ReviewCognitive computation2021
Deep Learning in Mining Biological Data.
Review in Cognitive computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
75 citing papers in PubMed.
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Beech leaf disease symptom detection using deep learning and computer vision tools.Journal of nematology · 2026Article
- Distinguishing early from late mild cognitive impairment: a multi-level analysis of regional morphometry and KLS-derived network topology.Frontiers in aging neuroscience · 2026Article
- Neuroprotective effects of traditional Chinese medicine using zebrafish models - a review.Frontiers in pharmacology · 2026Review
- Pattern and structural detection in grayscale images through the application of quantile graphs in higher-dimensional spaces.Scientific reports · 2025Article
- Data Augmentation and Synthetic Data Generation in Rare Disease Research: A Scoping Review.Medical sciences (Basel, Switzerland) · 2025Article
- From Fundamentals of Laser-Induced Breakdown Spectroscopy to Recent Advancements in Cancer Detection and Calcified Tissues Analysis: An Overview (2015-2025).Molecules (Basel, Switzerland) · 2025Review
- Droplet-based single-cell RNA sequencing: decoding cellular heterogeneity for breakthroughs in cancer, reproduction, and beyond.Journal of translational medicine · 2025Review
- Review
- From deep learning discovery to clinical validation: a new composite marker predicts mortality in type 2 diabetes.Cardiovascular diabetology. Endocrinology reports · 2025Article
- drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.ArXiv · 2025Article
- Prediction of pathogenic mutations in human transmembrane proteins and their associated diseases via utilizing pre-trained Bio-LLMs.Communications biology · 2025Article
- Characterization of Muscle Fatigue Degree in Cyclical Movements Based on the High-Frequency Components of sEMG.Biomimetics (Basel, Switzerland) · 2025Article
- Focal adhesion in the tumour metastasis: from molecular mechanisms to therapeutic targets.Biomarker research · 2025Review
- Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy.Diagnostics (Basel, Switzerland) · 2025Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures-known as deep learning (DL)-have been successfully applied to solve many complex pattern recognition problems. To investigate how DL-especially its different architectures-has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.