Build Neoantigen Prediction Tool for Brain Therapeutics

@AwaisChaudhry

Hello, Your seven-stage brain-specific neoantigen prediction pipeline is an excellent fit for my Python and Machine Learning (ML) experience. I can structure the workflow from CPTAC, SysteMHC, GEO, PRIDE, TCGA, BrainMetShare, GTEx, AFND, and IEDB inputs through reproducible preparation, feature engineering, ensemble modeling, validation, equity screening, and handover. I will build maintainable Python modules for metadata tracking, batch-aware splits, leakage-safe negative generation, missing-value handling, and the 23-feature matrix. The modeling layer can integrate XGBoost, Random Forest, PyTorch neural networks, logistic-regression stacking, and the Composite Immunogenicity Score, with ROC/PR metrics, Precision@20, SHAP interpretation, benchmark comparisons, and CPV/pPVI scorecards. Documentation will cover installation, environment management, reproducibility, assumptions, and execution of each stage. I will also clearly isolate external-tool dependencies such as NetMHCpan, MHCflurry, NetChop, BLAST, and pVACtools so your team can run or replace them consistently. Since the scope spans computational biology, data science, and validation, I recommend confirming dataset readiness and tool-access constraints before implementation. Which datasets and immunoinformatics tool outputs are already preprocessed, and which integrations should the first milestone prioritize? I’m Muhammad Awais, and I’m ready to turn the detailed step list into a transparent, testable research pi

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Brain-Specific Neoantigen Prediction Tool with ML Expertise ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for a Computational Biologist/ML Engineer for a neoantigen prediction tool. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects in bioinformatics and machine learning. I will leverage my expertise to build a robust pipeline that meets all your needs efficiently. ➡️ Why Me? I can easily handle your neoantigen prediction project as I have 5 years of experience in Python programming, machine learning, and data analysis. My expertise includes working with immunoinformatics tools and processing genomic data. Additionally, I have a strong grip on ensemble models and feature engineering techniques, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail, and I can share samples of my previous work. I look forward to connecting with you soon! ➡️ Skills & Experience: ✅ Python (pandas, numpy) ✅ Machine Learning (scikit-learn, xgboost, pytorch) ✅ Data Processing (WES/RNA-seq) ✅ Immunoinformatics Tools (NetMHCpan, MHCflurry) ✅ Feature Engineering ✅ Model Development ✅ Validation Techniques ✅ Data Normalization ✅ Population Genetics ✅ Documentation Skills ✅ Clear Communication ✅ Project Management Waiting for your response! Best Regards, Zohaib

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@devendrathakur12

Hi, I’m a Senior ML Engineer with 20+ years in Python and machine learning research. I have gone through your specific requirement for brain neoantigen prediction pipeline I built something like this for quantitative research, with 20+ years of engineering. I would use XGBoost with SHAP over relying mainly on the neural network because your 23 engineered biological features need clearer feature level interpretation. I will build the Python pipeline around pandas, scikit learn and XGBoost, with PyTorch handling the neural network stage. I will structure the seven stages so dataset splits happen before feature fitting, avoiding leakage from related samples. And I will add the requested CIS and CPV/pPVI calculations with held out evaluation, at least that is where I would start. I can send ML pipeline samples and research work examples. I should be clear I have not used NetMHCpan or MHCflurry directly in a delivered project. Are the preprocessed datasets already labelled and HLA typed? Which exact 20 TCGA and 20 BrainMetShare samples should define the first benchmark? Should the 8 to 12 week scope include independent reproduction of every public baseline? Free for a quick call this week? Or answer those three and I’ll map the pipeline first. Dev Singh

@belarjsoufiane20

Hi, I’m a data analyst/statistical engineer with 7+ years of experience in Python, R, statistical modeling, and machine learning. I can handle the ML and data-processing components of your pipeline, including feature engineering, XGBoost, Random Forest, PyTorch, cross-validation, ensemble modeling, SHAP, benchmarking, and reproducible reporting. I’m also comfortable working with WES/RNA-seq and complex research datasets. For the immunoinformatics tools, I can integrate NetMHCpan, MHCflurry, NetChop, and pVACtools based on your specified workflow and datasets. I estimate 2-4 weeks for the full project, with a budget around $700, depending on the preprocessing requirements.

@emilesd617

Hi, I am a Python machine learning developer with 8 years of rich experience in software development, with a strong background in data science, reproducible ML pipelines, ensemble models, and statistical analysis. I am familiar with Python, Pandas, NumPy, scikit-learn, XGBoost, PyTorch, SHAP, feature engineering, cross-validation, genomic data processing, HLA/neoantigen workflows, data visualization, and reproducible environments. For your pipeline, I can implement the seven stages from data preparation and 23-feature generation through ensemble training, validation, SHAP analysis, CPV/pPVI scoring, benchmarking, and documented reproducible handover, integrating tools such as NetMHCpan, MHCflurry, NetChop, and pVACtools as required. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Thanks. Emile.

@maxscender

I’ll treat this as an end-to-end engineering problem: source and harmonize the public datasets first, then build the 23-feature matrix with explicit handling of missing values and batch effects so the labels stay trustworthy. For model development, I’ll implement the specified XGBoost, Random Forest, and neural network with early stopping, then stack them via logistic regression to produce the Composite Immunogenicity Score. Validation will focus on held-out test performance and direct benchmarking against NetMHCpan, MHCflurry, and pVACtools, with SHAP and feature importance used to confirm biological plausibility rather than just fit. For the equity pre-screen, I’ll code CPV/pPVI directly from AFND allele frequencies, generate the scorecard across the five populations, and apply the promiscuous-peptide mitigation in a way that is traceable and reproducible. I’ll keep the pipeline modular, documented, and ready for handover, with all preprocessing and model steps runnable from a single environment file. The result will be a practical, interpretable tool your team can extend, not just a one-off analysis.

@shadabkhan92

As an AI-driven team, we take pride in building more than just prototypes. Our strength lies in creating production-grade ML infrastructure that makes a real impact on existing workflows. I bring with me a strong background in Python, specifically in libraries like pandas, numpy, scikit-learn, xgboost, and pytorch - exactly what you need to build your brain-specific neoantigen prediction tool. On top of my deep understanding of immunoinformatics tools like NetMHCpan, MHCflurry, NetChop - which we will integrate seamlessly into the pipeline - I also possess rich experience working with WES/RNA-seq data processing, furthering my suitability for this project. This proficiency bridges well with my strong math foundation and hands-on experience in ensemble ML models and SHAP values. While I understand that cancer immunology and immuno-oncology background are nice-to-haves, I assure you that our approach makes up for this. We appreciate the detailed step list you have provided and thrive on crossing boundaries to make intelligent systems that ship. On top of our core AI skills, we also have experience with population genetics, immunooinformatics concepts like AFND and CPV/pPVI – augmenting our ability to produce actionable results. With us on board, you can expect a solution that not only delivers exceptional predictive power but is also scalable via Odoo ERP and has robust IoT interface capabilities. Let's get your brain-neoantigen prediction tool to work!

@sf264

Hello, Your brain-specific neoantigen pipeline is a strong match for my Python and ML background. I can build the 23-feature pipeline, integrate tools such as NetMHCpan/MHCflurry, train the XGBoost + Random Forest + neural network ensemble, and handle SHAP, CIS and CPV/pPVI validation. I can keep the workflow reproducible with clean code, documentation, and an environment file. I’d be happy to discuss the datasets, implementation approach, timeline, and deliverables in chat. You can also check my portfolio and previous work for relevant ML projects. Best regards, Fahad

@mayr81

I can build the full 7-stage neoantigen prediction pipeline for brain metastasis mRNA/LNP/PNA therapeutics as specified, covering data sourcing, labeling, a 23-feature assembly, ensemble models (XGBoost/RF/NN + logistic meta-learner), and CIS scoring. The work will include rigorous batching-aware 70/15/15 splitting, robust preprocessing for missing values, and reproducible benchmarking on a held-out test set with AUC-ROC/AUC-PR plus Precision@20/Recall@20/F1. I will integrate comparative baselines (NetMHCpan, MHCflurry, pVACtools), generate XGBoost/RF feature importance, and produce SHAP explanations with a ranked top-10 feature list. For equity screening, I will implement CPV and pPVI exactly as described across EUR/AFR/EAS/SAS/AMR, apply CPV thresholds (PASS/CAUTION/FAIL), and provide the mitigation step by adding promiscuous peptides binding multiple common HLA alleles, then recompute CPV post-mitigation. Deliverables will be clean, documented Python code plus an environment file and a structured handover package to your team.

@rezamahi

I’m a Computational Biologist / ML Engineer with 6+ years of experience in Python-based bioinformatics, machine learning, data analysis, and reproducible scientific pipelines. I can build the proposed 7-stage pipeline from data sourcing and preprocessing through feature engineering, ensemble modelling, validation, equity pre-screening, and final handover. I’m comfortable with pandas, NumPy, scikit-learn, XGBoost, PyTorch, SHAP, WES/RNA-seq processing, HLA-related analysis, and reproducible ML workflows. For the immunoinformatics component, I can integrate and structure outputs from NetMHCpan, MHCflurry, NetChop, pVACtools, BLAST, IEDB and AFND into the specified 23-feature matrix, while keeping positive/negative labelling, batch-aware splitting, leakage prevention, and validation methodology clearly documented. I can implement the XGBoost, Random Forest, neural network and logistic-regression ensemble, followed by AUC-ROC/AUC-PR, Precision@20, Recall@20, F1, feature importance and SHAP analysis. I’ll also implement the CPV/pPVI calculations, five-population scorecards, mitigation analysis, and reproducible environment/code handover. I can provide anonymised examples of comparable Python ML/bioinformatics pipeline work and a clear milestone plan before starting. I’m ready to begin with Stage 1 and the supplied datasets.

@ianb65

Hi there, Your brain-specific neoantigen pipeline needs precise data processing, clean labeling, and reproducible modeling. I have strong expertise in exactly this type of Python, Data Visualization, and Data Processing work, and I can build the 7-stage workflow end to end: source and organize the datasets, engineer the 23-feature matrix, train and validate the XGBoost, Random Forest, neural network, and meta-learner, then package the reporting and handover so your team can reuse it confidently. Best regards, Ian

@SkillCrafts

I understand you need to build a brain-specific neoantigen prediction tool, similar to how I've previously integrated diverse immunopeptidomics datasets to identify patient-specific tumor antigens. My experience with handling large-scale genomic and proteomic data from repositories like CPTAC and GEO will be directly applicable here. My approach will involve a multi-step pipeline. First, I'll script automated data retrieval for MS immunopeptidomics from CPTAC, SysteMHC, GEO, and PRIDE, ensuring data quality and format standardization. Concurrently, I'll download TCGA WES/RNA-seq data for the specified 20 samples. Subsequent stages will focus on variant calling, peptide generation, HLA binding prediction using established algorithms (e.g., NetMHCpan), and MHC-I presentation prediction, tailored for brain metastasis contexts. To ensure alignment, could you clarify the specific HLA alleles you intend to prioritize for brain metastasis? Also, what is the expected output format for the predicted neoantigens? I'm eager to discuss how my technical expertise can accelerate the development of this crucial tool.

@sufyanasmat38

Hello, I have strong experience with Python, pandas, NumPy, scikit-learn, XGBoost, PyTorch, ensemble models, SHAP, and reproducible ML pipelines. I can implement the 7-stage neoantigen prediction workflow including feature engineering, model training, validation, benchmarking, equity scoring, mitigation, and complete documentation. I’m comfortable working with biological datasets, HLA-related features, WES and RNA-seq data, and building reproducible research pipelines with clear evaluation and reporting. I can start immediately and would be happy to review your step list and preprocessed data.

@MZITSERVICES

I am an experienced Python framework developer specializing in Django, Flask, and FastAPI with a strong track record of building secure, scalable, and high-performance applications. I develop powerful backend systems, RESTful APIs, automation tools, dashboards, and database-driven platforms with clean, optimized code. My focus is on speed, reliability, and long-term maintainability. I can handle complete project development, bug fixing, API integrations, deployment, and performance optimization efficiently. With strong problem-solving skills, fast communication, and commitment to deadlines, I am confident in delivering professional solutions that exceed expectations and help grow your business successfully. I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@longngoc2307

Perfect Match "NEOANTIGEN PREDICTION PIPELINE" — the main challenge is turning heterogeneous immunopeptidomics, WES/RNA-seq, HLA, and population data into a reproducible feature matrix without leakage between train and test sets. I’d structure the Python pipeline as separate data, feature, model, validation, and reporting stages so every result can be reproduced. For the ML layer, I’d implement the XGBoost, Random Forest, neural network, and meta-learner with batch-aware splits, then generate the requested ROC/PR, Precision@20, SHAP, and feature-importance outputs. I can work from the preprocessed data you provide and document each stage so your team can reproduce the environment and results. Which datasets and preprocessing steps are already completed, and do you already have the 23-feature matrix or should that be built from the supplied data?

@qualityxenter

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@melopgabriel

Hi, your 7-stage pipeline is clearly defined, and I can help turn it into a reproducible neoantigen prediction workflow for brain metastasis therapeutics. I’ve built Python-based ML and bioinformatics pipelines with pandas, numpy, scikit-learn, xgboost, and PyTorch, including data preparation, model training, and evaluation. I’m also comfortable working with WES/RNA-seq outputs, HLA-related features, and model interpretation with SHAP. My approach would be to structure each stage cleanly: source and track datasets, prepare positive and negative sets carefully, engineer the 23-feature matrix, then train and compare XGBoost, Random Forest, neural net, and the meta-learner. I’d keep the code modular, document assumptions, and make the final handover easy for your team to run and extend. If you’d like, I can map this into a concrete build plan and delivery path. Best regards, Gabriel

@FizzaNadeemK

Hi, this is a strong match for my background in Python, ML and bioinformatics. I can work through the pipeline from data preparation and feature engineering to the ensemble models, SHAP analysis, population-level equity scoring and final reproducible handover. I’m comfortable with pandas, scikit-learn, XGBoost, PyTorch and the kind of WES/RNA-seq and HLA-related data handling this project requires. I’d estimate around 8–10 weeks for the full pipeline, depending on dataset access and preprocessing, with a budget in your stated range. For similar work, I can share relevant ML/bioinformatics examples and discuss exactly what I handled in each. I’m also happy to start by reviewing your 7-stage plan and confirming the implementation approach before development begins.