Machine Learning
At Life Seq Data, we offer complete machine learning services, from data collection and preprocessing to advanced bioinformatics analyses. Our team of experts ensures high-quality and reliable results, supporting researchers worldwide in leveraging machine learning for biological insights and discoveries.
Our Services:
- Support Vector Machine (SVM) Algorithm: Classify cancer vs. healthy samples using gene expression data.
- K-Nearest Neighbors (KNN) Algorithm: Classify multiclass tissue types using gene expression data.
- Regression Algorithm: Analyze genome-wide associations using SNP data.
- Convolutional Neural Networks (CNN): Predict protein secondary structure from amino acid sequences.
- Random Forest Algorithm: Perform pathway-based classification using gene expression and SNP data.
- Recurrent Neural Networks (RNN): Predict sequence similarity using nucleotide sequences.
- Principal Component Analysis (PCA): Classify outliers using gene expression data.
- Hierarchical Clustering Algorithm: Cluster protein families using amino acid sequences.
- K-Means Algorithm: Cluster genes by chromosomes using gene expression data.
- T-Distributed Stochastic Neighbor Embedding (tSNE): Visualize single-cell RNA sequencing data.
- Non-Negative Matrix Factorization (NMF): Cluster gene expression profiles.
- Random Forest Regression and Extreme Gradient Boosting (XGB): Evaluate protein descriptors in rational protein engineering.
- Data Mining: Identify large-scale biomolecular interactions, including protein-protein and protein-DNA interactions.
- Deep Learning: Dimension reduction in single-cell gene expression data.
- DeepMNN: Correct batch effects in scRNA-seq data using mutual nearest neighbors.
- SCVIS Neural Networks: Identify cell types from single-cell transcriptomics data.
- Multiple Machine Learning Models: Predict and classify gene expression, RNA-binding proteins, and transcription factors.
- DeepLoc: Classify mutations and protein subcellular localization.
- Natural Language Processing (NLP): Access patient data efficiently with electronic medical records.
- Text Mining Algorithms: Search biological databases amidst the increase in biological publications.
- Markov Chain Optimization: Identify transcription binding sites.
- Probabilistic Graphical Models: Model genetic networks and regulatory structures.
- Genetic Algorithms: Model genetic networks and regulatory structures.
- Machine Learning in Drug Discovery: Use random forest, naive bayesian, and SVM in precision medicine and next-generation sequencing.
- Predict Motor Deficits: Use DNN, logistic regression, and random forest to predict motor deficits in stroke patients.
- Promoter Identification: Identify promoters in DNA sequences using multiple machine learning algorithms.
Machine Learning
Machine learning is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. AI significantly contributes to bioinformatics by leveraging pattern matching and knowledge-based learning systems to solve biological problems.
Machine Learning in Bioinformatics
Before the advent of machine learning, extracting valuable insights from large biological datasets was a significant challenge. Today, ML techniques such as deep learning can learn features of complex datasets and present them in an understandable manner. Machine learning applications in bioinformatics include genomics, proteomics, microarrays, systems biology, evolution, and text mining. It serves as an advanced tool in bioinformatics, aiding in molecular phenotypes, drug discovery, and determining unfamiliar diseases.
Deep Learning
Deep learning is a machine-learning algorithm that extracts high-level features from raw input by mimicking the human brain’s functioning. It is used in drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing, biomolecule interaction prediction, and more.
Neural Networks
Neural networks consist of interconnected neurons that aim to recognize the underlying relationship of a given set of data. They mimic human brain operations and have proven effective in pattern recognition. Neural networks are utilized in many bioinformatics areas, including disease classification and biomarker identification.
Types of Machine Learning Algorithms
Supervised Learning: Uses labeled biological datasets to train algorithms for classifying data or predicting outcomes accurately. Examples include SVM, KNN, CNN, random forest, RNN, and regression.
Unsupervised Learning: Analyzes and clusters unlabeled biological data to discover hidden patterns. Examples include PCA, hierarchical clustering, tSNE, k-means, and NMF.
Reinforcement Learning: Learns from the environment and supports automation. It is used in protein folding, health informatics, disease prediction, biomarker prediction, and NGS fragment assembly.
Machine Learning Tools in Bioinformatics
- DeepVariant: Used in genome data mining to predict common genetic variations accurately.
- Atomwise Algorithms: Predict molecules interacting with specific proteins with atomic precision for drug discovery.
- Cell Profile: Analyzes biological datasets, extracting meaningful information using deep learning techniques.
Why Choose Life Seq Data?
- State-of-the-Art Technology: We utilize the latest computational tools and machine learning methods.
- Expertise and Experience: Our team has extensive experience in applying machine learning to bioinformatics.
- Comprehensive Services: From data collection to advanced analyses, we offer complete machine learning services.
- Personalized Support: Tailored solutions to meet the specific requirements of your machine learning projects.
Explore our services and references to learn more about how we can support your machine learning needs. If you would like to collaborate with us, leave a message, and we will arrange a call with one of our bioinformaticians.
