Open research questions in Genetics, Bioinformatics, and Biomedical Research
199 unresolved questions extracted from the limitations and future-work sections of 1,102 Genetics, Bioinformatics, and Biomedical Research papers in our library. Each links back to the study that raised it.
What the literature leaves open
Further analysis of the role of genetic information in maintaining non-equilibrium conditions. The study of the application of enzyme-accelerated reactions in biotechnology.
The lack of understanding of the general principles and specific mechanisms governing the dynamics of genetic information in living systems. The need to establish the link between genetic information and thermodynamic equilibrium.
Bull Math Biol 86(11):135 Pastva S, Park KH, Huvar O, Rozum JC, Albert R (2025) An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean networks? J Math Biol 91(1):11 Spector R, Harrington HA, Gaffney EA (2026) Persistent homology classifies parameter dependence of patterns in Turing systems.
Traditional classification schemes overlook complex structure–function relationships. Limited understanding of biologically significant associations among amino acids.
Unraveling protein secrets: machine learning unveils novel biologically significant associations among amino acids · 2026 · DOIThe paper demonstrates that Cysteine clusters with polar amino acids rather than Methionine despite both containing sulfur atoms, suggesting overall physicochemical properties dominate specific chemical features, but does not systematically test this principle across amino acid pairs with shared chemical elements to establish the relative weighting of feature classes.
Unraveling protein secrets: machine learning unveils novel biologically significant associations among amino acids · 2026 · DOINew mathematical tools are needed to address the challenges of big data in bioinformatics. There is a growing interest in scalable optimization methods that can manage petabyte-sized genomic arrays. The development of mathematical tools that can integrate highly heterogeneous data records is needed.
The amount of biological data being produced has now started to exceed the computational resources available. There is a need for new mathematical tools to obtain more efficient dimensionality reduction while retaining the biological signals and removing the noise.
Further studies are needed to assess the effectiveness of the CURE model in improving student engagement, retention, and scientific identity. Further studies are needed to explore the use of other wild yeast strains and fermentation products. Further studies are needed to develop and refine the CURE model for use in other institutions and contexts.
Declaration of Fermentation: Community-Embedded Wild Yeast Bioprospecting as a Model for Place-Based CURE Design · 2026 · DOIMost existing CURE frameworks treat the research organism as an interchangeable teaching prop rather than a genuine scientific contribution. There is a need for novel CURE models that engage students in genuine inquiry and promote community partnerships. There is a need for proof of concept studies demonstrating the feasibility and logistical achievability of these models.
Declaration of Fermentation: Community-Embedded Wild Yeast Bioprospecting as a Model for Place-Based CURE Design · 2026 · DOIThe need for scalable, reproducible, and adaptive microorganism recognition. The need for robust knowledge retrieval for specialized biomedical and scientific contexts.
The complexity of DNAzyme-substrate interactions. The limited size of the sustained-activity dataset. The need for a data-driven approach to select high-potency DNAzymes.
CleaveSmart: deciphering the puzzle of rational 10–23 DNAzyme selection through interpretable AI insights · 2026 · DOIThe predictive performance of all models is lower for rapid-activity datasets. The study uses a limited size of the sustained-activity dataset (n = 38). The pipeline is designed for sustained catalytic activity and may not be applicable to rapid-activity datasets.
CleaveSmart: deciphering the puzzle of rational 10–23 DNAzyme selection through interpretable AI insights · 2026 · DOIInconsistent field results are a challenge in biopesticide research. Limited knowledge on microbiome interactions is a limitation. Regulatory hurdles and cost are also challenges. Data silos and farmer acceptance are additional limitations.
The Biopesticide Revolution: AI, Genomics, and Microbiome Engineering on a Global Scale · 2026 · DOIFuture research should focus on the intersection of CRISPR-based microbial engineering and multi-omics integration. Future research should explore the use of autonomous AI systems and digital agriculture platforms for decision support systems. Future research should investigate the potential of personalized microbiome solutions for biological crop protection.
The Biopesticide Revolution: AI, Genomics, and Microbiome Engineering on a Global Scale · 2026 · DOIAI applications in life sciences are reviewed across drug discovery, genomics, and marine biology, but none of these studies empirically compares AI model performance, interpretability, or real-world implementation outcomes across these distinct domains to identify domain-specific failure modes or transferability barriers.
The complexity of the cell - The need for students to develop collaboration skills and improve their understanding of complex cellular interactions - The potential for students to feel overwhelmed
The complexity of the cell often results in having to approach content piece by piece - Students struggle to find common themes or link together topics
In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.
Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation · 2024 · DOITraditional approaches are insufficient for understanding complex biological systems. There is a need for cross-disciplinary techniques to analyze complex biological data.
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology · 2023 · DOIUnderstanding the complex interactions within the network. Identifying the core of the network. Exploring potential applications for future drug discovery.
The need to rethink cancer biology in the context of network biology. The importance of understanding the network biology of living cells to better comprehend cancer.
The ongoing fight for recognition and equality faced by LGBTQIA+ researchers. The need for continued support and amplification of LGBTQIA+ visibility in the sciences. The challenge of providing a safe platform for queer researchers to share their stories and experiences.
The lack of recognition and support for LGBTQIA+ researchers beyond Pride Month. The need for continued efforts to promote LGBTQIA+ visibility in the sciences.
There is a lack of educational programs that integrate systems biology into other disciplines. The public is not well-informed about the progress, potential, challenges, and future of systems biology.
The paper does not explicitly state the limitations of the study. However, it mentions that computational results need to be validated even when produced by experts.
Most-cited papers in Genetics, Bioinformatics, and Biomedical Research
- From molecular to modular cell biology · Nature · 1999 · 2,902 citations
- Intracellular Aspects of the Process of Protein Synthesis · Science · 1975 · 2,584 citations
- Central Dogma of Molecular Biology · Nature · 1970 · 2,366 citations
- Database resources of the National Center for Biotechnology Information in 2025 · Nucleic Acids Research · 2024 · 472 citations
- Biosemantics · The Journal of Philosophy · 1989 · 421 citations
- Using the PyMOL application to reinforce visual understanding of protein structure · Biochemistry and Molecular Biology Education · 2016 · 413 citations
- Biological Corridors: Form, Function, and Efficacy · BioScience · 1997 · 337 citations
- Empowering biomedical discovery with AI agents · Cell · 2024 · 312 citations
- Is there a cell-biological alphabet for simple forms of learning? · Psychological Review · 1984 · 298 citations
- Biomolecules in the computer: Jmol to the rescue · Biochemistry and Molecular Biology Education · 2006 · 292 citations
Most recent work
- Resisting AI slop · Science · 2026
- Unraveling protein secrets: machine learning unveils novel biologically significant associations among amino acids · Network Modeling Analysis in Health Informatics and Bioinformatics · 2026
- PLANeT: Understanding and leveraging the genome of land plants for a sustainable future · Cell · 2026
- Rethinking bioinformatics expertise in the era of artificial intelligence · npj Digital Medicine · 2026
- Declaration of Fermentation: Community-Embedded Wild Yeast Bioprospecting as a Model for Place-Based CURE Design · bioRxiv · 2026
- Advancing molecular and cellular neuroscience: Vision and priorities from the new editor-in-chief · Molecular and Cellular Neuroscience · 2026
- Microbiome education at under-resourced institutions: current status, barriers, and approaches to successful implementation · Journal of Microbiology & Biology Education · 2026
- A zebrafish module for genetic problem-solving · Journal of Microbiology & Biology Education · 2026
- Bad AI-generated images can be good for teaching visual literacy in biology · Journal of Microbiology & Biology Education · 2026
- Fostering open science literacy through an asynchronous CURE: challenges and strategies of a fully online student research experience · Frontiers in Education · 2026
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