# Machine Learning in Bioinformatics

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-in-bioinformatics/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the prediction of protein subcellular localization using various computational methods such as amino acid composition, machine learning algorithms like support vector machines, and the analysis of signal peptides and transmembrane topology. The research aims to improve the accuracy and reliability of predicting the subcellular location of proteins, which has significant implications for understanding protein function and cellular processes. |
| Domain | Life Sciences |
| Field | Biochemistry, Genetics and Molecular Biology |
| OpenAlex ID | t12254 |
| Works | 66 |

## Topic papers all

Showing 15 of 66.

- [Metabolomics--the link between genotypes and phenotypes.](https://scholariq.org/papers/metabolomics-the-link-between-genotypes-and-phenotypes/)
- [Evolutionary and Biomedical Insights from the Rhesus Macaque Genome](https://scholariq.org/papers/evolutionary-and-biomedical-insights-from-the-rhesus-macaque-genome/)
- [KaKs_Calculator: Calculating Ka and Ks Through Model Selection and Model Averaging](https://scholariq.org/papers/kaks-calculator-calculating-ka-and-ks-through-model-selection-and-model/)
- [SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse](https://scholariq.org/papers/syngo-an-evidence-based-expert-curated-knowledge-base-for-the-synapse/)
- [Deep Learning and Its Applications in Biomedicine](https://scholariq.org/papers/deep-learning-and-its-applications-in-biomedicine/)
- [Constructing biological knowledge bases by extracting information from text sources.](https://scholariq.org/papers/constructing-biological-knowledge-bases-by-extracting-information-from-text/)
- [Global topological features of cancer proteins in the human interactome](https://scholariq.org/papers/global-topological-features-of-cancer-proteins-in-the-human-interactome/)
- [Deep Learning in Mining Biological Data](https://scholariq.org/papers/deep-learning-in-mining-biological-data/)
- [Critical assessment of protein intrinsic disorder prediction](https://scholariq.org/papers/critical-assessment-of-protein-intrinsic-disorder-prediction/)
- [Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones](https://scholariq.org/papers/integrative-annotation-of-21-037-human-genes-validated-by-full-length-cdna/)
- [Complex-Valued Networks for Automatic Modulation Classification](https://scholariq.org/papers/complex-valued-networks-for-automatic-modulation-classification/)
- [MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data](https://scholariq.org/papers/msvm-rfe-extensions-of-svm-rfe-for-multiclass-gene-selection-on-dna-microarray/)
- [LFQ-Analyst: An Easy-To-Use Interactive Web Platform To Analyze and Visualize Label-Free Proteomics Data Preprocessed with MaxQuant](https://scholariq.org/papers/lfq-analyst-an-easy-to-use-interactive-web-platform-to-analyze-and-visualize/)
- [AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities](https://scholariq.org/papers/alphafold-protein-structure-database-and-3d-beacons-new-data-and-capabilities/)
- [PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning](https://scholariq.org/papers/ppr-meta-a-tool-for-identifying-phages-and-plasmids-from-metagenomic-fragments/)

## Topic primary papers

- [KaKs_Calculator: Calculating Ka and Ks Through Model Selection and Model Averaging](https://scholariq.org/papers/kaks-calculator-calculating-ka-and-ks-through-model-selection-and-model/)
- [Deep Learning and Its Applications in Biomedicine](https://scholariq.org/papers/deep-learning-and-its-applications-in-biomedicine/)
- [Deep Learning in Mining Biological Data](https://scholariq.org/papers/deep-learning-in-mining-biological-data/)
- [Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods](https://scholariq.org/papers/prediction-of-antimicrobial-peptides-based-on-sequence-alignment-and-feature/)
- [GPS-Lipid: a robust tool for the prediction of multiple lipid modification sites](https://scholariq.org/papers/gps-lipid-a-robust-tool-for-the-prediction-of-multiple-lipid-modification-sites/)
- [New techniques for extracting features from protein sequences](https://scholariq.org/papers/new-techniques-for-extracting-features-from-protein-sequences/)
- [Binary classification of protein molecules into intrinsically disordered and ordered segments](https://scholariq.org/papers/binary-classification-of-protein-molecules-into-intrinsically-disordered-and/)
- [Improving taxonomy‐based protein fold recognition by using global and local features](https://scholariq.org/papers/improving-taxonomy-based-protein-fold-recognition-by-using-global-and-local/)
- [RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition](https://scholariq.org/papers/rnam5cpred-prediction-of-rna-5-methylcytosine-sites-based-on-three-different/)
- [NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model](https://scholariq.org/papers/neuropred-plm-an-interpretable-and-robust-model-for-neuropeptide-prediction-by/)
- [Deep-ProBind: binding protein prediction with transformer-based deep learning model](https://scholariq.org/papers/deep-probind-binding-protein-prediction-with-transformer-based-deep-learning/)
- [Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features](https://scholariq.org/papers/enhancing-sumoylation-site-prediction-a-deep-neural-network-with-discriminative/)
- [Neural networks for prediction of nucleotide sequences by using genomic signals](https://scholariq.org/papers/neural-networks-for-prediction-of-nucleotide-sequences-by-using-genomic-signals/)
- [MVPHI: a multi-view learning framework for predicting complex microbial interactions](https://scholariq.org/papers/mvphi-a-multi-view-learning-framework-for-predicting-complex-microbial/)
- [Epilysin (MMP-28) functions in promoting epithelial cell survival](https://scholariq.org/papers/epilysin-mmp-28-functions-in-promoting-epithelial-cell-survival/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.