Alternatives to ESMFold

With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.

GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding.

GPT-3.5 is the next evolution to GPT 3 large language model, OpenAI. GPT-3.5 models are able to understand and generate natural languages. There are four main models available with different power levels that can be used for different tasks. The main GPT-3.5 models can be used with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.

Language and its role as a means of demonstrating and facilitating understanding - or intelligence, as it is sometimes called - are fundamental to being human. It allows people to express themselves, build memories, and communicate ideas. These are the foundational components of social intelligence. Our teams at DeepMind are interested in the language processing and communication aspects, both for artificial agents and humans. As part of an broader portfolio of AI Research, we believe that the development and study more powerful language models, systems that predict and create text, have tremendous potential to build advanced AI systems. These systems can be used safely and effectively to summarise and provide expert advice, and follow instructions using natural language. Research is needed to determine the potential risks and benefits of language models before they can be developed.

Partek bioinformatics software provides powerful visualization and statistical tools in an intuitive interface. Researchers of all levels can explore genomic data faster and more efficiently than ever before. We turn data into discovery®. Our intuitive interface makes it easy for scientists to perform sophisticated array and NGS analysis using pre-installed workflows. Public and custom statistical algorithms can be used together to quickly and accurately distill NGS data into biological insights. Genome browser, Venn diagrams and heat maps, as well as other interactive visualizations, show the biology of your next generation sequencing and array data in vivid color. Our Ph.D. scientists can be reached at any time to assist with your NGS analysis. This product is specifically designed for next-generation sequencing applications that require high-level computing. It offers flexible installation and management options.

Gemini Ultra is an advanced new language model by Google DeepMind. It is the most powerful and largest model in the Gemini Family, which includes Gemini Pro & Gemini Nano. Gemini Ultra was designed to handle highly complex tasks such as machine translation, code generation, and natural language processing. It is the first language model that has outperformed human experts in the Massive Multitask Language Understanding test (MMLU), achieving a score 90%.

The expansion of large language model has led to significant advancements in natural language processing, understanding and generation. This study introduces a new system that uses Ascend 910 AI processing units and the MindSpore framework in order to train a language with over one trillion parameters, 1.085T specifically, called PanGu-Sigma. This model, which builds on the foundation laid down by PanGu-alpha transforms the traditional dense Transformer model into a sparse model using a concept called Random Routed Experts. The model was trained efficiently on a dataset consisting of 329 billion tokens, using a technique known as Expert Computation and Storage Separation. This led to a 6.3 fold increase in training performance via heterogeneous computer. The experiments show that PanGu-Sigma is a new standard for zero-shot learning in various downstream Chinese NLP tasks.

Gemini 2.0, an advanced AI model developed by Google is designed to offer groundbreaking capabilities for natural language understanding, reasoning and multimodal interaction. Gemini 2.0 builds on the success of Gemini's predecessor by integrating large language processing and enhanced problem-solving, decision-making, and interpretation abilities. This allows it to interpret and produce human-like responses more accurately and nuanced. Gemini 2.0, unlike traditional AI models, is trained to handle a variety of data types at once, including text, code, images, etc. This makes it a versatile tool that can be used in research, education, business and creative industries. Its core improvements are better contextual understanding, reduced biased, and a more effective architecture that ensures quicker, more reliable results. Gemini 2.0 is positioned to be a major step in the evolution AI, pushing the limits of human-computer interactions.

Qwen-7B, also known as Qwen-7B, is the 7B-parameter variant of the large language models series Qwen. Tongyi Qianwen, proposed by Alibaba Cloud. Qwen-7B, a Transformer-based language model, is pretrained using a large volume data, such as web texts, books, code, etc. Qwen-7B is also used to train Qwen-7B Chat, an AI assistant that uses large models and alignment techniques. The Qwen-7B features include: Pre-trained with high quality data. We have pretrained Qwen-7B using a large-scale, high-quality dataset that we constructed ourselves. The dataset contains over 2.2 trillion tokens. The dataset contains plain texts and codes and covers a wide range domains including general domain data as well as professional domain data. Strong performance. We outperform our competitors in a series benchmark datasets that evaluate natural language understanding, mathematics and coding. And more.

Cellenics can help you turn your single-cell sequencing data into meaningful insights. Cellenics is an open-source analytics tool developed by Harvard Medical School for single cell RNA sequencing data. Biomage hosts the community instance. It allows biologists to explore datasets of single-cells without having to write code, and helps scientists and informaticians work together more effectively. It can take you from count matrixes to publication ready figures in a matter of hours. It can also be seamlessly integrated into your workflow. It is fast, interactive and user-friendly. It's cloud-based and scalable. Cellenics, hosted by Biomage as a community instance, is free for academics with small/medium datasets (upto 500,000 cells). Over 3000 academic researchers use it to study cancer, cardiovascular health and developmental biology.

We have just launched the X1 large model series. Giga ML’s most powerful model can be used for pre-training, fine-tuning and on-prem deployment. We are Open AI compliant, so your existing integrations, such as long chain, llama index, and others, will work seamlessly. You can continue to pre-train LLM's using domain-specific databooks or docs, or company documents. The world of large-scale language models (LLMs), which offer unprecedented opportunities for natural language process across different domains, is rapidly expanding. Despite this, there are still some critical challenges that remain unresolved. Giga ML proudly introduces the X1 Large model 32k, a pioneering LLM solution on-premise that addresses these critical challenges.

ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation. ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks. ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately. ChatGPT can also process code and generate it.

GPT-3 models are capable of understanding and generating natural language. There are four main models available, each with a different level of power and suitable for different tasks. Ada is the fastest and most capable model while Davinci is our most powerful. GPT-3 models are designed to be used in conjunction with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.

GPT-4 (Generative Pretrained Transformer 4) a large-scale, unsupervised language model that is yet to be released. GPT-4, which is the successor of GPT-3, is part of the GPT -n series of natural-language processing models. It was trained using a dataset of 45TB text to produce text generation and understanding abilities that are human-like. GPT-4 is not dependent on additional training data, unlike other NLP models. It can generate text and answer questions using its own context. GPT-4 has been demonstrated to be capable of performing a wide range of tasks without any task-specific training data, such as translation, summarization and sentiment analysis.

Ai2 OpenScholar, a collaboration between the University of Washington's Allen Institute for AI and the University of Washington, is designed to help scientists navigate and synthesize the vast expanse of the scientific literature. OpenScholar uses a retrieval-augmented model of language to answer user queries. It does this by identifying relevant papers and then generating answers based on those sources. This ensures that information is accurate and linked directly to existing research. OpenScholar-8B set new standards for factuality and accuracy of citations on the ScholarQABench benchmark. OpenScholar-8B, for example, maintains a solid grounding in real retrieved articles in the biomedical domain. This is in contrast to models like GPT-4 which tend to hallucinate references. Twenty scientists from computer science, biomedicine and physics evaluated OpenScholar's answers against expert-written responses to evaluate its real-world application.

Adept is a ML product and research lab that builds general intelligence by enabling computers and humans to work together creatively. Designed and specifically trained to take actions on computers in response your natural language commands. ACT-1 is the first step in a foundation model which can be used with any software tool, API or website. Adept is creating a completely new way to accomplish tasks. It takes your goals in plain language and turns them into action on the software that you use every single day. We believe AI systems should be designed with users in mind -- where machines and people work together to find new solutions, make better decisions, and give us more time to do the things we love.

The ability of large language models to learn in zero- and few shots, despite being trained for hundreds of thousands or even millions of days, has been remarkable. These models are expensive to replicate, due to their high computational cost. The few models that are available via APIs do not allow access to the full weights of the model, making it difficult to study. Open Pre-trained Transformers is a suite decoder-only pre-trained transforms with parameters ranging from 175B to 125M. We aim to share this fully and responsibly with interested researchers. We show that OPT-175B has a carbon footprint of 1/7th that of GPT-3. We will also release our logbook, which details the infrastructure challenges we encountered, as well as code for experimenting on all of the released model.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

The toolkit was developed in the Data Sciences Platform of the Broad Institute. It offers a variety of tools, with a focus on variant detection and genotyping. Its powerful processing engine, high-performance computing capabilities and flexibility make it a great tool for any project. The GATK is a standard in the industry for identifying SNPs in RNAseq and germline DNA data. Its scope has now expanded to include somatic short variation calling, copy number (CNV), and structural variation (SV). The GATK includes not only the variant callers, but also many utilities that perform related tasks like processing and quality-control of high-throughput sequence data. It also bundles the Picard toolkit. These tools were designed primarily to process whole genomes and exomes generated by Illumina sequencing technology. However, they can be adapted for a variety other technologies and experimental design.

Our healthcare-specific Natural Language Processing and AI Engine stores more than 70% of healthcare data in clinical documents, reports and patient charts, clinician notes, discharge letters, and patient charts. This allows us to identify the concepts, attributes, and context that are needed to deliver business insight, optimize billing, identify and rank patient risks, compute quality metrics, collect sentiment and outcome data, and provide business insights.

GeoMx Digital Spatial Profiler allows you to quickly resolve tissue heterogeneity, and the complexity of the microenvironment with the most flexible and robust multi-omic spatial platform for analysis of FFPE tissue sections and fresh frozen tissue. GeoMx is a spatial biology platform which non-destructively profiles RNA and proteins from distinct tissue compartments, cell populations and an automated workflow that integrates standard histology staining. You can spatially profile the entire transcriptome and over 570 protein targets, either separately or simultaneously, using your choice of sample inputs. These include whole tissue sections (WTS), tissue microarrays or organoids. GeoMx DSP is the spatial biology platform you should choose for biomarker detection and hypothesis testing. Let the tissue guide you with a biology-driven profile that allows you to select the tissue microenvironments or cell types that are most important to you.

Interactive visualizations and analytics allow you to explore hundreds of single-cell transcriptome datasets as well as your own data. The software supports multimodal omics (e.g. CITE-seq and spatial transcriptomic. Explore the world's largest database of single-cell expression interactively. Access and query insights derived from a single cell database of millions of cells. The database is fully annotated, with cell type labels and experimental meta-data. BBrowser does not just create a portal to published works. It is an end-toend solution for YOUR single-cell data. Import your fastq, count matrices or Seurat objects and reveal the biological stories within. With a powerful package of visualizations, analyses and an intuitive interface you can easily mine insights from any single-cell dataset. Import data from single-cell CRISPR or Perturb-seq. Guide RNA sequences can be queried.

InstructGPT is an open source framework that trains language models to generate natural language instruction from visual input. It uses a generative, pre-trained transformer model (GPT) and the state of the art object detector Mask R-CNN to detect objects in images. Natural language sentences are then generated that describe the image. InstructGPT has been designed to be useful in all domains including robotics, gaming, and education. It can help robots navigate complex tasks using natural language instructions or it can help students learn by giving descriptive explanations of events or processes.

This free tool provides stunning visualizations of genomic data, giving you the power to see exactly what is happening at each base pair within your samples. GenomeBrowse is a desktop application that runs natively on your computer. You no longer have to compromise on speed or interface quality in order to achieve a consistent experience across platforms. It was designed with performance in mind, to provide a faster browsing experience than any genome browser currently available. GenomeBrowse has also been integrated into the powerful Golden Helix VarSeq annotation and interpretation platform. VarSeq is a powerful tool for filtering, analyzing, and annotating your data. If you enjoy the visualization experience provided by GenomeBrowse then try it out. GB can show all your alignment data. You can find context-relevant findings by looking at all your samples together.

John Snow Labs Medical LLM is a domain-specific large langauge model (LLM) that revolutionizes the way healthcare organizations harness artificial intelligence. This innovative platform was designed specifically for the healthcare sector, combining cutting edge natural language processing capabilities with a profound understanding of medical terminology and clinical workflows. The result is an innovative tool that allows healthcare providers, researchers and administrators to unlock new insight, improve patient outcomes and drive operational efficiency. The Healthcare LLM's comprehensive training is at the core of its functionality. This includes a vast amount of healthcare data such as clinical notes, research papers and regulatory documents. This specialized training allows for the model to accurately generate and interpret medical text. It is an invaluable tool for tasks such clinical documentation, automated coding and medical research.

We are a biotechnology company in clinical stage. We decode biology by integrating technological innovations across biology and chemistry to industrialize drug discovery. CRISPR genome editing and synthetic Biology allow for greater control over biology. Advanced robotics allows for reliable automation of complex laboratory research on an unprecedented scale. Neural network architectures allow for iterative analysis and inference from large, complex, in-house data sets. Cloud solutions increase the flexibility of high-performance computation. To build a next-generation biopharmaceutical business, we are using new technology to create virtuous learning cycles around datasets. A synchronized combination hardware, software, and data that is used to industrialize drug discovery. Redefining the traditional drug discovery process. One of the most extensive, broadest, and deepest pipelines in any technology-enabled drug company.

CodeGemma consists of powerful lightweight models that are capable of performing a variety coding tasks, including fill-in the middle code completion, code creation, natural language understanding and mathematical reasoning. CodeGemma offers 3 variants: a 7B model that is pre-trained to perform code completion, code generation, and natural language-to code chat. A 7B model that is instruction-tuned for instruction following and natural language-to code chat. You can complete lines, functions, or even entire blocks of code whether you are working locally or with Google Cloud resources. CodeGemma models are trained on 500 billion tokens primarily of English language data taken from web documents, mathematics and code. They generate code that is not only syntactically accurate but also semantically meaningful. This reduces errors and debugging times.

Qwen LLM is a family of large-language models (LLMs), developed by Damo Academy, an Alibaba Cloud subsidiary. These models are trained using a large dataset of text and codes, allowing them the ability to understand and generate text that is human-like, translate languages, create different types of creative content and answer your question in an informative manner. Here are some of the key features of Qwen LLMs. Variety of sizes: Qwen's series includes sizes ranging from 1.8 billion parameters to 72 billion, offering options that meet different needs and performance levels. Open source: Certain versions of Qwen have open-source code, which is available to anyone for use and modification. Qwen is multilingual and can translate multiple languages including English, Chinese and Japanese. Qwen models are capable of a wide range of tasks, including text summarization and code generation, as well as generation and translation.

OpenGPT is a German initiative that focuses on developing large AI languages models tailored to European requirements, with an emphasis on versatility, trustworthiness and multilingual capabilities. It also emphasizes open-source accessibility. The project brings together partners to cover the whole generative AI value-chain, from scalable GPU-based infrastructure to data for training large language model to model design, practical applications, and prototypes and proofs-of concept. OpenGPT-X aims at advancing cutting-edge research, with a focus on business applications. This will accelerate the adoption of generative AI within the German economy. The project also stresses responsible AI development to ensure that the models are reliable and aligned with European values and laws. The project provides resources, such as the LLM Workbook and a three part reference guide with examples and resources to help users better understand the key features and characteristics of large AI language model.

BLOOM (autoregressive large language model) is trained to continue text using a prompt on large amounts of text data. It uses industrial-scale computational resources. It can produce coherent text in 46 languages and 13 programming language, which is almost impossible to distinguish from text written by humans. BLOOM can be trained to perform text tasks that it hasn’t been explicitly trained for by casting them as text generation jobs.

EXAONE, a large-scale language model developed by LG AI Research, aims to nurture "Expert AI" across multiple domains. The Expert AI alliance was formed by leading companies from various fields in order to advance EXAONE's capabilities. Partner companies in the alliance will act as mentors and provide EXAONE with skills, knowledge, data, and other resources to help it gain expertise in relevant fields. EXAONE is akin to an advanced college student who has taken elective courses in general. It requires intensive training to become a specialist in a specific area. LG AI Research has already demonstrated EXAONE’s abilities in real-world applications such as Tilda AI human artist, which debuted at New York Fashion Week. AI applications have also been developed to summarize customer service conversations, and extract information from complex academic documents.

ChatGLM-6B, a Chinese-English bilingual dialogue model based on General Language Model architecture (GLM), has 6.2 billion parameters. Users can deploy model quantization locally on consumer-grade graphic cards (only 6GB video memory required at INT4 quantization levels). ChatGLM-6B is based on technology similar to ChatGPT and optimized for Chinese dialogue and Q&A. After approximately 1T identifiers for Chinese and English bilingual training and supplemented with supervision and fine-tuning as well as feedback self-help and human feedback reinforcement learning, ChatGLM-6B, with 6.2 billion parameters, has been able generate answers that are in line with human preference.

Through scientific rigor and human insight, healthcare breakthroughs can change the world, bringing hope to humanity. We believe that AI can help in this area, through collaboration between researchers, healthcare organisations, and the wider ecosystem. Today, we are sharing exciting progress in these initiatives with the announcement that Google's large language model (LLM) for medical applications, called Med PaLM 2, will be available to a limited number of customers. In the coming weeks, it will be available to a small group of Google Cloud users for limited testing. We will explore use cases, share feedback, and investigate safe, responsible and meaningful ways to utilize this technology. Med-PaLM 2, which harnesses Google's LLMs aligned with the medical domain, is able to answer medical questions more accurately and safely. Med-PaLM 2 is the first LLM that has performed at an "expert" level on the MedQA dataset consisting of US Medical Licensing Examination-style questions.

Our large language model can understand and reason with any observability data including unstructured data so you can quickly restore software and systems back to health. Our LLM is trained to understand and mitigate critical incidents across all types of architectures. This gives enterprise developers access to one of the world's top debugging experts. Our LLM was created to solve the most difficult part of the software development process - debugging incidents in production. Our model does not require any training and can be used with any observability data systems. It can learn from feedback and fine-tune based upon past incidents and patterns within your environment, while keeping your data within your boundaries. Flip can resolve critical incidents in seconds.

We solve the most difficult problems to make AI models efficient, helpful and reliable. We are the pioneers of open models. We give them to our users, and empower them to share their ideas. Mistral-7B is a powerful, small model that can be adapted to many different use-cases. Mistral 7B outperforms Llama 13B in all benchmarks. It has 8k sequence length, natural coding capabilities, and is faster than Llama 2. It is released under Apache 2.0 License and we made it simple to deploy on any cloud.

The next generation of the large language model. This release includes modelweights and starting code to pretrained and fine tuned Llama languages models, ranging from 7B-70B parameters. Llama 1 models have a context length of 2 trillion tokens. Llama 2 models have a context length double that of Llama 1. The fine-tuned Llama 2 models have been trained using over 1,000,000 human annotations. Llama 2, a new open-source language model, outperforms many other open-source language models in external benchmarks. These include tests of reasoning, coding and proficiency, as well as knowledge tests. Llama 2 has been pre-trained using publicly available online data sources. Llama-2 chat, a fine-tuned version of the model, is based on publicly available instruction datasets, and more than 1 million human annotations. We have a wide range of supporters in the world who are committed to our open approach for today's AI. These companies have provided early feedback and have expressed excitement to build with Llama 2

Our new flagship model, the Small Language Model (SLM), has all the power and performance that you would expect from a leading LLM. Excels at generalized tasks, instruction-following, and human preferences. The best 70B model available. SuperNova is a generalized task-based AI that can be used for any generalized task. It's similar to Open AI's GPT4o and Claude Sonnet 3.5. SuperNova is trained with the most advanced optimization & learning techniques to generate highly accurate responses. It is the most flexible, cost-effective, and secure language model available. Customers can save up to 95% in total deployment costs when compared with traditional closed-source models. SuperNova can be used to integrate AI in apps and products, as well as for general chat and a variety of other uses. Update your models regularly with the latest open source tech to ensure you're not locked into a single solution. Protect your data using industry-leading privacy features.

Smaug 72B is an open-source large-language model (LLM), which is known for its key features. High Performance: It is currently ranked first on the Hugging face Open LLM leaderboard. This model has surpassed models such as GPT-3.5 across a range of benchmarks. This means that it excels in tasks such as understanding, responding to and generating text similar to human speech. Open Source: Smaug-72B, unlike many other advanced LLMs is available to anyone for free use and modification, fostering collaboration, innovation, and creativity in the AI community. Focus on Math and Reasoning: It excels at handling mathematical and reasoning tasks. This is attributed to the unique fine-tuning technologies developed by Abacus, the creators Smaug 72B. Based on Qwen 72B: This is a finely tuned version of another powerful LLM, called Qwen 72B, released by Alibaba. It further improves its capabilities. Smaug-72B is a significant advance in open-source AI.

GPT-4 and other foundation models have accelerated AI's development. The most powerful models, however, are closed commercial models or partially open. RedPajama aims to create a set leading, open-source models. Today, we're excited to announce that the first phase of this project is complete: the reproduction of LLaMA's training dataset of more than 1.2 trillion tokens. The most capable foundations models are currently closed behind commercial APIs. This limits research, customization and their use with sensitive information. If the open community can bridge the quality gap between closed and open models, fully open-source models could be the answer to these limitations. Recent progress has been made in this area. AI is in many ways having its Linux moment. Stable Diffusion demonstrated that open-source software can not only compete with commercial offerings such as DALL-E, but also lead to incredible creative results from community participation.

LLaMA (Large Language Model meta AI) is a state of the art foundational large language model that was created to aid researchers in this subfield. LLaMA allows researchers to use smaller, more efficient models to study these models. This furtherdemocratizes access to this rapidly-changing field. Because it takes far less computing power and resources than large language models, such as LLaMA, to test new approaches, validate other's work, and explore new uses, training smaller foundation models like LLaMA can be a desirable option. Foundation models are trained on large amounts of unlabeled data. This makes them perfect for fine-tuning for many tasks. We make LLaMA available in several sizes (7B-13B, 33B and 65B parameters), and also share a LLaMA card that explains how the model was built in line with our Responsible AI practices.

MEGA (Molecular Evolutionary Genetics Analysis), a powerful, user-friendly software package designed to analyze DNA and protein sequences from species and populations. It allows for both manual and automatic sequence alignment, phylogenetic trees inference, and evolutionary hypotheses testing. MEGA is a powerful tool for comparative analysis of sequences and understanding molecular evolutionary processes. It supports a wide range of statistical methods, including maximum likelihood, Bayesian Inference, and ordinary least-squares. MEGA has advanced features like real-time captions to explain the results of the analysis and the methods used. It also uses the maximum composite likelihood method to estimate evolutionary distances. The software comes with powerful visual tools such as the alignment/trace editors and tree explorers, and supports multi-threading to ensure efficient processing. MEGA is compatible with Windows, Linux and macOS.

Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulatory in RNA-Seq sample. It accepts aligned RNA Sequence reads and assembles them into a minimal set of transcripts. Cufflinks estimates the relative abundances for these transcripts by calculating how many reads each one receives, while taking into account biases from library preparation protocols. Cufflinks is the result of a collaboration between the Laboratory for Mathematical and Computational Biology. We provide binary packages for Cufflinks to make the installation process easier. This saves users the sometimes frustrating task of building Cufflinks which requires you to install the libraries. Cufflinks comes with a number tools for analyzing RNASeq experiments. Some of these tools are standalone, while others form part of a larger workflow.

Code Llama, a large-language model (LLM), can generate code using text prompts. Code Llama, the most advanced publicly available LLM for code tasks, has the potential to improve workflows for developers and reduce the barrier for those learning to code. Code Llama can be used to improve productivity and educate programmers to create more robust, well documented software. Code Llama, a state-of the-art LLM, is capable of generating both code, and natural languages about code, based on both code and natural-language prompts. Code Llama can be used for free in research and commercial purposes. Code Llama is a new model that is built on Llama 2. It is available in 3 models: Code Llama is the foundational model of code; Codel Llama is a Python-specific language. Code Llama-Instruct is a finely tuned natural language instruction interpreter.

Eidogen-Sertanty’s Target Informatics platform (TIP), is the first global structural informatics system. It enables researchers to examine the druggable genome from an structural perspective. TIP increases the rapidly expanding body experimental protein structure information and transforms structure based drug discovery from an inefficient, data-scarce discipline to a high-throughput science with rich data. TIP is a tool that bridges the knowledge gap between bioinformatics (bioinformatics) and cheminformatics. It provides drug discovery researchers with a knowledge bank of information that is both unique and highly complementary to existing bio- and cheminformatics platform information. TIP's seamless integration between structural data management technology and unique target-to-lead analysis capabilities enhances every stage of the discovery pipeline.

The Bioconductor Project aims to develop open source software that allows for repeatable and precise analysis of biological data. We encourage a collaborative and inclusive community of data scientists and developers. Resources to maximize Bioconductor's potential. Our tutorials, guides, documentation, and guides cover everything from basic functionality to advanced features. Bioconductor is an open-source and open-development software that uses the R statistical language. It has an active user base and two releases per year. Bioconductor offers Docker images with every release, and supports Bioconductor in AnVIL. Bioconductor, founded in 2001, is an open-source project widely used in bioinformatics. Over 1,000 developers have contributed over 2,000 R packages, which are downloaded over 40 million times per year. Bioconductor is cited in over 60,000 scientific publications.

Language Understanding (LUIS), a machine learning-based service that builds natural language into apps and bots. Rapidly create custom models that are enterprise-ready and can be continuously improved. Natural language can be added to your apps. LUIS is a language model that interprets conversations to find valuable information. It extracts information from sentences (entities) and interprets user intentions (goals). LUIS is seamlessly integrated with the Azure Bot Service, making creating sophisticated bots easy. You can quickly create and deploy a solution faster by combining powerful developer tools with pre-built apps and entity dictionary, such as Music, Calendar, and Devices. The collective knowledge of the internet is used to create dictionaries. This allows your model to identify valuable information from user conversations. Active learning is used for continuous improvement of the quality of the models.

Choose two custom cell groups and compare their top differentially-expressed genes. Use millions of cells in the integrated CZ CELLxGENE Corpus for powerful analyses. Use an interactive, no-code interface to perform interactive analyses of a dataset. Explore how spatial, environmental and genetic factors influence gene expression patterns. Use published datasets to understand them or as a starting point for identifying new cell subtypes and states. Census allows you to access any custom slice of standard cell data from CZ CELLxGENE in R or Python. Explore an interactive encyclopedia that contains 700+ cell types, detailed definitions, markers genes, lineage and relevant datasets. Browse and download 1,000+ datasets and hundreds of standardized data sets that characterize the functionality of healthy human and mouse tissues.

Jinni's content-to-audience platform is based on taste and offers revolutionary personalization options for video content discovery as well as targeted digital advertising for entertainment companies. Jinni's Entertainment Genome™, which is made up of thousands of content attributes, or "genes", not only understands subtle differences in TV and film entertainment content but also each individual's entertainment preferences. This allows Jinni to match content titles with the right content titles. Our mission is to be the best content-to-audience platform available for entertainment brands. We use one platform to match and promote entertainment content to the right audience, significantly increasing the profitability of entertainment advertisers and platform operators. Jinni's semantic algorithms, which match content to users' preferences, have set the stage for the next generation in content discovery and recommendations.

Combining the multiomic data and medical history of an individual to deliver more personalized healthcare. Use purpose-built databases to support large-scale analyses and collaborative research across populations. Accelerate your research with scalable workflows, integrated computation tools and integrated computing. Protect patient privacy by ensuring HIPAA compliance and using built-in data access, logging and logging. AWS HealthOmics enables healthcare and life sciences organizations and their software partner to store, query and analyze genomic, transcriptionomic, or other omics data, and then generate insights using that data. Store and analyze omics for hundreds of thousands patients to understand the relationship between omics variation and phenotypes in a population. Create reproducible and traceable workflows for clinical multiomics to reduce turnaround time and increase productivity. Integrate multiomic analyses into clinical trials to test out new drug candidates.

Clara's domain specific tools, AI pretrained models, accelerated applications, and accelerated AI applications are enabling AI advances in many fields, including medical device, imaging, drug discovery and genomics. Holoscan allows you to explore the entire pipeline of medical device deployment and development. With the NVIDIA IGX Developer Kits, you can build containerized AI apps using the Holoscan SDK. The NVIDIA IGX SDK includes pre-trained AI model, healthcare-specific acceleration libraries and reference applications for medical devices.