Computational Biology & Medicine
With the increasing availability of data and the improved understanding of system-level interactions in biological systems, computational methods have found many applications in biology and medicine. Research in CISE develops algorithms for long-standing problems in computational biology, such as predicting and characterizing protein interactions, optimizing metabolic networks, and developing computational neuroscience models. Another line of research develops computational methods to advance experimental observation techniques used in biology, from imaging methods to atomic force microscopy. A burgeoning area of research applies Artificial Intelligence (AI) methods to medical data, leading to disease/outcome prediction models and medical decision-making tools.
CISE Faculty Recognized for Groundbreaking AI Research with AIRR Awards
Recognized for their cutting-edge research in artificial intelligence, 11 CISE faculty affiliates have been honored with AI Research Resource (AIRR) awards. This Hariri Institute program supports AI research at Boston University by giving researchers access to the New England Research Cloud (NERC), a regional computing infrastructure that provides cloud-based resources tailored to academic research. A […]
High-speed, real-time feedback-driven single particle tracking with concurrent smFRET
The goal of this project is to establish proof-of-concept for a new high speed, Real-Time Feedback-Driven Single Particle Tracking (RT-FD-SPT) fluorescence microscope with concurrent spectroscopic readout. The project is motivated in large part by open questions in the spatiotemporal dynamics of activated growth factors. The ability to follow individual growth factors moving in their natural […]
EAGER: The Biothreats Emergence, Analysis, and Communications Network (BEACON)
This project will develop an open-source Large Language Model that will be able to identify, verify, prioritize, summarize and predict outcomes of disease emergence events in humans, other animals, and plants. That model will link detected signals and model outputs to human verification and public health context with the goal of sharing event reports on […]
DMS/NIGMS 1: Multilevel stochastic orthogonal subspace transformations for robust machine learning with applications to biomedical data and Alzheimer’s disease subtyping
Late-onset Alzheimer’s Disease (AD) is the most common form of dementia, with an estimated 6.5 million Americans aged 65 and older living with AD today – this number will double by 2050. AD occurs in more than 35% of individuals over the age of 85 and is the fifth leading cause of death among Americans […]
DMS/NIGMS 1: Multilevel stochastic orthogonal subspace transformations for robust machine learning with applications to biomedical data and Alzheimer’s disease subtyping
Late-onset Alzheimer’s Disease (AD) is the most common form of dementia, with an estimated 6.5 million Americans aged 65 and older living with AD today – this number will double by 2050. AD occurs in more than 35% of individuals over the age of 85 and is the fifth leading cause of death among Americans […]
Cyber-Secure Bio-Electronic Capsules: Transforming Healthcare and Environmental Monitoring
Pioneering an unprecedented method for tracking gastrointestinal (GI) health and environmental conditions, Rabia Yazicigil, a CISE Faculty Affiliate and Assistant Professor at Boston University (ECE, BME), has been awarded the prestigious 2024 National Science Foundation (NSF) CAREER award. Yazicigil’s research on cyber-secure biological systems (CSBS), particularly the miniaturized ingestible bio-electronic capsule, is the first of […]
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting […]
Cheng & Tian’s Newest Microscopy Advance Published by Nature Communications
Professor Ji-Xin Cheng’s research group has made notable strides in improved chemical imaging technologies, especially for medical purposes, over the last few years. Their latest, the development of a new type of mid-infrared photothermal (MIP) microscope, was published by Nature Communications in December. The paper, co-authored by collaborator and CISE affiliate Professor Lei Tian, Post-Doctoral Associate […]
Two BU Researchers Receive over $1 Million Each in Funding from the Chan Zuckerberg Initiative
Imagine being able to watch the smallest units of life—like cells and molecules—working together in real time. Seeing and measuring biological processes, a field called dynamic imaging, can help scientists unlock tremendous knowledge for treating diseases, from cancer to Alzheimer’s. In an effort to take biological imaging to the next level, two Boston University College […]
Bond-selective intensity diffraction tomography
Recovering molecular information remains a grand challenge in the widely used holographic and computational imaging technologies. To address this challenge, we developed a computational mid-infrared photothermal microscope, termed Bond-selective Intensity Diffraction Tomography (BS-IDT). Based on a low-cost brightfield microscope with an add-on pulsed light source, BS-IDT recovers both infrared spectra and bond-selective 3D refractive index […]