with 🧬GenoCode Wizard🔬! Dive into genetic analysis, decode sequences, and explore bioinformatics with ease. Perfect for researchers and students!

生成操作流程的生物信息学 GPT

Assists in evaluating protein engineering tools.

Especialista em Biotecnologia e Análise de Dados Acadêmicos

A guide for ML & LLM bootcamp with a bioengineering background.

Academic-level genomics expertise.

Expert in microsporidia and omics, tailors responses to student or expert background

Advises on Bioconda recipes, following community guidelines

Especialista em dados de pós-graduação em Fisiologia

Echoes Watson & Crick, educates on DNA with depth.

Aids in biological science writing with concise, professional advice.

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

Enhances PubMed searches with MeSH and follow-up advice

Chemical biology expert for literature, data viz, and coding

Genome Guide: Powered by OpenAI - A leader in genomic data analysis.

Advanced Mycological Genomic Analysis Expert

Innovative GPT in genetic engineering, compliant with OpenAI's usage policies