Cell Image Analysis Techniques

Fiji: an open-source platform for biological-image analysis

From molecular to modular cell biology

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Department of Computer Science

Cancer Biology and Therapeutics: Navigating Recent Advances and Charting Future Directions

Cancer, a multifaceted and heterogeneous ailment, persists as a substantial global public health concern. Recent strides in cancer biology have profoundly augmented our comprehension of the intricate genetic and molecular mechanisms underlying the inception and advancement of cancer. This headway has propelled the formulation of innovative therapeutic strategies, embracing targeted interventions and immunotherapies, which have showcased promising outcomes in clinical assessments. However, a multitude of obstacles continue to challenge the creation of efficacious and individualized cancer treatments. This systematic review encapsulates an extensive vista of contemporary advancements in cancer biology and therapeutics, accentuating pivotal breakthroughs in our grasp of cancer's intricacies, while illuminating the latest therapeutic avenues for cancer management. Moreover, the paper elaborates on the quest's upcoming trajectories in cancer research and treatment, encompassing burgeoning technologies and uncharted domains of inquiry that might pave the way for more efficacious and personalized interventions for individuals afflicted by cancer.