You will get Custom Bioinforamtics Machine Learning Models for Your Data
Project details
Bioinformatics & Custom Deep Learning Solutions
Hello, I am Sheraz. If you are looking for advanced Machine Learning or Deep Learning specifically tailored for complex biological datasets, you have come to the right place. I specialize in turning high-dimensional -omics data and biological signals into actionable research insights.
What I Offer:
Genomics & Transcriptomics: scRNA-seq clustering, trajectory inference, and differential expression analysis.
Deep Learning Architectures: CNNs for sequence motifs, Transformers for protein modeling, and GANs for data synthesis.
Graph Neural Networks (GNNs): Modeling metabolic pathways and drug-target interactions (DTI).
Multi-Omics Integration: Harmonizing Proteomics, Metabolomics, and Clinical phenotypic data.
Signal & Image Processing: Deep Learning for EEG/EKG and automated pathology image analysis.
Why Choose Me:
Research Rigor: 3+ years of experience bridging data science with biological discovery.
Publication-Ready: High-resolution visualizations (UMAP, Volcano plots) and clean, documented code.
Custom Models: I can build deep learning architectures from scratch to fit your specific research niche.
Thank you
Hello, I am Sheraz. If you are looking for advanced Machine Learning or Deep Learning specifically tailored for complex biological datasets, you have come to the right place. I specialize in turning high-dimensional -omics data and biological signals into actionable research insights.
What I Offer:
Genomics & Transcriptomics: scRNA-seq clustering, trajectory inference, and differential expression analysis.
Deep Learning Architectures: CNNs for sequence motifs, Transformers for protein modeling, and GANs for data synthesis.
Graph Neural Networks (GNNs): Modeling metabolic pathways and drug-target interactions (DTI).
Multi-Omics Integration: Harmonizing Proteomics, Metabolomics, and Clinical phenotypic data.
Signal & Image Processing: Deep Learning for EEG/EKG and automated pathology image analysis.
Why Choose Me:
Research Rigor: 3+ years of experience bridging data science with biological discovery.
Publication-Ready: High-resolution visualizations (UMAP, Volcano plots) and clean, documented code.
Custom Models: I can build deep learning architectures from scratch to fit your specific research niche.
Thank you
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$90
|
Standard
$180
|
Advanced
$260
|
|---|---|---|---|
| Delivery Time | 5 days | 3 days | 3 days |
Number of Revisions | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 2 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Model Documentation | |||
Data Source Connectivity | - | - | |
Model Validation/Testing |
Frequently asked questions
5 reviews
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MF
Mohd F.
Sep 15, 2025
Step-by-Step HTGNN Code Explanation and Tutorial
Sheraz is a professional expert in his field. He attends to all project requirements and delivers them successfully.
PP
Paulina P.
Apr 1, 2025
bioinformatics
AA
Amal A.
Apr 6, 2024
Multiple datasets integration
Sheraz integrated different omics datasets for various tissue types. he was patient, understood the task well, and provided helpful advice. The work was completed on time. I'd gladly work with him again for similar tasks.
TN
Technique N.
Jan 20, 2024
Mobility Prediction with Deep Learning: help needed for a proposal
HF
Hend F.
Oct 23, 2023
GraphSage model
Sheraz is flexible and a quick responder and learner. He have experience in GNN and GraphSage for normal size graph. i liked working with him although we couldn't finish the whole work as large graphs needs dealing with big data not just graph algorithms.
Thanks Sheraz
Thanks Sheraz
About Sheraz
Bioinformatics | NGS | scRNA-seq | Multi-omics | Machine Learning
100%
Job Success
Multan, Pakistan - 4:25 pm local time
My work spans RNA-seq, multi-omics integration, and AI-driven biological modeling, from raw data processing to predictive modeling and publication-ready results.
Most clients come to me when they:
• Have large-scale biological data but need structured analysis
• Want to apply machine learning or deep learning to biological problems
• Need scalable, reproducible pipelines
• Require biologically meaningful interpretation of results
Core Bioinformatics Expertise
Transcriptomics & NGS Analysis
• Bulk RNA-seq (DESeq2 / edgeR / limma)
• scRNA-seq (Seurat / Scanpy / Monocle)
• Differential expression & biomarker discovery
• Functional enrichment & pathway analysis
NGS & Data Processing Pipelines
• FASTQ → QC → Alignment → Quantification workflows
• Variant calling & genomic analysis
• Automated, scalable pipeline development
• High-throughput data processing
Machine Learning & Deep Learning
AI for Biology
• Deep learning models for biological data
• CNNs, RNNs, and Transformer-based architectures
• Predictive modeling for genomics & drug discovery
• Feature extraction from high-dimensional datasets
Advanced Modeling
• Graph Neural Networks (GNNs) for biological networks
• Gene regulatory network modeling
• Multi-omics data integration (genomics, transcriptomics, proteomics)
• Explainable AI for biological interpretation
Reproducible Research & Engineering
• End-to-end R/Python workflows
• Clean, well-documented, GitHub-ready code
• Publication-quality figures & visualizations
• Methods writing and result interpretation support
Tools & Stack
R • Python • Bioconductor • Seurat • Scanpy
PyTorch • TensorFlow • scikit-learn
FASTQC • STAR • Salmon • GATK • BLAST
Linux • AWS / GCP
What You Can Expect
• Clear communication and realistic timelines
• Scientifically rigorous and reliable analysis
• Scalable and reproducible solutions
• Insights that connect data to biology
Steps for completing your project
After purchasing the project, send requirements so Sheraz can start the project.
Delivery time starts when Sheraz receives requirements from you.
Sheraz works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preprocessing & Quality Control
will clean the raw biological data, handle missing values, and perform normalization (e.g., Min-Max scaling or Z-score) to ensure the data is ready for deep learning architectures.
Model Architecture Design & Training
Selecting the best-fit model (CNNs for signal data, GNNs for molecular graphs, or Transformers for sequencing). I will implement the training pipeline using PyTorch or TensorFlow, including hyperparameter tuning.