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Advancing Healthcare in Low-Resource Environments Through an Optimization and Deployment Framework for Medical Multimodal Large Language Models

Advancing Healthcare in Low-Resource Environments Through an...

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ieee EMBS International conference on Information Technology Applications in Biomedicine (ITAB)

作者： Aya El Mir Lukelo Thadei Luoga Boyuan Chen Muhammad Abdullah Hanif Muhammad Shafique eBRAIN Lab Division of Engineering New York University Abu Dhabi UAE

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

The critical shortage of medical professionals in low-resource countries, notably in Africa, hinders adequate health-care delivery. AI, particularly Multimodal Large Language models (MLLMs), can enhance the efficiency of healthcare systems by assisting in medical image analysis and diagnosis. However, the deployment of state-of-the-art MLLMs is limited in these regions due to the high computational demands that exceed the capabilities of consumer-grade GPUs. This paper presents a framework for optimizing MLLMs for resource-constrained environments. We introduce optimized medical MLLMs including TinyLLaVA-Med-F, a medical fine-tuned MLLM, and quantized variants (TinyLLa VA - Med- FQ4, Tiny LLa VA - Med- FQ8, LLa VA - Med-Q4, and LLaVA-Med-Q8) that demonstrate substantial reductions in memory usage without significant loss in accuracy. Specifically, TinyLLaVA-Med-FQ4 achieves the greatest reductions, lowering dynamic memory by approximately 89% and static memory by 90% compared to LLaVA-Med. Similarly, LLaVA-Med-Q4 reduces dynamic memory by 65% and static memory by 67% compared to state-of-the-art LLaVA-Med. These memory reductions make these models feasible for deployment on consumer-grade GPUs such as RTX 3050. This research underscores the potential for deploying optimized MLLMs in low-resource settings, providing a foundation for future developments in accessible AI-driven healthcare solutions.

关键词： Accuracy Quantization (signal) Image analysis Large language models Biological system modeling Africa Medical diagnosis bioinformatics Medical diagnostic imaging Optimization

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Predicting non-classical secretory proteins by using Gene Ontology terms and physicochemical properties

Predicting non-classical secretory proteins by using Gene On...

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2011 ieee International conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Chyn Liaw Shinn-Ying Ho Institute of Bioinformatics and Systems Biology National Chiao Tung University

Eukaryotic secretory proteins that traverse classical ER-Golgi pathway are usually characterized by short Nterminal signal ***,several secretory proteins lacking the signal peptides are found to be exported by a nonclassical secretion ***,predicting nonclassical secretory proteins regardless of the N-terminal signal peptides is necessary for developing a critical computational *** prediction methods have been proposed by using various types of features to predict secretory ***,prediction performance seems not *** study proposes an SVM-based prediction method,namely ProSec-iGOX,which uses a major set of informative Gene Ontology(GO) terms and a minor set of assistance *** properties as the assistance features are useful when a query protein sequence without homologous protein with annotated GO *** data sets,S25and S40,having the identity 25% and 40%,respectively,are adopted for performance *** ProSec-iGOX yields test accuracies of 95.1% and 96.8% when adopting on the data sets S25 and S40 *** latter accuracy(96.8%) is significantly higher than that of SPRED(82.2%),which uses frequency of tri-peptides and short peptides,secondary structure,physicochemical properties as input features to a random forest *** experimental results show that GO terms are effective features for predicting non-classical secretory proteins.

关键词： Gene Ontology secretory physicochemical properties non-classical secretion signal peptides

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STATISTICAL LEARNING FOR DEPENDENCE MINING AND PROBLEM SOLVING:FUNDAMENTALS,CHALLENGES,AND A UNIFIED THEORY

STATISTICAL LEARNING FOR DEPENDENCE MINING AND PROBLEM SOLVI...

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2005 International conference on Neural Networks and Brain(ICNN&B’05)

作者： Lei Xu Chinese University of Hong Kong

An intelligent system is featured by both its abilities of interpreting what are observed via discovering knowledge about the world it survives,and its problem solving skills of handling each issue encountered in the ***,the abilities and skills are obtained by two types of learning via evidences or data from the *** to noises in observation and a finite size of samples,learning is statistical in nature,which faces two key *** is finding appropriate mathematical representations to suit various dependence structures underlying world. The other is getting a good theory to guide learning such that dependence structures are not only learned into mathematical representations but also with an appropriate complexity that matches the size of samples(i.e.,learning reliable structures of underlying world).This paper consists of part *** first two parts summarize typical dependence structures for tackling the challenge one and typical learning theories for tackling for tackling the challenge *** third part introduces Bayesian Ying Yang(BYY) system as a general framework that unifies typical dependence structures and BYY harmony learning for the challenge two,with several favorable *** illustrate this BYY learning,in the fourth part we further introduce fundamentals of independence subspaces and advances obtained from BYY harmony learning on typical independence subspaces,including PCA,MCA,DCA,ICA,FA,TFA,NFA,BFA,LMSER,as well as their temporal ***,a concluding remark is made and new results of BYY learning in other learning areas are also briefly listed.

关键词： Statistical learning Problem-solving Bayesian methods Humans bioinformatics Biology Decision making Neurons Pattern recognition Intelligent systems

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Application of Artificial Intelligence in Malignant Tumors: A Science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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ieee Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A Science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural Science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence bioinformatics Cancer Genetic algorithms

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Insect navigation and communication in flight and migration: A potential model for joining and collision avoidance in MAVs and mobile robots fleet control

Insect navigation and communication in flight and migration:...

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ieee conference on Aerospace

作者： Zhanshan Ma Axel W. Krings Richard Millar Feng Wang Jun Chao Computational Biology & Bioinformatics Lab KIZ Chinese Academy of Sciences PRC & Computer Science Department & iBE University of Idaho USA Computer Science Department University of Idaho Moscow ID USA Department of Systems Engineering Naval Postgraduate School MD USA Computer Science Department Kunming University of Science and Technology Kunming China Kunming Institute of Zoology KIZ Chinese Academy and Sciences Kunming China

The human being should be awarded the championship of navigation on the planet, on the virtues that they not only have invented and manufactured the compass, GPS, aircraft, vessel, spacecraft, but also have demonstrated exceptional non-instrumental navigation skills. The Austronesian expansion from the Asian mainland into the Pacific performed by the Pacific navigators, who eventually populated the most remote islands of the Pacific about 4000 years ago, is a vivid example (Wehner 1998). However, animals, especially flying birds and insects are strong contenders. Monarch butterflies can migrate up to 2000 miles from their reproductive sites in the eastern US and Canada to their over-wintering sites in the forests of Mexico, and it is postulated that they may possess a biological equivalent of a low-resolution GPS system that is based on the magnetic field of the earth. In fact, even the long-legged ants (Cataglyphis fortis) in the Saharan desert use the dead-reckoning navigation strategy, which is attributed to the Polynesians, but the ants apparently have acquired the capability much earlier, given the relative short evolutionary history of humans. In this article, we briefly review the state-of-the-art research on insect navigation and communication used in flight and communication, with the objective to inspire cross-disciplinary studies in aerospace engineering, biology and computer science. After a brief review, we overview and identify seven cross-disciplinary research topics that may draw on inspirations from insect navigation and communication in flight and migration. These topics include: ants colony inspired swarm intelligence, honeybee inspired group decision-making, insect sociobiology, MAV/mobile robot flight control and remote control of insect flights, optimal migration strategy, Quorum sensing, and joining and collision avoidance for MAV fleet control. An interesting question one may pose is: given the rich and advanced navigation and communication t

关键词： Insects Navigation Visualization Birds Sun

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MQML: Multi-Omic Quantum Machine Learning Based Cancer Classification, Biomarker Identification in Human Lung Adenocarcinoma

MQML: Multi-Omic Quantum Machine Learning Based Cancer Class...

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Quantum Computing and Engineering (QCE), ieee International conference on

作者： Mandeep Kaur Saggi Sabre Kais Department of Chemistry Purdue University West Lafayette IN USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning (QML) presents novel opportunities for resolving, accelerating, and refining computational biology challenges. In biomedical research and personalized medicine, multi-omics integration offers a comprehensive understanding of complex biological systems, bridging the gap between basic research and clinical applications. Recent advancements in quantum computing could enhance multi-omics integration by leveraging unique quantum principles, potentially revealing intricate patterns in multi-omics datasets and offering new insights into the molecular landscape of lung cancer. This study focuses on the integration, feature selection, diagnostic classification, and biomarker identification of lung cancer types-LUAD primary tumor and solid tissue normal-using QML. Given the heterogeneity and high dimensionality of the data, we aim to assess the performance of QML in feature selection, reduction, encoding, and diagnostic classification in integrated multi-omics cancer datasets. We demonstrate the efficacy of Quantum Neural Networks (QNNs) for classifying multi-omics lung cancer data from The Cancer Genome Atlas, comparing our framework with 16 and 64 dimensions of feature using QML. Our results show that the Multi-omic Quantum Machine Learning (MQML) framework achieves superior classification performance with smaller training datasets, as determined by significant and non-significant p-values. This study provides empirical evidence for the potential application of unconventional computing approaches in biomed-ical sciences, particularly in oncology research, including cancer subtype diagnostic, survival, and stage classification. Key results include the identification of sixteen genes and thirty-two genes with the hybrid quantum-classical neural net-work model, with notable genes such as MIR632, CYP19A1, RP11-108K3.1, C14orf39, THUMPD1, ACSM3, GSC, MIR92a-2, DYRK1B, DNAJA1, IRX1, FMR1, POU2F2, MIR9-2, ZNF274, MIR196a-1, MIR3653, MIR33b, MIR301a, MIR182

关键词： Training Quantum computing Lungs Transcriptomics Lung cancer Machine learning Feature extraction Encoding bioinformatics Tumors

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Enabling Large-Scale Biomolecular Conformation Search with Replica Exchange Statistical Temperature Molecular Dynamics (RESTMD) over HPC and Cloud Computing Resources

Enabling Large-Scale Biomolecular Conformation Search with R...

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International conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Nayong Kim Richard Platania Wei Huang Chris Knight Tom Keyes Seung-Jong Park Joohyun Kim Center for Computation & Technology Louisiana State University Baton Rouge LA USA Department of Computer Science Louisiana State University Baton Rouge LA USA Center for Proteomics and Bioinformatics School of Medicine Case Western Reserve University Cleveland Ohio USA Leadership Computing Facility Argonne National Laboratory Argonne Illinois USA Chemistry Department Boston University Boston MA USA

We present the latest development and experimental simulation studies of Statistical Temperature Molecular Dynamics (STMD) and its parallel tempering version, Replica Exchange Statistical Temperature Molecular Dynamics (RESTMD). Our main contributions are i) introduction of newly implemented STMD in LAMMPS, ii) use of large scale distributed cyber infrastructure including Amazon EC2 and the nationwide distributed computing infrastructure GENI, in addition to High-performance Computing (HPC) cluster systems, and iii) benchmark and simulation results highlighting advantages and potentials of STMD and RESTMD for challenging large-scale bio molecular conformational search. In this work, we attempt to provide convincing evidence that RESTMD, combining two advanced sampling protocols, STMD and the replica exchange algorithm, offers various advantages over not only conventional ineffective approaches but also other enhanced sampling methods. Interestingly, RESTMD has benefits over the most popular Replica Exchange Molecular Dynamics (REMD) as an application maximizing its capacity in HPC environments. For example, RESTMD alleviates the need of a large number of replicas which is unavoidable in REMD and is flexible in order to exploit the maximum amount of available computing power of a cluster system. Continuing our recent effort in which RESTMD was implemented with a community molecular dynamics package, CHARMM, and the Hadoop MapReduce, in this work, we report latest development outcomes. First of all, we plugged the implementation of STMD into LAMMPS, one of the most popular public molecular dynamics packages. This is expected to position STMD and RESTMD appealing to investigators from a broad range of life science fields. Secondly, Hadoop MapReduce-based RESTMD is now able to run on Amazon EC2 and the nationwide network virtual organization, the GENI distributed computing environment. Thirdly, in order to find optimized parameters for RESTMD simulations, simulation res

关键词： Temperature distribution Heuristic algorithms computational modeling Scalability Histograms Benchmark testing

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A real-time HFO detection framework for localization of SOZ in iEEG recording of patients with epilepsy

A real-time HFO detection framework for localization of SOZ ...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Behrang Fazli Besheli Chandra Prakash Swamy Amir Hossein Ayyoubi Luciano R. F. Branco Gregory A. Worrell Zhiyi Sha Jay R. Gavvala Kai J. Miller Nuri F. Ince Department of Neurologic Surgery Mayo Clinic Rochester MN USA Bioinformatics and Computational Biology Department University of Minnesota Minneapolis MN USA Department of Biomedical Engineering University of Houston Houston TX USA Department of Neurology Mayo Clinic Rochester MN USA Department of Neurology University of Minnesota Minneapolis MN USA Department of Neurology UT Health Houston TX USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Interictal High-frequency Oscillation (HFO) between 80-600 Hz in intracranial EEG (iEEG) is a promising biomarker of the epileptogenic zone in individuals with epilepsy. Numerous studies revealed that the resection of channels with a high rate of HFOs correlates with favorable surgical outcomes. Early feedback to clinicians regarding the distribution of HFOs during the iEEG recording, especially after the implantation of electrodes, would be helpful for clinical decisions. However, iEEG recording can easily get corrupted by various factors mimicking real HFOs. This study presents a real-time HFO detection framework within MATLAB/Simulink that exhibits robustness against such pseudo-HFOs. This detector is responsible for identifying the initial pool of candidate HFOs and transmitting them via user datagram protocol (UDP) to an external application. The external application contains a machine learning tool that is utilized for post-processing and isolating the real-HFOs. It is implemented in a graphical user interface (GUI) that provides visual feedback regarding the distribution and waveforms of HFOs. The entire processing pipeline was validated by randomly selecting 10-minute segments of interictal iEEG recordings from 10 subjects. It successfully identifies the seizure onset zone (SOZ) in these subjects, achieving an average accuracy of 65% using the detected Ripples and 74% using the detected events with both Ripples and Fast Ripples. Importantly, the spatio-temporal distribution of detected HFOs in real-time showed more than 98% spatio-temporal similarity index compared to offline analysis. Our framework proves to be an effective tool for the automatic identification of HFOs in real-time with the ability to promptly stream the HFO analysis results and provide early feedback regarding the probable SOZ regions to clinicians for surgical decision-making.

关键词： Visualization Protocols Pipelines Surgery Epilepsy Hafnium oxide Real-time systems Robustness Recording Graphical user interfaces

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A length bias corrected likelihood ratio test for the detection of differentially expressed pathways in RNA-Seq data

A length bias corrected likelihood ratio test for the detect...

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ieee Global conference on Signal and Information Processing

作者： Ariana Broumand Tao Hu Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Center for Bioinformatics and Genomic Systems Engineering Texas A&M University College Station TX 77843 USA

ISBN: (纸本)9781479975921

RNA-Seq has become an important alternative to microarrays in transcriptomic studies. The unique features of count-based RNA-Seq data pose new challenges for pathway analysis and call for new computational tools. In this study, we developed a likelihood ratio test to identify differentially expressed pathways in RNA-Seq data. The proposed method takes into account the coherent gene expression patterns by considering a common variance component shared by genes within a pathway. Additionally, we implemented a method to correct the length bias existing in the differential expression analysis using RNA-Seq data, where longer transcripts are more likely to be identified as differentially expressed. We demonstrated the ability of the proposed method using both synthetic and real data. We found that the top differentially expressed pathways between liver and kidney tissue samples identified using our method are associated with organ-specific functions.

关键词： coherent expression length bias likelihood-ratio test RNA-Seq differential expression analysis pathway

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