Multimodal Gene Text Disease Prediction

@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 Matlab, Python, and similar tools. 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

@ahmadayaz

I propose to develop a robust data pipeline integrating gene expression matrices with clinical notes using PyTorch or TensorFlow. I will design a multimodal architecture with separate encoders for each modality and a fusion layer for accurate predictions. Training and evaluation scripts with comprehensive metrics will be implemented, including an ablation option for transparency. Deliverables will include Python source code, a detailed README, and a performance summary report. My expertise in multimodal deep learning, NLP preprocessing, and bioinformatics will ensure successful model training and outperformance of baselines. I'm eager to collaborate on this project and welcome any additional requirements you may have.

@kamran2012

I can help with this, Hi, I will deliver a PyTorch multimodal pipeline that pairs a gene expression encoder with a clinical text encoder (BioBERT or similar), fuses them via a cross attention layer, and outputs a disease risk score. The ablation mode will let you toggle each modality off to confirm the fusion variant beats single modality baselines. You will get a short update at the end of each day. Questions: 1) What format are your gene expression matrices in (CSV, h5ad, GEO)? 2) Are the clinical notes already labeled with disease/no disease, or do we need to derive labels? Send me a message and we can go over the details. Best regards, Kamran

@PINESCRIPTERR

Hi, I can develop a reproducible multimodal learning pipeline that preserves patient-level alignment between gene-expression matrices and clinical notes while preventing train/validation leakage. I recommend PyTorch with a normalized gene encoder and a pretrained clinical-language encoder, followed by gated or attention-based fusion and a calibrated binary prediction head. The training framework will include stratified patient-level splits, class-imbalance handling, deterministic seeds, checkpointing, and metrics covering ROC-AUC, PR-AUC, accuracy, precision, recall, F1, calibration, and confidence intervals. Ablation experiments will compare gene-only, text-only, and fused models using identical splits. I will also include statistical comparison and explainability outputs for influential genes and text features. The fusion model’s improvement will be measured transparently; a meaningful gain cannot be guaranteed before verifying dataset quality and complementary signal. Deliverables will include clean Python modules, training and inference scripts, a reproducible notebook, configuration files, README, and a concise results report. Question 1: How many aligned patients, gene features, and positive disease outcomes are available? Question 2: Are the clinical notes de-identified, and may a pretrained clinical language model be used? Regards, Houssame

@corpmember29

Hello!! I have similar kind of expertise and work experience for the **{{ MULTIMODAL GENE TEXT DISEASE PREDICTION }}**. I have worked on AI/ML, deep learning, NLP, and data-driven prediction systems and can share relevant experience. I have 10+ years of experience in software development and AI solutions, with expertise in Python, PyTorch, TensorFlow, NLP pipelines, machine learning models, data preprocessing, and predictive analytics. I can develop a multimodal deep learning system that combines gene expression data and clinical text using separate encoders, advanced fusion techniques, and prediction models. I can also build complete data pipelines, training/inference workflows, evaluation metrics, and reproducible notebooks with proper documentation. The solution will include clean source code, model architecture documentation, performance analysis, and a scalable structure for future improvements and fine-tuning. I will provide complete source code, technical documentation, 2 years of FREE ongoing support after project completion, and assistance with deployment and future enhancements. I eagerly await your positive response. Thanks.

@nrajuu07

I'm a deep learning engineer with experience building multimodal architectures in PyTorch combining structured biological features and clinical text, including separate encoders, cross-modal fusion layers, and binary classification heads. I'll build the full pipeline: gene expression ingestion with normalisation, clinical note tokenisation using a pretrained transformer encoder, a fusion architecture capturing cross-modal interactions, and training and evaluation scripts with AUC, precision-recall, accuracy, and ablation runs isolating each modality's contribution. You'll receive clean documented Python source code, a reproducible notebook on your sample dataset, a clear README covering environment setup and fine-tuning guidance, and a short performance report. Ready to start immediately. Relevant multimodal and bioinformatics project samples available on request.

@emilesd617

Hi, I am a machine learning developer specializing in multimodal deep learning and biomedical data analysis with 8 years of rich experience in software development. I am familiar with Python, PyTorch, TensorFlow, Machine Learning, Deep Learning, NLP, Bioinformatics Data Processing, Data Science, Model Evaluation, and Data Analysis. I understand that you need a multimodal disease-prediction system that combines gene-level features with clinical text while preserving correct sample alignment. I can build separate encoders for both modalities, implement a fusion layer for cross-modal learning, create reproducible training and inference pipelines, compare the fused model against single-modality baselines, and report AUC, accuracy, precision-recall, and ablation results clearly. 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. Looking forward to discussing more details. Thanks. Emile.

@mohjib092

Hello, Your project is a great fit for my experience in deep learning, multimodal AI systems, NLP, and biomedical data processing. I can help you build a robust pipeline and model that combines **gene expression features with clinical text data** to predict disease risk using a well-structured and reproducible approach. I will develop a complete data workflow that aligns gene matrices with clinical notes, including preprocessing, normalization, tokenization, feature extraction, and validation of sample consistency. The model will include separate encoders for each modality, a fusion mechanism to learn cross-modal interactions, and a prediction layer for binary classification or risk scoring. Do you already have the gene expression data and clinical notes paired at the patient/sample level, or will the pipeline also need to handle the initial data matching and quality validation process? Best regards.

@Aaleen110

I understand you need a deep-learning system to fuse gene expression data and clinical text for disease prediction. My experience with Python and ML includes building models that process diverse data inputs to achieve specific classification goals. I previously developed a multimodal model that combined imaging and patient history data to predict treatment response with 92% accuracy. I will build a data pipeline using Python to ingest your gene expression matrices and clinical notes. For feature extraction, I'll use established libraries for text processing and gene data manipulation. The multimodal architecture will be implemented using TensorFlow or PyTorch, creating a joint representation of gene and text features to output a binary risk score. Regarding the clinical text, will the notes be structured in a consistent format, or should I account for significant variability in their structure and content? Ready to start as soon as you confirm scope.

@alizain721

Hi, I will build the multimodal gene plus clinical-text prediction pipeline you described: a reproducible PyTorch implementation with a clean ingestion layer that preserves sample alignment, tokenisation and normalization for clinical notes and gene matrices, separate encoders for each modality, a cross attention fusion layer capturing cross-modal interactions, and training and evaluation scripts that report AUC, accuracy and precision recall plus an ablation switch to isolate each modality. I previously delivered a comparable PyTorch system on a 5,000-sample clinical cohort that showed clear AUC gains from fusion over single-modality baselines. My code will include a runnable notebook, README, and a short performance report with chosen hyperparameters and statistical tests. If you share the sample dataset or repo access I will run initial baseline vs fusion experiments and send a short written list of findings and reproducibility notes within 48 hours, free of charge. Happy to jump on a quick chat. Ali Zain

@sufyanasmat38

Hi! I have experience building multimodal deep learning pipelines using PyTorch, combining structured biological data with NLP models to produce accurate and reproducible predictions. I can develop a clean architecture with dedicated encoders for gene expression data and clinical text, an effective fusion mechanism, and a well optimized prediction pipeline for disease risk classification. The solution will include robust preprocessing, modular training and inference scripts, comprehensive evaluation with AUC, accuracy, precision recall, and ablation studies to demonstrate the value of multimodal learning. I focus on clean, well documented code, reproducible experiments, and explainable model design that can be extended as your dataset grows. Once you share the sample dataset and data specifications, I can build a complete end to end pipeline with clear documentation, a reproducible notebook, and a concise performance report. I’m ready to start immediately and deliver a reliable, research quality solution.

@demivision

Hi there, Thank you for outlining such an exciting and meaningful project. We are Demivision LLC, a specialized team with deep expertise in multimodal deep learning, NLP for clinical text, and bioinformatics data pipelines. Your goal—to build an integrated model that fuses gene expression data with free-form clinical notes for disease prediction—aligns perfectly with our experience in both computational biology and applied machine learning. We fully understand the importance of a robust, aligned data pipeline that preserves the correspondence between gene matrices and clinical notes. Our approach would begin by designing modular preprocessing routines: normalizing and encoding gene features, while leveraging advanced NLP tools for tokenizing and embedding clinical text, all while ensuring strict alignment per sample. For the model, we propose a dual-encoder architecture—using transformer or LSTM-based encoders for text, and a dense or convolutional encoder for gene data—followed by a fusion mechanism (such as concatenation with cross-attention or gating) to capture synergistic effects. The prediction head will output both binary and probabilistic scores, with training scripts incorporating clear metrics (AUC, accuracy, precision-recall) and an ablation framework to quantify each modality’s contribution. You’ll receive well-documented, modular Python code (PyTorch or TensorFlow), a reproducible notebook, and a concise report detailing performance and hyperparameters. Our workflow emphasizes clarity, reproducibility, and extensibility—making it easy to adapt the pipeline to future datasets or modalities. We’d love to collaborate with you and deliver a solution that not only meets but exceeds your expectations. Please let us know if you’d like to discuss any aspect in more detail. Best regards, The Demivision LLC Team

@albertc1996

The key challenge is ensuring proper alignment between gene-expression samples and clinical notes while building a fusion model that genuinely outperforms single-modality baselines. I would build a PyTorch pipeline with dedicated gene and text encoders, a cross-modal fusion layer, and a complete training/evaluation workflow including AUC, precision-recall, and ablation analysis. I can also include cross-validation, model checkpointing, and reproducible experiment tracking for future extensions. What are the approximate dataset size and the dimensionality of the gene-expression features?

@ashinnd84

Hello Sir/Madam, we are a team of senior AI/ML, Automation Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@mahads31

With my broad skillset and extensive experience in Python, I am incredibly well-suited for your multimodal gene text disease prediction project. I've successfully developed multiple models that combined different types of data, including gene features and textual features to make predictions. Plus, my proficiency in deep learning, NLP preprocessing, and bioinformatics-style gene features are a great match for this project. In terms of your deliverables, I can provide you with high-quality Python source code for the model and all necessary scripts as per your requirement. I also have a strong background in building data-pipelines that will ensure smooth ingestion of your gene expression matrices alongside the associated clinical notes while preserving sample alignment. Moreover, my expertise in PyTorch and TensorFlow will guarantee a well-documented model with separate encoders for each modality and an efficient fusion layer. Lastly, using cutting-edge evaluation metrics such as AUC, accuracy, precision-recall along with an ablation option is something I'm familiar with and can integrate seamlessly into your project. My ultimate focus is not just delivering working code but rather gaining valuable insights throughout the process. If you choose me, you can rest assured that you'll receive not only a high-performance system but also a runnable notebook or script and a comprehensive README to reproduce,

@jonasdedzo

Hello, Your project is an interesting application of multimodal AI, and I’d love to help develop the system. I can create the complete workflow, from preparing and aligning gene expression data with clinical text, through feature extraction, model training, fusion architecture design, and performance evaluation. I can work with PyTorch or TensorFlow and provide a structured implementation with notebooks/scripts, baseline comparisons, metrics such as AUC and precision-recall, and documentation explaining the setup and usage. My goal would be to build a reproducible model that clearly demonstrates the value of combining both data sources. Could you share details about the available dataset format, target disease labels, and the size of your gene and clinical text samples so I can plan the most suitable architecture? Looking forward to discussing the project.

@akgarg007

Hello, This project perfectly aligns with my expertise in building AI-powered applications and handling complex data pipelines. I'm particularly excited about the multimodal deep learning aspect, combining gene data with clinical text for disease prediction. My plan is to: 1. Develop a robust Python data pipeline to ingest and align your gene expression matrices and clinical notes, including necessary preprocessing. 2. Build a well-documented PyTorch model featuring separate encoders for each modality and a sophisticated fusion layer to capture cross-modal interactions. 3. Implement comprehensive training and evaluation scripts with the requested metrics and an ablation study option. I'm confident I can deliver high-quality, reproducible results within your timeline. Let's discuss how I can bring this innovative system to life for you. Best regards, Ashwani

@melopgabriel

Hi, Your project is a strong fit for a multimodal PyTorch pipeline that joins gene expression matrices with clinical notes for disease prediction. I can build the full workflow so sample alignment stays intact from ingestion through training and evaluation. I’ve worked on deep-learning systems that combine structured biomedical data with NLP features, including separate encoders, fusion layers, and ablation-friendly training loops. For this, I’d use clean preprocessing for gene normalization and text tokenization, then design two modality-specific encoders with a fusion block that learns shared representations before the final classifier. I’ll also provide reproducible training and inference scripts, AUC/PR/accuracy reporting, and a notebook plus README so the system is easy to run and extend. The code will be documented and organized for review. If you’d like, I can outline the model structure and delivery plan next. Best regards, Gabriel

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings