Machine learning in biology - Quiz
A) Microbial communities from environmental DNA samples B) Human genetic disorders and hereditary diseases C) The evolution of complex multicellular organisms D) Chemical synthesis of pharmaceutical compounds
A) High dimensionality of datasets B) Inability to collect environmental samples C) Lack of available supercomputers D) Lack of interest from the scientific community
A) Diabetes B) Colorectal cancer C) Alzheimer's disease D) Inflammatory bowel disease
A) To sequence the entire human genome B) To identify soil bacteria biodiversity C) To classify healthy patients and patients with IBD symptoms D) To predict the climate change impact on ecosystems
A) Gradient boosted tree model B) MegaR C) Ph-CNN D) RF-FVS
A) The number of researchers involved B) The geographic location of the samples C) The speed of the internet connection D) Performance and diversity of decision trees in the ensemble
A) Recurrent forest with functional variable sequencing B) Random forest coupled with forwarding variable selection C) Random functional variable selection D) Robust feature with forward validation system
A) Plant-pathogen interaction data B) CDI and CRC datasets C) IBD and Diabetes datasets D) Soil and water microbiome data
A) A new sequencing technology B) A supercomputer specifically for metagenomics C) A neural network architecture D) An R package for taxonomic profiles and classification models
A) There are no microbial communities in the environment B) Lack of available software C) Data complexity D) Environmental samples are not valuable
A) Quantum neural networks B) Hopfield neural networks C) Linear neural networks D) Gaussian neural networks
A) Treatment efficacy for colorectal cancer B) The speed of DNA sequencing in labs C) Relationship among soil, microbiome biodiversity, and ecosystem stability D) Genetic mutations in human gut bacteria
A) It decreases linearly B) It disappears completely C) It increases exponentially D) It remains constant
A) 99.01% B) 90.17% C) 75.14% D) 81%
A) Carbohydrates B) Lipids C) Amino acids D) Nucleotides
A) Protein filaments B) Beta sheets C) Primary chains D) Quaternary bonds
A) 20-30% B) 82-84% C) 50-60% D) 95-99%
A) Primary, secondary, or tertiary B) Folded, unfolded, or neutral C) Alpha, beta, or gamma D) Helix, sheet, or coil
A) 88–90% B) 70–75% C) 95–100% D) 60–65%
A) DeepMind B) Pauling Institute C) CASP D) Corey Laboratories
A) 2022 B) 2020 C) 1951 D) 2018
A) Lipid membrane synthesis B) RNA translation errors C) Protein loop modeling D) DNA sequencing optimization
A) Cell division modeling B) Protein contact map prediction C) Genome assembly D) Metabolic pathway simulation
A) Tertiary structure B) Pentagonal structure C) Atomic structure D) Hexagonal structure
A) Hierarchical Markov model B) Hidden Machine model C) Heuristic Markov method D) Hidden Markov model
A) Independent random variables B) The user input C) External noise D) The underlying state process
A) Only for non-biological data B) No C) Only for discrete states D) Yes
A) Visual cortex modeling B) Image classification C) Metagenomic filtering D) Profiling multiple sequence alignments
A) Convolution zone B) Receptive field C) Translation window D) Feature map
A) Pre-processing B) Computational power C) Training data D) Validation
A) Pre-determined by the analyst B) Hand-engineered by humans C) Via manual feature extraction D) Through automated learning
A) Patristic distance B) Random selection C) GC content D) Sequence identity
A) Calculated by physical modeling B) Generated by artificial intelligence C) Derived from hardware limitations D) Programmed by hand
A) A categorical class B) A raw string of data C) A numerical value D) An analog signal
A) A numerical valued feature B) A discrete class label C) A symbolic proof D) A hardware interrupt
A) Binary serialization B) Principal component analysis C) Data encryption D) Linear regression
A) 4 billion B) 16 million C) 12 thousand D) 16 thousand
A) Quantum computing B) Text mining C) Proteomics D) Genomics
A) By combining low-level features into abstract features B) By following pre-defined manual rules C) By simulating the organism's physical structure D) By ignoring complex dataset patterns
A) Scaling B) To compress the memory C) To initialize the weights D) To increase the gradient
A) Four B) Two C) One D) Three
A) X B) Y C) b2 D) W1
A) Support vector machines B) Neural networking C) Principal component analysis D) Bootstrap aggregating
A) Increasing the number of decision trees B) Calculating gradient descent C) Removing outliers from the original dataset D) Imputing missing values
A) Divisive B) Affinity propagation C) Partitional D) Agglomerative
A) They start with one large cluster B) They are always hierarchical C) They determine all clusters at once D) They cannot be used on large datasets
A) K-medoids B) Affinity propagation C) K-means D) BIRCH
A) MAMMAL B) ESMFold2 C) GeneFormer D) BIRCH
A) Each element as a separate cluster B) A random subset C) The whole set D) An empty dataset
A) HyenaDNA B) Evo2 C) GeneFormer D) ESMFold2
A) Standardized databases B) Erroneous interpretation C) Algorithm efficiency D) High-speed processing
A) Optical microscopy B) X-ray diffraction C) Manual observation D) Paper-based recording
A) Cancer genomic studies B) Plant hybridization C) Stock market analysis D) Meteorological prediction
A) Acoustic engineering B) Soil chemistry C) Urban planning D) Cellular imaging
A) Hardware manufacturing B) Energy grid management C) Variant calling D) Transportation logistics
A) Manual database entry B) Natural language processing C) Random guess algorithms D) Physical paper files
A) Individual genomic variability B) Hospital budget limits C) Geographical location D) Administrative paperwork
A) Every 6 months B) Every 10 years C) Every 18 months D) Every 5 years
A) Atmospheric pressure B) Genome rearrangements C) Global population trends D) Mechanical vibration
A) Genetic sequencers B) Cellular microscopes C) Lab-on-a-chip D) Protein synthesizers
A) Genetic network induction B) DNA chemical synthesis C) Biological tissue harvesting D) Microchip physical manufacturing
A) Protein folding rates B) Cell division frequency C) Viral replication cycles D) Gene expression
A) Microchip heat reduction B) Expression pattern identification C) Data storage compression D) Hardware circuit design |
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